Q3 2024 Palantir Technologies Inc Earnings Call
and Newture III, Quarter 24 earnings call. We'll be discussing the results announced in our press release issued after the market closed and posted on our investor relations website.
During the call, we will make statements regarding our business that may be considered forward-looking within applicable security laws, including statements regarding our fourth quarter in fiscal 2020-4 results. The Analystance Expectations for our future financial and operational performance, and other statements regarding our plans, prospects, and expectations.
These statements are not promises or guarantees and are subject to risks and uncertainties, which would cause them to differ materially from actual results.
Information concerning those risks is available in our earnings, press release, distributed after the market close today in our SEC filings. We undertake no obligation to update forward-looking statements, except as required by law.
Further, during the course of today's call, we will refer to certain adjusted financial measures. These non-gap financial measures should be considered in addition to not as a substitute for or an isolation from Gap measures.
Additional information about these non-gat measures, including reconciliation of non-gap to comparable gap measures, is included in our press release and investor presentation provided today. Our press release, investor presentation, and other earnings materials are available on our investor relations website at investors.palonsure.com.
Over the course of the call, we will refer to various growth rates when discussing our business. These rates reflect your career comparisons unless otherwise stated.
Joining me on today's call are Alex Karp, Chief Executive Officer, Shyam Sankar, Chief Technology Officer, Dave Glazer, Chief Financial Officer, and Ryan Taylor, Chief Revenue Officer and Chief Legal Officer. I'll now turn over to Ryan to start the call.
Ryan Taylor: Our results are exceptionally strong. Revenue grew 30% year over year in Q3 driven by an intensifying AI revolution that the US is rapidly driving.
Ryan Taylor: Our US business achieved 44% year over year and 14% sequential revenue growth. We are focused on deploying AI models in production amidst the commoditization of cognition, caused by the rapid advancement in AI models.
Our US government business revenue growth accelerated to 40% year over year and 15% sequentially, while our US commercial business momentum continued with 54% year over year and 13% sequential revenue growth.
This AI revolution that is transforming industry as well as government is also transforming markets.
In September, Descent P500 added power into its index, a testament to our exceptional growth, profitability, and market leadership amidst the singular era of accelerating technological progress.
Ryan Taylor: We're witnessing the commoditization of cognition with the rapid advancement of AI models. Almost all investment in the AI space has been focused on supplying and improving these models.
Ryan Taylor: What will differentiate the AI have from the have-notch is the ability to maximally leverage these models in production by capitalizing upon the rich context within the enterprise.
Ryan Taylor: This is Palantir's Focus
We see this in the results we're delivering for our customers.
Ryan Taylor: Those who embrace quantified exceptionalism through AIP are able to take advantage of the commoditized cognition in a levered way to advance their differentiation. In this winter take all AI economy, the divide is widening between those who are leveraging AIP and those who are not.
At a leading global insurance organization, AIP has helped automate key underwriting workflows, reducing the typical underwriting response time from over two weeks to three hours.
We implemented over 10 business use cases in just nine months at associated materials, increasing its on time, in full delivery rates from 40% to 90%.
At Trinity Rail, it took just three months to get to a functional workflow with a 30 million dollar impact to its bottom line.
Ryan Taylor: Last quarter we closed 104 deals over $1 million. The evolving deal cycle as we take customers from prototype to production is having a particularly phenomenal effect on the growth of our U.S. commercial business, which continues to see AIP driven momentum both in expansions and new customer acquisitions.
Ryan Taylor: In US commercial, we closed nearly 300 million dollars of TCB and customer count grew 77% year over year, compared to 37% year over year in Q3 2023.
Ryan Taylor: To highlight a few notable deal cycles, a large American equipment rental company expanded its work with us less than eight months after converting to an enterprise agreement, increasing the account ARR 12 fold.
Ryan Taylor: A bottled water manufacturer, especially pharmaceutical company, and an agricultural software provider, all signed 7 figure ACV deals less than 2 months after their initial boot camps.
Ryan Taylor: In our U.S. government business, we are outfitting our warfighters with advantages over our adversaries.
Last quarter marked our U.S. government businesses continued strength through the end of the U.S. government fiscal year. It was our strongest sequential growth in 15 quarters, driven largely by our DOD businesses 21% quarter over quarter growth.
We remain proud of our progress delivering the next generation targeting No-Thrut Titan, with our efforts fully ramping throughout Q3.
Ryan Taylor: Talentiers also delivering AI through Maven Smart System, allowing customers like the 18th airborne to match the performance of what used to be a 2000 staff targeting cell during Operation Iraqi Freedom to a targeting cell of roughly only 20 people today.
Last quarter, founder signed a new five-year contract to expand these Maven Smart System AIML capabilities across the US military services, including the Army, Air Force, Space Force, Navy and US Marine Corps. As Vice Admiral Frank Whitworth recently said, quote,
Speaker Change: This partnership is Tantamount to ensuring that we keep America safe and ready.
The AI Revolution is underway now. The chasm between the AI have and have not is rapidly widening and the whole world is watching.
Speaker Change: I'll now turn it over to show.
Speaker Change: Thanks Ryan, to divide between AI halves and have not as rapidly accelerating in this winter take-all AI economy.
Speaker Change: What will differentiate the AI have from the have-nostas, the ability to maximally leverage these models in production by capitalizing upon the rich context within the enterprise.
Ryan Taylor: That's why our focus on delivering proof, not proof of concepts, continues to pay off. Years, the foundational investments in our infrastructure and in our topology have positioned us uniquely to harness and deliver on AI demand. This is Challengers Focus.
Ryan Taylor: The market has been focused on AI supply, the models. We see this clearly in the progress, but also in the capital sunk into these models.
Ryan Taylor: Indeed, the models continue to improve, but more importantly, the models across both open and closed source are becoming more similar. They are converging. All while pricing for inference is dropping like a rock. This only strengthens our conviction that the value is in the application and workflow layer, which is where we excel.
Ryan Taylor: Tapping into this rapidly expanding pool of leverage from AI labor means more than just saving money. It means a massive acceleration results for our customers.
Ryan Taylor: As Ryan mentioned, we have automated the insurance underwriting process for one of America's largest and most well-known insurers with 78 AI agents, taking a process that took two weeks to three hours.
Ryan Taylor: More than the labor savings, this presents the customer with an asymmetrical advantage in the marketplace, to buy and contract before the competition has even gotten through 15% of their process. In U.S. government, we automated the foreign disclosure process for sharing critical and timely intelligence with allies from three days to three hours.
Ryan Taylor: The Center for Security and Emerging Technologies at Georgetown published a study on Maven that showed how the entire targeting and virus process can be done in Maven with 20 people, it used to take 2000.
Ryan Taylor: There's a huge opportunity for our customers to automate the tale and liberate capital to reinvest in the truth across government and commercial. We see enterprise autonomy as a key theme in our proof.
Ryan Taylor: Our deep investments in C.J.C.2 combined joint all-domain command and control continue to meet their moments. First and foremost, Maven has powered responses to rear-world events across the globe.
Ryan Taylor: This past quarter, the Army, was the first military department or milled-up to adopt Maman. We're happy with the progress that we continue to make with Army Titan, an AIDP, and Palantra's role as the application integrator in the Joint Fires Network.
Ryan Taylor: Miven is our military's fight tonight's solution. At a time when North Korean troops are in Ukraine, Russia is providing satellite intelligence to the hoothies and Iran is launching ballistic missiles at allies.
Ryan Taylor: We are investing aggressively to expand the perimeter to give our warfighters the unfair advantage they deserve. Advanced multi-insensor fusion, integrated logistics, into fires, and large-scale command and control of forms of autonomous systems.
Ryan Taylor: We announced warp speed last quarter, our modern American manufacturing operating system.
Ryan Taylor: We as a nation must re-industrialize, prevent escalating conflict and regain deterrence.
Ryan Taylor: Before the fall, the Berlin Wall only 6% of major weapon systems spend went to defense specialists, the so-called primes.
Ryan Taylor: 94% went to dual-purpose companies who were invested in both freedom and prosperity.
Ryan Taylor: Chrysler built cars and missiles, Ford built satellites until 1990, and General Mills, the serial company made weapons.
Ryan Taylor: Today that 6% has become 86% when including firms whose only commercial exposure is an aerospace.
Ryan Taylor: We won World War II and the Cold War with an American industrial base, not a defense industrial base
Ryan Taylor: And we need to bring that back at warp speed. In addition to working with new champions like Andrew and Shield AI, we're also working with L3 Harris and two other of the big primes to help them bend Adam's better with bits.
Ryan Taylor: Lastly, we continue to invest in AIP as a developer platform.
Ryan Taylor: Green suitors at the 18th Airborne Corps built 15 applications in our developer environment for their August Warfighter exercise. Army software factory is cranking out software at units in Europe and even for the Vice Chief of Staff of the Army.
Ryan Taylor: The 100-in-first built their search and rescue common operating picture to power hurricane Haleen response, built entirely by uniform service members.
Ryan Taylor: We have released our JADC2 SDK, including examples and documentation for government and third-party developers to start building on Appalachry.com slash defense slash SDK.
Ryan Taylor: And we have DevCon this month, our first gatherings specifically for AIP platform developers across commercial and government.
Ryan Taylor: where we will be releasing a ton of new product investments and enhanced OSDK, more ergonomic compute modules, the multimodal data plane, and much more. I'll turn it over today to talk to the financials.
Speaker Change: Thanks, Shyam. Q3 was an exceptionally strong quarter as revenue growth accelerated to 30% year over year, exceeding the high end of our prior guidance by nearly 450 basis points.
Ryan Taylor: As America rapidly embraces the AI revolution, this increase in AI demand has driven the outperformance in our U.S. business, which grew 44% year over year.
Ryan Taylor: Our US commercial business grew 54% you over year and 13% sequentially. Our US government business grew 40% you over year and 15% sequentially, a 7-fold increase compared to the prior year period growth rate and the strongest growth we've seen in 15 cores.
Ryan Taylor: On the back of this strength, we are increasing our full-year revenue guidance midpoint to 2.807 billion, representing a 26% year-over-year growth rate.
Ryan Taylor: We delivered these outstanding top-line results while expanding adjusted operating margin to 38% how you've been strong-earned economics of our business. Our revenue and profitability drew a four-point sequential increase to a rule of 40 score from 64 in the second quarter to 68 in the third quarter.
Ryan Taylor: We had an exceptional cash flow quarter with cash from operations of 420 million and a just of recast flow of 435 million, representing margins of 58% and 60% respectively.
Ryan Taylor: On a Trailing 12 month basis, we generated over $1 billion in adjusted free cash flow for the first time in the company's history.
Ryan Taylor: We're also proud to have joined the S&P 500 Laws Quarter, understoring our sustained profitability and growth.
Ryan Taylor: Turning to our global top line results.
Ryan Taylor: Revenue continues to accelerate as we see continued momentum from AIP. We generated 726 million in revenue, up 30% year over year and 7% sequentially.
Ryan Taylor: Excluding impact of revenue from strategic commercial contracts, third quarter revenue, 33% year over year and 7% so, eventually.
Ryan Taylor: Customer Count, 339% year over year, and 6% sequentially, to 629 customers.
Ryan Taylor: Revenue from our largest customers continues to expand. Third quarter, Charlie 12, month revenue from our top 20 customers, increased 12% year over year to 60 million per customer.
Ryan Taylor: Now, moving to our commercial segment. Third quarter commercial revenue, grew 27% year over year, and 3% sequentially to 317 million. Exfuing impact from strategic commercial contracts, commercial revenue grew 30% year over year and 3% sequentially.
Ryan Taylor: 3rd quarter commercial TCV booked with 612 million for representing 52% growth year over year and 60% growth sequentially.
Ryan Taylor: Our US commercial business continues to see unprecedented demand, with AIP driving both new customer conversions and existing customer's fanshines in the US as we continue to deploy AM models in production.
Ryan Taylor: 3rd quarter US commercial revenue grew 54% year over year and 13% sequentially to 179 million. Excluding revenue from strategic commercial contracts, US commercial revenue grew 59% year over year and 12% sequentially.
Ryan Taylor: In the third quarter, we booked 297 million of US commercial TCV, representing 13% gross sequentially. To our remaining deal value in our US commercial business, through 73% year over year and 7% sequentially.
Ryan Taylor: Or US Commercial Customer Count, grew to 321 customers, revucking 77% growth year over year and 9% growth sequentially.
Ryan Taylor: We generated $138 million in international commercial revenue in the third quarter, representing 3% growth year-over-year but a 7% sequential decline as a result of continued headwinds in Europe and a step down in revenue from a government-sponsored enterprise in the Middle East.
Ryan Taylor: Despite those headwinds, we continue to build on our transformational work with some of our largest international customers, including signing a multi-year renewal with BP.
Ryan Taylor: We also continue to capitalize on targeted growth opportunities in Asia, the Middle East, and beyond.
Ryan Taylor: Revenue from strategic commercial contracts was $9.6 million for the quarter. We anticipate fourth quarter 2024 revenue from these customers to decline to between $6 to $7.5 million, comparing to $20 million in the fourth quarter of 2023.
Ryan Taylor: We continue to anticipate 2024 revenue from these customers to be less than 2% of full year revenue.
Ryan Taylor: Shifting to our government segment, third quarter government revenue grew 33% year over year and 10% sequentially to 408 million.
Ryan Taylor: Third quarter U.S. government revenue accelerated to $320 million, representing 40% growth year-over-year and 15% growth sequentially.
Ryan Taylor: This acceleration was driven by continued execution in existing programs, new awards reflecting the growing demand for AI in our government software offerings, and favorable deal timing in the quarter, coupled with a government year-end cycle.
Ryan Taylor: Third quarter international government revenue was $89 million, representing 13% growth year-over-year, but a 5% sequential decline as a result of revenue catch-up in Q2 that we noted last quarter and less favorable deal timing.
Ryan Taylor: Third quarter TCV booked was $1.1 billion, up 33% year-over-year and 16% sequentially.
Ryan Taylor: Net dollar retention was 118% an increase of 400 basis points from last quarter. The increase was driven both by expansions at existing customers and new customers acquired in Q3 of last year as we see the effect of the AI revolution in both industry and government.
Ryan Taylor: As net dollar retention does not include revenue from new customers that were acquired in the past 12 months, it does not yet fully capture the acceleration and velocity in our U.S. business over the past year.
Ryan Taylor: We ended the third quarter with $4.5 billion in total remaining deal value, an increase of 22% year-over-year and 4% sequentially, and $1.6 billion in remaining performance obligations, an increase of 59% year-over-year and 15% sequentially.
Ryan Taylor: As a reminder, RPO is primarily comprised of our commercial business, as it does not take into account contracts within an initial term of less than 12 months, and contractual obligations that fall beyond termination for convenience clauses, both of which are common in most of our government business.
Ryan Taylor: Turning to Margin and Expense. Adjusted gross margin, which excludes stock-based compensation expense, was 82% for the quarter.
Ryan Taylor: Adjusted income from operations, which excludes stock-based compensation expense and related employer payroll taxes, was $276 million, representing an adjusted operating margin of 38 percent and marking the eighth consecutive quarter of expanding adjusted operating margins.
Ryan Taylor: Q3 adjusted expense was $450 million, up 6% sequentially, and 14% year-over-year, primarily driven by our continued investment in AIP and technical talent. We continue to expect expenses to ramp through the fourth quarter as we invest in the product pipeline and accelerate the journey from AI prototype to production.
Ryan Taylor: In the third quarter, we generated GAAP operating income of $113 million, representing a 16% margin. We generated GAAP net income of $144 million, representing a 20% margin.
Ryan Taylor: Third quarter adjusted earnings per share was $0.10, and gap earnings per share was $0.06.
Ryan Taylor: As previously communicated, we've aligned our compensation program with the performance of the company's goals, including its stock price.
Ryan Taylor: On the back of the company's strong performance, our inclusion in the S&P 500, and the increase in our stock price, we will continue to monitor if we become required to accelerate stock-based compensation expenses if certain market-based vesting criteria are achieved earlier than expected.
Ryan Taylor: Additionally, our combined revenue growth and adjusted operating margin accelerated to 68% in the third quarter, a four-point increase to our Rule of Forty score from the prior quarter.
Ryan Taylor: Turning to our cash flow.
Ryan Taylor: In the third quarter, we generated $420 million in cash from operations and $435 million in Adjusted Free Cash Flow, representing a margin of 58% and 60% respectively. For the first time ever, on a trailing 12-month basis, we generated over $1 billion in Adjusted Free Cash Flow, representing a margin of 39%.
Ryan Taylor: Through the end of the third quarter, we repurchased approximately 1.8 million shares as part of our share repurchase program.
Ryan Taylor: As of the end of the quarter, we have $954 million remaining of the original authorization.
Ryan Taylor: We ended the quarter with $4.6 billion in cash, cash equivalents, and short-term U.S. Treasury securities.
Ryan Taylor: Now turning to our outlook. For Q4 2024, we expect revenue of between $767 and $771 million, and adjusted income from operations of between $298 and $302 million.
Ryan Taylor: For full year 2024, we are raising our revenue guidance to between $2.805 and $2.809 billion. We are raising our U.S. commercial revenue guidance to in excess of $687 million, representing a growth rate of at least 50 percent.
Ryan Taylor: We are raising our adjusted income from operations guidance to between $1.054 and $1.058 billion. We are raising our adjusted free cash flow guidance to in excess of $1 billion. And we continue to expect gap operating income and net income in each quarter of this year.
Ryan Taylor: With that, I'll turn it over to Alex for a few remarks, and then Ana will kick off the Q&A.
Speaker Change: Thank you for watching!
Alex Karp: Given how strong our results are, I almost feel like we should just go home. But, you know, we've been saying since we went public in a DPO that we would build infrastructure to make America and its allies a dominant force in the world. We claimed
Alex Karp: to much skepticism that this would be done in a software product, that defense and commercial industry would be driven by software.
Alex Karp: hardware hybrid technologies, that there were very few companies in the world that could actually do that, that these companies are basically only built in America, that the companies that have tried to do this that aren't Palantir are built by ex-Palantirians.
Ryan Taylor: that the financials of Palantir would flow from our products and our culture and our way of implementing.
Ryan Taylor: that we would bring violence and death to our enemies while making targeting and general issues of safety better for our allies and for Americans.
Ryan Taylor: that we would stand by our values in thick and thin, including that the West and America are superior ways of organizing, and that this is a great country, and historically anomalous in its greatness.
Ryan Taylor: and that we would build a company with the best people from all over the world, but primarily from America, to power America and its allies.
Ryan Taylor: and even we are shocked by the 44%.
Ryan Taylor: growth in the U.S. off of a two billion dollar base.
Ryan Taylor: So this is not some speculative, small-base...
Ryan Taylor: 44% growth. Even we are happy to see that we grew 30%, to see the real re-acceleration in USG to 40%, and to see the very, very strong results in US commercial.
Ryan Taylor: also allied countries that
Ryan Taylor: have begun to realize that AI is the way in which to make their defenses superior in the face of brutal, heinous...
Ryan Taylor: immoral and often terroristic enemies where you need a superior form of fighting that's both safer for you and more dangerous for the adversary and controls how you hit them and when and where and allows you to maximize your results. It is also just jarring to see how America adopts
Ryan Taylor: the most important, most agile, and most impactful technology independent of who the purveyor and builder of that we are.
Ryan Taylor: building this company, and we believe we're at the beginning, and watching American adoption both in government and commercial.
Ryan Taylor: while not forcing us to change, while accepting that many of the ideas of how we have of how to make your enterprise better, whether it's insurance has been mentioned, oil and gas.
Ryan Taylor: any kind of complicated manufacturing, supply chain, healthcare.
Ryan Taylor: obviously, defense and intel, that a new and different way of building software and implementing it, meaning the infrastructure is where the value is, despite the fact that, as you may have noticed, many have noticed, that the experts that write about these things seem to believe the commodity, i.e., the LLM, is the valuable aspect of this, and that the actual asset, meaning how you manage the commodity, is the actual value, despite great skepticism
Ryan Taylor: The market seems to have decided what works works, no matter how theoretically appealing the idea is of building software around how you may have learned to build it in business school, is to you as the person not buying the software.
Ryan Taylor: the people buying the software who are allowing our market to grow at 30% in aggregate, 44% in America.
Ryan Taylor: 40% in US COM
Ryan Taylor: and dramatically in U.S. government and dramatically in U.S. COM are speaking with their feet and their implementations. And we're very, very proud of that. And I'm particularly proud to see the warfighter adoption of this.
Ryan Taylor: Defense, Intel, beginning to embrace
Ryan Taylor: the application of large language models in infrastructure, obviously something that Palantir is particularly specialized in, and we just, we are really, I think, as a company enjoying this phase.
Ryan Taylor: You know, when you build a company over a long period of time, there are good and bad phases. But to see, in fact, our view of what it makes to make enterprises stronger and better show up in these dramatic results is super gratifying, and we plan to continue.
Speaker Change: Thank you for watching!
Ryan Taylor: Thanks Alex. We'll now turn to a few questions from our shareholders before opening up the call. We received a few questions on AI. How will Palantir differentiate its AI offerings from others including the model creators and how is AIP different and how will Palantir maintain its competitive edge?
Speaker Change: Well, Alex talked about how the models, the LOMs are commoditized, but if you look at the models...
Speaker Change: you see that they're getting better, which is awesome. But they're also getting more similar across both closed and open source models. While they're improving, they're converging upon each other, all while the price of inference is dropping precipitously.
Ryan Taylor: And that's, you know, so if you even look at these model companies, they have to build applications around these models to extract value. That's where we have a decade-long head start. You know, we've been building the forge to create and implement AI applications at scale throughout the enterprise, and that differentiation starts with the ontology.
Ryan Taylor: You know, using the ontology to drive AIP across these applications. When you look at the legacy software companies, I'm not sure they understand it yet, but when you look at the innovative Silicon Valley companies, they recognize the wall of tech investments this implies that's in front of them that's going to act like a grade filter.
Ryan Taylor: I would say, kind of an addendum to that, it's...
Ryan Taylor: Well, first of all, I think people are beginning to recognize we were right, and these numbers show it.
Ryan Taylor: So, that creates a dynamic of, okay, well, if you can actually extract value from large language models in any context.
Ryan Taylor: then clearly the company that does it is very valuable. And a lot of our customers, even a year ago, they were kind of skeptical of, can you make these things useful? I would say most of the customers I deal with are pretty skeptical. You can make large language models do anything but do a science experiment. And in a weird way, even though the models are improving, they're meeting up against greater skepticism among clients because clients have tried them and it's just a high school experiment.
Ryan Taylor: And then if you get to, so it's like there's the market and analysts seem to have put a lot of credibility into the models and we do too. We think they're very valuable when managed correctly, when used in a way that an enterprise can understand.
Ryan Taylor: One of the problems that people have is you're not involved in enterprise software. It's very hard to understand how an enterprise actually works.
Ryan Taylor: You cannot take a large language model that gives you an ELO score of 1,200 and use it on targeting on the battlefield. There's a security model, there's a way in which the data is understood, there's certain things you can't share, there's places you would use certain models but not others. How do you bring that back to your corpus of truth to understand, in a lethal context, who dies and who doesn't? And you have very similar use cases in underwriting and in healthcare.
Ryan Taylor: Understanding the actual way technically enterprise is driven as embodied by Foundry, as embodied by our abstraction layer on top of Foundry, which includes all those nuances which we call an ontology, as powered by AIP, are all sorts of things that our clients are being just beginning to discover every day.
Ryan Taylor: Of course, the main way they're discovering it is, holy shit, I can do this in an hour that used to take five hours, or 50 hours, or, you know, or I could have 2,000 people, or 20 and 10. And by the way,
Ryan Taylor: On the targeting on the battlefield, we've talked about basically two orders of magnitude and reduction of people, but it's also in many cases two orders of magnitude in the production of your ability to do things in an efficacious manner. There are whole global events now that would be very different without these.
Ryan Taylor: without our ability to manage these things in our infrastructure. And that's us, obviously, generating a lot of excitement internally that spills into our earnings.
Speaker Change: Our next question is from Ryan who asks, as Palantir continues to invest in new AI technologies and expand globally, how are you balancing these investments with maintaining or improving profitability and operating margins, especially given the current macroeconomic challenges?
Speaker Change: We're just bouncing. We're excelling.
Ryan Taylor: Revenue grew 30% year-over-year in Q3.
Ryan Taylor: 44% in the U.S. in the quarter, and you couple that with our expanding margins in the quarter. We did 38% adjusted operating margin, our eighth consecutive quarter of expanding margins, and we posted 68 in the Rule of Forty score.
Ryan Taylor: at the same time.
Ryan Taylor: We had an outstanding Adjusted Picasso margin in the quarter, 60%.
Ryan Taylor: to an excess of a billion dollars, well over that actually, which represents a 39 percent margin in Q4. And we're doing all this while we're continuing to invest at the beginning of the AI revolution. That is, there is an incredible amount of demand there and we're investing in technical talent and we're continuing to build out world-class products.
Speaker Change: There's a steel man version of this, which is given how well you're doing, given you've really accelerated to 30%, given the U.S. is growing 44%, why don't you blow up your rule of 68, which by the way, to my knowledge, is the single best of comparable companies in the world, and significantly better than many very strong companies. So an average, normal way of looking at Palantir would be like, oh great, you have a 44% growth on a $2 billion base in the U.S.
Speaker Change: and you have a rule of 68, get that 68 down to 50 and maybe you can grow it. But in fact, that way of looking at a business misunderstands the way in which Palantir builds. We believe that by investing, and we know at this point, instead of trying to have 10,000 clients, all of whom hate you, it's kind of what people want, 10,000 clients that hate you, but they can't get your product, we want a smaller number of the world's best partners.
Ryan Taylor: that quite frankly are dominating with our product and the way you do that is by not blowing up your margin and getting 10,000 sales people. It's actually by going deeper on the product.
Ryan Taylor: And, in fact, what we see is the deeper and better the product, the more we drive sales, the more we have our cultural singular advantage as a panelist here, not as a commodity product. We are not a commodity. We do not want our customers to be commodities. We want them to be individual titans that are dominating their industry or the battlefield.
Ryan Taylor: And we reflect that in how we do things. We are not trying to be your average...
Ryan Taylor: Harvard Business School preferred company with like that that crush that reduces the margins
Ryan Taylor: has a thin product and then has a lower rule of 40 and presumably higher growth. By the way, I don't think you'd get higher growth than what we have.
Ryan Taylor: Honestly, although we of course are always pushing and want even higher growth.
Ryan Taylor: You know, the people who tend to ask these questions tend to be modeling companies with 20% growth and lower margins. We have 44% growth in the most important market in the world, arguably not the only, but by far the most valuable market in the world, while having a rule of 68, i.e. the best in the world.
Ryan Taylor: and we're going to maintain the contradiction of having both high margins and high growth. It's not one or the other. They're actually interplayed and they're not a contradiction. They power each other. That's how you know you have world-class products. That's what you see in your numbers.
Speaker Change: Thank you both. Our next question is from Dan with Wedbush. Dan, please turn on your camera and then you'll receive a prompt to unmute your line.
Dan: A great quarter and congrats to you and the team. So my question is,
Speaker Change: with Boot Camp Conversions.
Dan: Has it even surprised you just how quickly, from a customer coming into a bootcamp, potential customer, to conversion, to mega deals, what you're seeing and what do you think that says about your process and where we are in this AI revolution where Palantir sits?
Speaker Change: Yeah, I think obviously the most indicative of what we're seeing and the impact is the results. The 44% year-over-year growth in U.S. commercial, 54% year-over-year growth in U.S., 54% in U.S. commercial, the sequential growth we're seeing. I think, you know, I gave examples of boot camps where we're seeing multiple different customers across different industries that are going from...
Dan: from the initial boot camp to a seven-figure ACV deal within a matter of less than two months. We're seeing that, we're feeling that in the conversations we have with customers in then our push to go from prototype to production and the expansions we're seeing at customers.
Speaker Change: And I'm seeing that, you know, in the conversations, as we said, you know, really, it's going to be the AI haves and the have-nots. The haves are moving quickly, making decisions quickly, and adopting quickly. And I'm feeling that in the conversations we have with them in the conversions we're seeing.
Speaker Change: There's a small number of increasingly large number of customers meaning interested that get this.
Speaker Change: and they are just moving really quickly, and anyone who is involved in the enterprise, so if you take company XYZ and then five people go to a different company, the first thing they do is pick up the phone and call us.
Speaker Change: and then and so it's just the way in which this just expands in the U.S. quite and in some other countries but especially in the U.S. from anyone who touched this
Speaker Change: wants to use it in any part of anything they're doing. And it goes from one person. And then there's this transience in America where people really are moving to different companies a lot, and they're talking to each other. And there's
Speaker Change: They're just...
Speaker Change: a willingness to take business metrics and use those business metrics against technology that's not ideological.
Speaker Change: And if you look at even 10 years ago, there was no form of software that had this kind of adoption and this kind of readiness. And also it was
Speaker Change: Conversely, just not possible to show this kind of results this quickly. And we tend to focus on the results on the outside. But in, you know, AI and large language models also allow us to scale our product on the inside. So one of the unfair things about this revolution is if you have business acumen and you have a product that is good,
Speaker Change: or Stellar in my humble opinion, you can make it even better.
Speaker Change: internally and externally and so that allows us to also scale many many more people to many many organizations with the same number of people.
Speaker Change: as long as they're the best in the world. And so it's really this from touch...
Speaker Change: however we do it. Some of it's boot camps, but some of it's just like, hey, I used to work at a company. I heard really good things about your product. I want it tomorrow. How quickly can you get here? What is the first use case you could do? And then the first thing that they always ask is, well, show me some of the things that you've done in other places. Like even cross-fertilization between government and non-government. I was at an important government
Speaker Change: entity a couple weeks ago, and they obviously may even be very useful for them. But then they start asking, well, what are you doing for hospitals? Could you use this on FOIA requirements? Could you use the same thing for managing our people? How could you make sure that our people are safe and happy? How do you move parts?
Speaker Change: How do we do procurement? A lot of the things, Shyam, so there's like a massive cost fertilization even between verticals that otherwise would never talk.
Speaker Change: In the past, government use cases did not, non-government use cases were just not things we were doing in government. And certainly, from industry to industry, we did not have this, you know, from real estate to supply chain to large hospital things.
Speaker Change: The use cases that they're doing inside our product, they are technically, basically the same for us.
Speaker Change: Thank you. Thank you. Thank you.
Speaker Change: Thank you. Our next question is from Mariana with Bank of America. Mariana, please turn on your camera and then you'll receive a prompt to unmute your line.
Mariana: Good afternoon, everyone.
Speaker Change: Hi. We can't see you, but it's okay. Okay. You calm around, Steph.
Mariana: So, on government, U.S. government is up, what, like, 40%, and this is in line with what we saw at USAID.
Speaker Change: L3Harris, Andrew, Chill AI, but others were advertising volunteer bloggers, and they were like advertising partnerships with you.
Speaker Change: What changed for them to actually want a partner? That's the first one. And then, Dave.
Dave Glazer: If you don't mind giving an update on the strategic commercial contracts And how are you thinking about the remaining deal value as we see news about Lillium But also a recent pick up of some like stock awards as a form of payment from these companies
Dave Glazer: Thank you.
Speaker Change: On the first bit here, there's really two dimensions to what we're seeing is acceleration in partnerships on the U.S. government side. The first is something I've talked about earlier, which is mission manager. Really, how do we take not our software, but our software infrastructure, Rubik's and Apollo, as radical accelerants for these companies to get to revenue, to service their existing revenue in a more profitable way, and expand their market access?
Dave Glazer: So that's been just a clear win across the board that's good for the government, it's good for these partners, and it's good for us.
Dave Glazer: I don't think Shyam is taking enough credit here, or Shyam, Aki, and others.
Dave Glazer: One of our biggest issues in the U.S. government would be just simply a friction coefficient. And we had the problem that although we are completely focused on helping the U.S. government and allies first, and ourselves second, and that's one of the reasons most of us at the table are still here.
Dave Glazer: There was a general perception a couple years ago that, you know, basically, and, you know, you see it in our numbers, Palantir wins, and maybe we win too much, and we keep winning, and people are like, Palantir makes all this money, someone else has got to make money.
Dave Glazer: And one of the things Shyam and I would say Aki and others.
Dave Glazer: did a tremendous job of is like, look, we're in this for the supremacy of the U.S. and its allies, and we're going to prove this by opening up part of our products.
Dave Glazer: and allowing you to sit on government data which is, you know, lay people assume you could just put a product on government data. You can't do that. There's all these tech...
Dave Glazer: products we've built that allow you to safely and securely work directly with the U.S. government and
Dave Glazer: We've begun offering that to all sorts of defense tech startups and begun partnering with more established...
Dave Glazer: large integrators like L3 and many others actually and the strategy there was look if we actually believe what we believe let's show it and
Dave Glazer: This strategy, which Shyam led and Aki empowered and also kind of spent, you know, did incredible work on, has led to a situation where most people involved in tech innovation now view Palantir as their ally.
Dave Glazer: And so instead of going into every meeting saying, oh yeah, Perlmutter's great, but their fearless leader is batshit crazy, and, you know, he might go off to his commune in New Hampshire, and whatever they were saying.
Dave Glazer: Thank you very much.
Dave Glazer: It's now like, yeah, the products are the best, and we have great products, and so that's a really important shift. And that was a business strategy, and it really was done by others. And that has shifted our ability to get to market because most people don't want to resist. They're not hating the player. They're playing with us.
Speaker Change: This is always a tough act to follow but the second part of that I'll just close out with is really helping these companies with their production. In the same way that we help Airbus build every plane or Chrysler build every car, how do we help L3 and Anduril and Shield and all of these new entrants and existing primes build...
Speaker Change: Their weapon systems better and in particular where they have fixed price drive margin expansion as a consequence of doing that
Speaker Change: And then on the strategic commercial contracts, they're a tiny part of the business, right? Basically 1% of revenue in Q3. On the forward look metrics, it's even like, it's quite minimus and the program ended three years ago. So, you know, we can still answer the question, it's basically not relevant anymore.
Speaker Change: Please see the complete disclaimer at https://sites.google.com
Speaker Change: Thank you
Speaker Change: Alex, we have a lot of individual investors on the line. Is there anything you'd like to say before...
Speaker Change: before we end the call.
Speaker Change: You know, as usual, we're in it together with you.
Speaker Change: even stronger and better, and our allies stronger, better, and all of us more lethal, besides protecting Palantirians and most ex-Palantirians.
Speaker Change: Our individual investors are near and dear to our and certainly my heart.
Speaker Change: And, you know, I love it that you guys are winning. You know, there'll always be ups and downs in building a business.
Speaker Change: But we're definitely fighting for you guys and you know the decision to do a DPO
Speaker Change: where we, which is essentially, was a decision to make sure that individuals got to participate and your willingness to spend time and look at what we're doing and actually look at the facts on the ground and not just to read theory has been crucial to Palantir as a business.
Speaker Change: and it's part of what makes Palantir great and also our nation so great. So thank you and thank you for your support.
Speaker Change: Thank you. That concludes Q&A for today's call.
Speaker Change: Robot Framework is a keyword-driven automation testing approach that solves the problems faced by other scripted automation testing tools.
Speaker Change: It doesn't remove the need of scripting, but reduces it to great extent.
Speaker Change: Robot Framework implements the method of action words.
Speaker Change: a set of keywords intended to make the arguments of certain test functions easier to invoke.
Speaker Change: and reduce the amount of coding required for new test cases.
Speaker Change: This video will take you through all such important concepts necessary to get skilled in implementing robot framework in various testing scenarios.
Speaker Change: We will now look at the agenda of this session, but before we begin, please subscribe to Intellipaat and hit the bell icon to get the latest updates from Intellipaat. First of all, we will begin the session with understanding what is automation. After that, we will discuss automation approaches.
Speaker Change: Moving further, we will understand the basic functionalities of Python and the concepts of Python in unit testing.
Speaker Change: Moving further, we will discuss Selenium WebDriver, and finally, to end this session, we are going to understand how to read and write data using set frameworks and drivers. So without any further ado, let's begin.
Speaker Change: Yeah.
Speaker Change: So, before starting, I will just introduce myself and I just want to say that I am a
Speaker Change: look like, what is the expectation from your end, and then we can get started into the post-curricular narrative.
Vijay: So, myself Vijay, I am about 11 plus years of experience in QA, automation mainly. So, mainly I am focusing on open source technologies like Cucumber, JMeter, PVV, these approaches and I am working with Selenium, EPM and these things.
Speaker Change: So here, this part, for the intelligentsia, I am doing this session.
Speaker Change: So I have the course curriculum for you. They have shared with me the content and everything. Based on that, I'm going to. So the prerequisite for this course is that there is no explicit prerequisite required because, but it is good to know Java.
Speaker Change: because if we know Java we can compare and do the studies or not only Java any other programming language then it is going to be additional thing but it is not mandatory because Python is not going to be very complex. Okay then, so this is the course where the interpreter is given for
Speaker Change: So, I...
Speaker Change: First we may have the introduction about the automation approaches and other things then we will start with the Python So as you are saying that yes, the robot framework is something like our default the keyword methodology only, keyword driven framework only Where just you are going to use some of the keywords and do it
Speaker Change: using no much programming is required but I don't know they have also included the Python over there where we required some of the programming idea but as you are already aware of automation Selenium with Java.
Speaker Change: So, all the concepts more or less in Python will be similar to Java only, only syntax wise there will be some changes and there are here and there some.
Speaker Change: declaration part is not required like that small small changes only in PYTHA
Speaker Change: So, compared to Java, Python is more easier because Java is where the compiler and interpreter are getting performed, but Python is where only interpreter is involved. So, in that way, Python is going to be comparatively very less complex.
Speaker Change: so that is fine and Selenium WebDriver with Python they have given so this is like the commands and everything related to your Selenium as it is
Speaker Change: But instead of your Java library, I'm going to use Selenium Python library.
Speaker Change: And there the syntax again, the methods, whatever you are using, findElement, findElement, switchToFrames.
Speaker Change: Thank you.
Speaker Change: as a whole. So in that perspective. Similarly, calling the browser, so those things are going to be different. But other things like XPath identification, everything is the same as here, Selenium with the Java only, because element-wise we can't do anything, it is something related to application.
Speaker Change: for reading and writing the data you know in Java you have used to file input stream output streams so here we know those kind of stream libraries are available instead we use an alternate approach
Speaker Change: Similarly, for Excel, there we make use of JXL or Apache PUI framework, but here we are going to use PyCharm.
Speaker Change: But one session, one automation we will be doing in the Eclipse session.
Speaker Change: the page factory initialization you are doing, all these things. But here how it is going to be different that we are going to discuss. And then we are going with the Robotic Prim Model.
Speaker Change: So, this is where we are going to download the RPE IDLE and using that IDE we are going to just to configure the test scripts and we are going to run it.
Speaker Change: So here we will be discussing similar to Selenium whatever we are discussing here the same information like handling the alert boxes, drop-down boxes, all these things are discussed over here and how to generate the reporting and other stuffs are done here.
Speaker Change: So, this is our plan. We will get started with the, so this topics might not be required, but still this is the plan for you. So, I have to go by that way. So, let me walk through that and then we will get into the basics of Python.
Speaker Change: So first, let's start with the benefits of automation testing.
Speaker Change: So, automation testing you know as an automation tester still now you are working, so process wise you are not going to make much difference.
Speaker Change: So, till now whatever the manual, so you have worked on the manual end as well as on the automation end. So, on the manual end whatever the operations you have performed, the same operations only you are performing for automation testing.
Speaker Change: where additionally one portion you are getting or one cycle or one step or one process you are getting involved. What is that? Once as a manual tester you prepare a test case, you are filtering the test cases.
Speaker Change: So, say manual test cases, 100 cases you have written means, then you go through the manual test cases and you want to identify what are the cases could be automated.
Speaker Change: So then, you are automating the scripts or automating that application and creating test automation scripts that can be of any type, QTP or Selenium or APM or anything.
Speaker Change: So that is where the automation testing is different. So automation testing as you are aware it is not going to be something which is going to do everything automatically but it is going to follow the instruction provided by the automation tester. So we are going to provide the instruction. How do we provide the instruction?
Speaker Change: That is where we are using the scripting languages or programming languages based on or feasible for each and every operating system or each and every automation tools. For example, in QTP, the instructions are provided in VBScript.
Speaker Change: If I go with Selenium with Java, the instructions are provided with the Java code or Java language.
Speaker Change: So in Selenium with Python we are going to write the Python script. So automation testing is a technique where we are going to use some sort of automation tools.
Speaker Change: In order to replace the user operations, so instead of a manual user sitting and performing an operation, let
Speaker Change: The two will do that.
Speaker Change: But for that tool, I am going to give the instructions, how the tool has to do it, how much time it has to take it, what is the step by step operation that the tool has to do everything.
Speaker Change: So what are the reason we are doing that means as a process now everyone is following the automation testing that is well but what is the main reason we follow automation testing means now the most important thing is
Speaker Change: repeatability so as you are working in the real time you know mostly we work with the when you are in the service based companies or service based projects like banking or insurance
Speaker Change: So those are very long time projects, mostly you will have a contract of five years or seven years contract.
Speaker Change: So those, on a monthly basis, they may give some change request or a new request, so where you are updating everything.
Speaker Change: Now you can take even Gmail itself. You just think of the Gmail scenario. So like around 10 years, the Gmail has been in place. But whatever the default concept of Gmail, which was during the time of interaction, that
Speaker Change: The same remains now, the inbox, composing, drafts, everything else. So now when they have introduced or when they are upgrading it to a new...
Speaker Change: Technology or Energy
Speaker Change: They need to make sure that the existing
Speaker Change: components are working. So for that we need to do a regression testing. So regression testing may have around in a real time
Speaker Change: Even though we say multiple things as a benefit, but most importantly as a automation tester in the real time, the repeatability is the main factor we consider. And then about the
Speaker Change: time so consumption of time you know as automation tester it consumes the time but not from the perspective of writing the test cases it consumes the time from the perspective of execution
Speaker Change: So when you use CIECD tools like Jenkins or TeamCity
Speaker Change: You don't want to wait for any build requirement or anything. You just mark it. So as soon as the developer maps the build and new build is deployed on a night or on a timely execution, it is going to run and you will get the reports.
Speaker Change: So mostly all the regressions which we will map it towards tonight when I'm leaving the organization We are going to do that. So there is no manual intervention required as well as there is no process Like a manual user should be there to invoke the scripts or anything else
Speaker Change: But the most important thing about this is ensure consistency.
Speaker Change: and the label The Impressive.
Speaker Change: So, the meaning of reliability and results is, you know, as a factor.
Speaker Change: So, when we are as a manual tester, when we are doing level 1 testing, level 2 or you consider you are doing a testing in QA environment on staging and UAT environment.
Speaker Change: There is a possibility where one or two test cases may be missed or they may be skipped putting an assumption that it is not, it will work.
Speaker Change: because that is a major complex test case like that or it is an easier test case. So like that, there is a possibility where as a manual user you can skip it. But an automation script is not going to skip anything.
Speaker Change: unless you intentionally go and do that, right?
Speaker Change: It is always going to execute line by line only as a code.
Speaker Change: They are having a sequence where it has to execute line by line.
Speaker Change: So, any of them are auto-skipping or nothing can be happening there. So, results wise I can't rely on the tools because it is going to strictly follow the instruction provided by the automation tester.
Speaker Change: Thank you.
Speaker Change: consistency wise also.
Speaker Change: So today it has taken one hour for execution, tomorrow also plus or minus five.
Speaker Change: Not more than that.
Speaker Change: and similarly today it has executed 700 cases means tomorrow also if the application has
Speaker Change: possibly fine and with no errors or with no showstoppers, then it is going to take the same amount of time and the same line.
Speaker Change: Benefits of Automation Testing, MATLAB.
Speaker Change: When we see the other side of automation testing, there are some factors involved, even though you may justify it saying, hey, I'm using an open source tool, so I'm not charged with any licensing costs.
Speaker Change: Anything else? Right, well done, good. But if you see Selenium with Java, you people are using.
Speaker Change: But most of the organizations, like if it is a product-based organization mainly, they don't rely on open source.
Speaker Change: So, even though your application is created in Java, they still rely on IntelliJ because it provides major functionalities like configuring the SVNs, Git directly over there and it provides some sort of security jetbrains.
Speaker Change: Itself is there providing the community edition as well as the commercial edition also.
Speaker Change: But we prefer to go with the commercial edition, so in that also there is a cost involved.
Speaker Change: And compared to any other tools, when we go for open source tools, the market is huge. So, the automation testers demand is also high. So, cost-wise, we may say that
Speaker Change: from the wider perspective, say, it decreases the cost.
Speaker Change: but definitely it is not like that, it all together it is decreasing the cost because the time is reducing, execution time is reducing because of that because the testers mostly are charged on a hourly basis so
Speaker Change: It is getting compensated, that's all I can say, but I don't, at any point of time I am not ready to agree that.
Speaker Change: Thank you very much.
Speaker Change: so why automation as we are aware of that so the first and foremost as you everyone has worked up in automation the most important thing is we as a industry when you are working you will do the testing on multiple
Speaker Change: levels like first when the development is done it will be moved to the QA environment. So first level zero or QA environment testing has to be done. So in that identify the major defects and get it isolated fix it. Once all of them are fixed then it has to be moved to the staging environment.
Speaker Change: So, there again level 1 testing is done, so where you identify the next level of bugs.
Speaker Change: So once again, if you fix that, then move it to the IT environment or pre-production environment. Again, so like that, multiple levels of testing you will do. So that will increase the coverage of testing. So that is one aspect. And the other most important thing is, if you are having multiple application servers,
Speaker Change: Like you know, Google.co.in is there, Google.co.uk is there, similarly Yahoo!
Speaker Change: Similarly, any application if they are deployed in multi-global global occurrences.
Speaker Change: So they have a set of functionality themes, but according to the region, the results and other things may vary.
Speaker Change: So for that, I cannot create another automation scripts, right? So the default, we just update the URL and we will be running it.
Speaker Change: And the other most important thing is software testing does not require human intervention which is as I said overnight execution we are going to do.
Speaker Change: It increases the speed of test execution.
Speaker Change: it increases the speed of test execution means we cannot do anything on the application end to make the application faster and it is not realistic also if we are doing like that
Speaker Change: What do we mean by increase the speed of testing solution means, we can consider from the perspective
Speaker Change: prerequisites and post-requisites. For example, when you want to do a prerequisite for your manual testing, what are the things you do? Yesterday you may have done the regression testing, so you have to clear the databases.
Speaker Change: plus all the data.
Speaker Change: If you need, you have to create the data, ask like that, prerequisites are there.
Speaker Change: Once you have done all the activities, you have to close the connection with the database, you have to close the files, all these things, and you have to, if there are any batch process has to be started, you have to stop that like that. So in that case.
Speaker Change: In when we are going for automation we can write backfiles or we can call the backfiles at the end of it or in Selenium with Java itself you have the before suite and after suite where we can write some of the cases which has to be done after the suite is completely ended.
Speaker Change: So, like that, so in that perspective only it is.
Speaker Change: The speed of test execution is increased, not from the perspective of interacting with the application. Because interacting with the application, it requires the amount of time to be provided for the application to perform the operation. I cannot make fast the application because the application may not be able to complete the operation what it is supposed to do.
Speaker Change: Automation helps in increased test coverage. How it helps in increased test coverage? Again, this is not based on the automation tool. It is based on the automation tester. So we have to cover most of the scenarios and based on that only we can completely cover all the testing part.
Speaker Change: so this part we accept it because you know as a manual tester when level 0 we find one or two defects and we retest it and the level 1 we will be lethargic you know that
Speaker Change: because already in 11.0 no errors are found so how can be the same build only they have deployed again
Speaker Change: So we may skip some test cases that is what they are saying here which test case to automate so as a tester
Speaker Change: So, I don't know which process you people are following, currently we are in Agile. So, for example, there is a scenario for me, there is a user story provided for this week.
Speaker Change: for this print. So.
Speaker Change: Based on the user story, the manual testers are going to prepare the test cases, you know in agile documentation are not important, but still they will be providing the high level cases.
Speaker Change: From the high level cases, I am going to identify the critical business scenarios, which may require to test in all the builds or which may require to test in all the builds.
Speaker Change: every time whenever there is a change in a particular module or particular application. So identifying those kind of critical modules and check the possibility whether that can be automated or not.
Speaker Change: So that is the first case which test cases means first one is complex that is complex in that sense not about the operations you perform the complexity of the business
Speaker Change: and how that case is impacting the
Speaker Change: application and also you can identify
Speaker Change: tedious or which is difficult in order to perform manually for example in a simple way
Speaker Change: you want to do a registration after a registration successful message is done you want to write a query in the database and drive the data and you have to compare the data whatever you have registered whether it is available means yes you can do it
Speaker Change: But it is like a process where you have to execute multiple queries manually, retry one by one record and you have to compare it front end and the database, so that can be automated.
Speaker Change: because that manually it is going to take some time because you have to run query one by one for each query execution it is going to take time and manually you have to compare the things whereas instead of that if you are using a string comparison it is going to be easier.
Speaker Change: Bruh
Speaker Change: and what are the cases is not suitable for automation. As an automation tester you should be aware of that now. Test cases that are newly designed are not executed manually at least once. The reason is
Speaker Change: The most important factor of automation is stability. When application is not stable, we never consider it for automation, right, because when the application is unstable
Speaker Change: Suppose consider you are automating a Selenium application, the element is keep on updating. How many times you can go inside the object repository file or inside the script and make changes related to that. Today there is a field called gender, male, female.
Speaker Change: It is a radio button. Tomorrow they are changing into a dropdown. So a dropdown means you have to use select queries. If it is a radio button, you have to use list, find elements. And based on that, you have to identify the value and stuff.
Speaker Change: So, now the logic itself we have created.
Speaker Change: So, when they keep on changing like that, you cannot go further. So, the stability of the application is important and that can be any kind of tool, not only for Selenium. Even QTP, the same problem, when the object repository is captured and stored, now the object changes, QTP also will not recognize it.
Speaker Change: and test cases for which the requirements are frequently changing. For example, you know
Speaker Change: The Gmail functionality before 3 or 4 years if you take. Functionality wise there is no major change, but the login screen wise there is a change.
Speaker Change: You may know that before three years, you have to enter the username and password together and then you have to click on the sign in, but now first you will enter a username and click on the next button.
Speaker Change: and then it will prompt for the password.
Speaker Change: So, functional wise, if you see, if the username is correct, then our username is valid, then only it will ask for password, but earlier it was not like that. So, here the requirement is a little bit changed.
Speaker Change: So, in that case you have to insert a new line of code to make sure that you are hitting on the next button. So, like that in that case is also automation has not.
Speaker Change: will not be a suitable option.
Speaker Change: test cases which are executed on an ad-hoc basis.
Speaker Change: Basically, you know that in a manual testing, you do monkey testing, exploratory testing, ad-hoc testing.
Speaker Change: But these cases we never automate. We automate either with the functional test cases or regression test cases.
Speaker Change: So exploratory testing is to ensure only whether the application is suitable. So whenever we start the application to automate, we may do it. But other than that, ad hoc testing or exploratory or monkey testing, we never automate.
Speaker Change: So these are the things.
Speaker Change: So this is a testing process.
Speaker Change: So, basically this testing process will not be experienced by everyone.
Speaker Change: because when we are moving from manual testing to automation testing, we may first get involved in the execution process and then we will be moving to a...
Speaker Change: announcing the script or revamping the script and then we will start working with the script. But the actual process of automation is a little bit different.
Speaker Change: So, where it is a cyclic process, so consider that an application is released or an application is completed development and it is given to the testing means.
Speaker Change: Assume that there is no existing framework or existing automation approaches available in your organization then this is the process we follow. The first and foremost thing is for that application
Speaker Change: Before this test stool selection there will be one process we will have which is called as
Speaker Change: Factors to Check for Automation
Speaker Change: So, whenever an application is given, so we will test the three factors for the application whether this application can be considered for automation. As I earlier said, one is stability of the application.
Speaker Change: and the need for automation.
Speaker Change: and the level of execution because so from the beginning whenever we discuss about automation testing everyone says a point like time-consuming process manually so when you go for automation it is very faster but is it really like that means no automation is a time-consuming process because we have to prepare a script
Speaker Change: execution wise it is very faster so it is able to compensate the entire time
Speaker Change: So, but when does that execution is faster, because of that we are compensating means, when is it possible means, when the level of testing is at least greater than 3 or something else.
Speaker Change: take Amazon. So Amazon has multiple features enabled to that because it has a seller login, buyer login, and you have a finance domain where all the product prices, everything gets involved.
Speaker Change: So that is from the other aspects.
Speaker Change: So, when I want to do an end-to-end testing, it is like I have to log in, I have to purchase a product, and then I have to log into the finance module and see whether the cost is added, whether the quantity is decreased, whether the stock is decreased, everything I have to verify.
Speaker Change: So in that case multiple levels of testing is proposed, but if the same take some applications like just information providing websites like a company website or something
Speaker Change: so that and all not having much complexity and those are not required for automation at all because those are one-time activities so once you test it and deliver it that's all after that maybe after five or six years the same customer will come for upgrading the site
Speaker Change: So, during that time we may use a different technology or something, so those things cannot be automated or need not to be automated.
Speaker Change: 20 only can be automated means is it a good approach to choose automation here?
Speaker Change: or your application you know as you are four or five years working in automation you know that still now we cannot automate capture images, OTPs. We have a workaround for that, that is different.
Speaker Change: But still now the reality is we can't do that. So if your application is having multiple ODBs related things, then we cannot go for automation. So that one is, and then the stability as discussed in the previous point. Stability is the application elements are keep on changing.
Speaker Change: So how can I consider it for automation? So when these three factors are convinced
Speaker Change: Then we can go for the next thing. So assume that the test tool selection, it includes three factors again. What are the factors? Means one is the licensing, which is ROI, return on investment.
Speaker Change: so based on that only they will identify for example the testing cost you may know that 60-40 is the priority we provide right 60 for development 40 for testing so the in this 40 percentage how much I'm going to purchase for licensing cost
Speaker Change: So should I go for a licensing tool or not? So that we need to identify. That is where the first tool selection depends on the return on investment and then it depends on the resources.
Speaker Change: So, suppose I am not able to choose licensing due to budget, because of that I am going to choose the open source means, do I have enough resources who can work on open source?
Speaker Change: If I don't have enough resources, do I have time for them to provide the appropriate training and get them involved? So considering these three factors only, you can be able to select a tool.
Speaker Change: So, once you select the tool immediately you cannot start the automation, you have to define the scope for automation.
Speaker Change: So how do you define the scope? That is where you are isolating the cases for automation. So just by isolating the cases, you cannot do it. So during the scope of automation, here you prepare a POC.
Speaker Change: proof of concept. So here you have selected a tool called QTP. Now under the scope of automation you have identified minimum of 70 percentage can be automated.
Speaker Change: but I am not sure whether this application is suitable with QTP. So that is where the POC comes. So the POC is where you take one complex module
Speaker Change: prepare the test cases for it, choose a framework for it like it can be data driven or keyword or anything and implement that
Speaker Change: for this complex module alone.
Speaker Change: so prepare the framework for that particular and run it generate the reports
Speaker Change: showcase it or demo it to the customers or the end users or the management to make sure that whatever we are expecting is available with the reports and this is how we expect the automation program.