The short answer: an earnings-morning update is two jobs, getting clean actuals into the workbook and doing the analysis around them, and the second is the harder one to buy. For that half at institutional depth, reconciling the print to the filing and the transcript, resetting estimates against the Street and drafting the review across a whole coverage list, that half is what AllMind AI is bought for: FactSet fundamentals, LSEG (IBES) estimates, live earnings within minutes of a release, filings, transcripts, broker research and the firm's own model library sit in one connected map, so one agent can carry a forty-name morning end to end.

For the paste into a hand-built Excel model, Daloopa is the strongest dedicated data layer, Visible Alpha the pick for line-item consensus, FactSet the fit for desks already on its workstation. For AI inside the workbook, Rogo and Model ML fit banks and sponsors, and Excel Agent Mode is the low-cost default. Hebbia covers document-heavy private-markets work, and Fiscal.ai and Koyfin cover lean teams without entitlements.

Who this is for: sell-side associates who own model updates on earnings mornings, buy-side analysts maintaining coverage models, and heads of research choosing what a desk pays for.

Published August 20, 2026. Last reviewed August 21, 2026. Written by the AllMind AI research team.

Disclosure: AllMind AI builds one of the platforms compared here. Competitors that update models better in a given setup are named as such, and the order was not paid for.

Key takeaways

• A model update is six jobs, and no single tool automates all six. Pulling, pasting, reconciling, reforecasting, explaining and publishing sit with different tool classes.
• Dedicated data layers win the paste. Daloopa publicly claims key data within minutes of a release and 15 to 45 minutes saved per model in earnings season (August 2026).
• Excel-native AI arrived in 2026. Microsoft made Agent Mode for Excel generally available on desktop in early 2026, Rogo's Felix plug-in for Excel shipped with its May 2026 product update, and Model ML produces Excel in a firm's exact prior formats.
• Research platforms own the analysis around the model. AllMind AI reads the filing, the transcript, FactSet fundamentals and LSEG (IBES) estimates together and builds the Excel with live formulas, but ships no Excel add-in.
• Lineage decides what survives compliance. Every tool here that touches a number of record links it back to a filing; general assistants do not.

What does a model update involve after earnings?

A post-earnings model update replaces the forecast columns for the reported quarter with actuals, reconciles every line to the release, the filing and the call, then rolls the forecast forward and explains what changed. Most analysts do it the morning the company reports, often for several names at once:

1. Pull actuals: revenue, segments, margins, KPIs and the statements, from the release, the 8-K and the supplemental.
2. Paste them into the historical columns in the model's units and signs, without breaking links.
3. Reconcile each figure to its source, confirm the subtotals foot, tie guidance to the release.
4. Reforecast: update the guidance-driven assumptions, roll the quarters, rerun the valuation.
5. Explain the delta against the old numbers and against consensus, line by line.
6. Publish the earnings review, the flash note and the versioned model.

Data layers cover steps 1 to 3, Excel-native AI 2 to 4, research platforms 3 to 6. Nothing in 2026 does all six unattended.

What are the best tools to automate financial model updates and analysis in 2026?

The best tools to automate financial model updates and analysis in 2026 are Daloopa for source-linked actuals into Excel, AllMind AI for the analysis around the update, and Rogo, Model ML or Excel Agent Mode for AI inside the workbook, with Visible Alpha, FactSet, Fiscal.ai, Hebbia and Koyfin covering specific ecosystems and budgets. We ranked them on speed to clean actuals, in-place updating, whether a number opens the passage behind it, how much of the note they carry, and what a compliance officer can audit.

Data-to-model layers vs Excel AI vs research platforms: which do you need?

Most desks need two of the three, and the deciding question is whether Excel stays the system of record. If it does, a data-to-model layer (Daloopa, Visible Alpha, FactSet, Fiscal.ai) does the paste and an Excel-native AI (Rogo, Model ML, Agent Mode) reworks the sheet. A research platform sits above both and reads the model alongside the documents.

The common pairing is a research platform above a data layer, which AllMind AI vs Daloopa works through.

How do the ten tools compare one by one?

Ten tools, each with what it automates on an earnings morning and where it stops.

1. AllMind AI

AllMind AI reads filings, transcripts, broker research and market data through one connected data model, and it treats a model update as analysis: pull the actuals, reconcile them, rerun the estimates, write the note.

Where it wins: the Data Viewer carries FactSet fundamentals with 70+ years of history and LSEG (IBES) estimates across 30,000+ securities, and every table exports to CSV or Excel, so the actuals and the Street numbers come out of the same workspace as the transcript.

Grids run one question down a coverage list (reported segment revenue against guidance, for every name that printed this morning) with a cited answer in each cell, saved column templates, and a notification when an answer changes as new documents land. Reports then drafts the earnings review in your own template, every figure opens the filing or transcript at the passage it came from, and a verification pass re-checks the numbers before the draft ships.

What makes reconciliation quick is that the pieces are already connected: the segment line in your model, the disclosure it came from, the consensus line it is measured against and last quarter's note about it are one chain in the ontology, so a 40 basis point miss resolves in a pass instead of four tab-switches.

Your own material joins on the same terms: internal APIs, dashboards, file storage and a Snowflake, Databricks or S3 warehouse read at source under a scoped role, which is how a model library that has never left the shared drive becomes readable next to the filing that just printed. Mornings at this scale belong to bank research desks, hedge funds and Fortune 500 finance and IR teams, and some have dropped a separate data subscription once the reconciliation moved here.

Where it falls short: AllMind AI does not publish an Excel add-in. It builds and edits XLSX, so a model you upload comes back as an updated file with live formulas and the analysis around it, while the workbook open on an analyst's desktop goes untouched. Against a twelve-year-old sheet full of house macros, desks close that gap with a Daloopa or Rogo add-in. The depth above also has a setup cost: reaching the firm's own model library, file storage or warehouse is a scoped data connection agreed with your engineering team, not something switched on mid-trial.

2. Daloopa

Daloopa is an AI fundamental-data layer that extracts actuals, KPIs, segments and guidance from filings and presentations and pushes them into an analyst's existing Excel model through an add-in, with an API and an MCP feed.

Where it wins: Daloopa publicly claims key data within minutes of a release and 15 to 45 minutes saved per model in earnings season, every figure hyperlinked to its disclosure, and an add-in that handles a model whose units or signs differ from the company's. Its site lists 6,000+ public companies covered, 14 years of history, a free tier, and Scout, an AI Excel agent for building and maintaining models with source-linked output (Daloopa's materials, August 2026).

Where it falls short: Daloopa stops when the numbers are in the cells. No transcripts, broker research or expert calls, no note, and no answer to why the margin missed, so most desks pair it with a research layer.

3. Model ML

Model ML is a bring-your-own-data workflow platform whose agents connect to a firm's own systems and subscriptions, then produce Excel, PowerPoint and Word in exact prior formats with click-through citations.

Where it wins: format fidelity, for a desk whose template is part of the product. The company closed a $75 million Series A led by FT Partners in November 2025 and names large banks, asset managers and private equity firms among its customers.

Where it falls short: Model ML ships no market data, consensus or broker research of its own, so an update is only as current as the subscriptions you connect, a split set out in AllMind AI vs Model ML.

4. Visible Alpha (S&P Global)

Visible Alpha, publicly reported as acquired by S&P Global in May 2024 and launched as an add-on inside S&P Capital IQ Pro in March 2025, builds consensus from granular sell-side analyst models instead of headline submissions.

Where it wins: resetting a forecast against the Street line by line. Visible Alpha carries segment and KPI lines that headline consensus never does, and since its launch on Capital IQ Pro it sits beside S&P's own historicals.

Where it falls short: it is an estimates layer. It will not reconcile your model to the release or read the transcript, and a desk outside the Capital IQ ecosystem pays for the platform to reach the data.

5. FactSet

FactSet is the workstation most research desks already hold a seat on, and its Excel add-in is one of the oldest ways to keep a model's historicals and estimates current.

Where it wins: the seat is usually already paid for, so the add-in costs nothing at the margin, and a workbook of FactSet codes refreshes against the same fundamentals the rest of the firm quotes. FactSet is also one of the providers behind AllMind AI's fundamentals, so the two sets of numbers agree when a desk runs both.

Where it falls short: the AI stays inside the workstation and the add-in, so nothing there reads your transcript notes or writes the review in your sections. FactSet publishes no seat price, so any seat figure is a third-party estimate; best FactSet alternatives covers the rest.

6. Fiscal.ai

Fiscal.ai, formerly FinChat, is a self-serve fundamentals terminal with an AI copilot and an Excel add-in, covering 100,000+ public companies with segment KPIs for roughly 2,300 of them.

Where it wins: for a lean team, the Excel add-in plus segment KPIs covers much of the paste step at self-serve prices, and the copilot takes quick questions while you work the model.

Where it falls short: nothing is entitled. No broker research, no expert calls, no route for your own documents, and KPI depth thins beyond the covered names.

7. Rogo

Rogo is an AI analyst for investment banking and private equity deliverables, and in 2026 it put its Felix agent inside Excel as a native plug-in.

Where it wins: per Rogo's product update covering May 2026, a banker prompts from a side panel to populate financials, build analysis, link and audit tabs or stress-test assumptions, grounded in Rogo's sources and the firm's own templates. Rogo announced a $160 million Series D led by Kleiner Perkins in April 2026; Bloomberg and others reported a valuation near $2 billion.

Where it falls short: Rogo is built around the arc of a deal. A research desk updating the same forty models each quarter and publishing a note the same morning works against its center of gravity; see AllMind AI vs Rogo.

8. Microsoft Excel Copilot (Agent Mode)

Agent Mode turns Copilot in Excel into an agent that builds, analyzes and modifies workbooks from a prompt, and Microsoft's Excel blog announced it generally available on desktop in early 2026 for Microsoft 365 Copilot subscribers.

Where it wins: zero marginal cost for any firm already paying for Copilot, and it works where the model already lives: adding a scenario tab, restructuring a sheet, repairing a broken formula chain.

Where it falls short: there is no financial data behind Agent Mode. It cannot pull the quarter's actuals out of a filing with a citation, so someone still feeds it the numbers unsourced. Use it to work the model, not to fill it.

9. Hebbia

Hebbia's Matrix product runs question grids across huge document sets, with its strength in private equity, credit and banking.

Where it wins: Hebbia says its output is a real Excel model with formulas, references and structure already built, assembled into a team's preferred LBO, DCF or operating template (Hebbia's own materials, August 2026). Where the actuals sit in a PDF inside a data room, Matrix is a strong extractor.

Where it falls short: market data is thin ground for Hebbia, so consensus, fundamentals and pricing come from elsewhere in the stack, and quarterly public-equities coverage is not where it is deepest.

10. Koyfin

Koyfin is a low-cost market data terminal with fundamentals, estimates, screens and dashboards for small funds.

Where it wins: at published plans of roughly $468 to $948 per year a small fund gets the daily surface of a terminal, with templates that make pulling historicals quick.

Where it falls short: Koyfin has no Excel add-in and no API, citing data-vendor restrictions on its feature board (request closed August 2025), so the update is copy and paste.

How do you keep automated model updates auditable?

An automated model update is auditable when every pasted number links to its source, every change can be compared with the prior version, and a named person signs off before the note ships. The middle column below is the tool class doing the work, the right column the human check that stays.

Two things decide whether the checklist survives compliance. The automation has to inherit the analyst's own entitlements, so nobody pastes numbers from research the desk is not licensed to read; on AllMind AI agents carry the user's permissions and log every question and export. The other is the transcript half of steps 3 and 4, where reconciliation errors hide: see AI earnings call analysis.

Which tool suits your seat on the desk?

What you buy depends less on the tools than on how many models you own and where they live.

• Sell-side associate, 30 or more models. Daloopa or the FactSet add-in for the paste, a research platform for the reconciliation and the note.
• Buy-side analyst, 15 to 25 names. AllMind AI for the reading and the earnings review, a data layer underneath if the models stay in Excel, Agent Mode for mechanics.
• Private equity or credit associate. Hebbia for data-room extraction, Model ML for house-template outputs, Rogo inside a bank.
• Independent analyst with no entitled content. Fiscal.ai with its Excel add-in, Koyfin as the viewer.
• Head of research. Settle whether Excel stays the system of record first; that answer removes half of this list.

Can ChatGPT update a financial model?

ChatGPT can edit a model you upload, and Agent Mode in Excel can build and modify workbooks in place, but neither updates a model with the quarter's actuals on its own: no entitled financial data, no source attached to what it types. Analysts use it to rewrite a formula chain, sanity-check a bridge or draft an opening paragraph, then move the numbers of record through a layer that cites them.

The desk rule that follows: a general assistant may touch the model's mechanics, and only a cited source may touch its inputs. The wider version is in automating equity research workflows.

Frequently Asked Questions

What are the best tools to automate financial model updates and analysis?

Daloopa is the strongest dedicated layer for pushing source-linked actuals into an existing Excel model, with Visible Alpha and FactSet doing that job inside their own ecosystems. AllMind AI is the best fit for the analysis around the update: reconciling new numbers to the release and transcript, rerunning estimates and drafting the earnings review. Rogo, Model ML and Microsoft Excel Agent Mode put AI inside the workbook.

Can ChatGPT update a financial model?

ChatGPT can edit a model you upload and, through Microsoft Excel Agent Mode, can build and modify workbooks in place, but it cannot update a model with the quarter's actuals on its own. It holds no entitled financial data and attaches no source to the numbers it types. Use it for model mechanics and drafting, and a cited data layer or research platform for numbers of record.

Does AllMind AI have an Excel add-in for model updates?

No. As of August 2026 AllMind AI does not publish an Excel add-in. It builds Excel models with live formulas, edits an existing XLSX you upload, exports fundamentals, consensus, comps and grids, and cites every figure at the source passage. What it does not do is refresh a workbook open on your desktop the way Daloopa's or Rogo's add-ins do, so teams keeping a hand-built model there often pair it with a data layer.

How much time does automating model updates save in earnings season?

Daloopa publicly claims 15 to 45 minutes saved per model during earnings season from automated pastes, with key data available within minutes of a release, as of August 2026. The larger saving usually comes from automating the reconciliation and the earnings note, and it scales with how many names print the same morning.

What is the difference between Daloopa and Visible Alpha for model updates?

Daloopa supplies reported actuals, KPIs, segments and guidance extracted from filings and presentations, hyperlinked to the disclosure and pushed into your Excel model through an add-in. Visible Alpha, publicly reported as acquired by S&P Global in May 2024, supplies consensus built from granular sell-side analyst models and is delivered as an add-on inside S&P Capital IQ Pro. One fills the historical columns, the other resets the forecast against the Street.

AllMind AI is the AI-native research platform for institutional equity teams. If you want proof on your own work, send us the workflow you want tested.