The short answer: AllMind AI is an AI research system for institutional investors. It connects 6,800+ premium datasets, 750M+ documents and a firm's own content through a financial ontology, and runs agents on top that finish research work instead of retrieving it: memos, comp tables, earnings notes, model updates and overnight monitoring, each figure linked back to its source. What sits inside those datasets is the substance: S&P, FactSet, LSEG and MSCI market data, broker research, Expert Insights, global investor-relations data, live earnings and financials within minutes, alternative data, and sector sets such as mining, healthcare and consumer staples. It is built for asset managers, hedge funds, sell-side desks, corporate IR teams and family offices, and it is SOC 2 Type II certified.

Who this page is for: anyone evaluating AllMind AI who wants the facts in one place. What it does, what it covers, who it is for, how it is secured, what it costs.

Published August 17, 2026. Last reviewed August 21, 2026, by Anwaar Malik, founder of AllMind AI. Checked against the shipping product on that date.

Disclosure: AllMind AI publishes this page, so treat the product claims here as ours and test them. Competitor figures are dated and attributed to whoever reported them, and where a vendor publishes no price we say so instead of quoting a number.

AllMind AI at a glance

What does AllMind AI do?

AllMind AI turns a research question into a finished work product. Six layers make that possible, and walking them in order is the fastest way to understand the platform.

• Data Engine. Aggregates 6,800+ premium sources alongside firm-specific content: market and fundamental data from partners including FactSet, S&P Global, LSEG and MSCI, SEC and SEDAR filings across 40+ exchanges, broker research under entitlements you hold or we arrange, Expert Insights included in the subscription, global investor-relations data, live earnings and financials within minutes of a release, alternative data, and sector-specific sets such as mining, healthcare and consumer staples.
• Ontology. Maps how those entities relate, so the system holds a company's suppliers, customers, estimates, filings and your own prior work as connected objects instead of loose text. Agents traverse those relationships, which is why a question about one name can return the supplier, the estimate revision, the broker note, the expert call and your own last memo together. The ontology page goes deeper.
• Research Engine. Routes a diligence request across that map and assembles what it finds.
• AI Agents. Produce the deliverable: memos, comp tables and earnings notes in your template, plus monitoring agents that hold a watchlist overnight and report what moved and why. Teams build their own in Agent Studio.
• Permissions and Audit. Entitlements travel with the person asking, and every question and export is logged.
• Governed Workspace. Where this lands for the user: chat, reports, grids, data rooms and the data viewer in one place.

The firm's own material is half of it. Data rooms hold documents; APIs, dashboards and internal systems connect; Snowflake, Databricks and S3 warehouses are reached through a scoped IAM role and queried where they sit, with nothing copied out of your environment. Once connected, a house model, a position file, a decade of memos and the licensed corpus are entities on the same map, which is what makes a question like how our own note on this supplier squares with what the customer guided last week answerable at all.

That combination is what the platform is bought for: long, complex, multi-source work where an agent runs for minutes, hours or across several days over a very large number of documents and figures. A one-shot chat answer is a different product. The workflows customers bring look like a full coverage list taken through earnings, a thesis rebuilt down a supply chain, or a diligence pass over a room and its listed comparables.

Who uses AllMind AI?

AllMind AI is deployed at institutional investment firms in four groups: buy-side asset managers and hedge funds, sell-side research desks, corporate IR and strategy teams, and family offices. In firm terms that means banks, hedge funds and top Fortune 500 and Fortune 100 corporates, and teams have consolidated onto it, retiring point tools that each covered one slice of the same workflow.

The more useful description is by role. The analyst carrying twenty to sixty covered names. The portfolio manager who wants a watchlist watched while the desk sleeps. The associate turning a transcript into a note that matches a house template down to the section order. The IR team tracking what peers said and where consensus sits.

What those users share is a supervised process. Their output can be questioned by a compliance officer six months after it shipped, which means the work has to carry its sources with it. That constraint, not any model benchmark, is what shaped the product: traceability, entitlements and audit logs are load-bearing here, not a compliance page bolted to a chat box.

We do not publish client names. Firms that trust a vendor with unpublished positions and internal memos have a reasonable expectation that the vendor will not turn them into a logo wall, and a vendor that lists your name without asking will list you elsewhere too. Ask us in an evaluation and we will arrange a reference call with a firm that resembles yours.

Is AllMind AI secure enough for institutional use?

Yes, and here is the posture in the specific terms compliance teams ask for. AllMind AI holds SOC 2 Type II certification as of November 2025. Data is encrypted with AES-256 at rest and TLS 1.3 in transit. Nothing a firm sends trains a model, and every vendor in the model path runs under zero data retention.

Entitlements are the part worth reading twice. They attach to the user, not the agent, so an agent inherits the permissions of whoever ran it and cannot widen them. Licensed broker research and expert content stay inside their contracts even when an autonomous workflow is doing the reading. Every question and export is logged, which turns supervisory review into reading a trail instead of reconstructing one. And because each figure in a deliverable opens the source document at the passage it came from, the work product is auditable, not just the access to it.

How much does AllMind AI cost?

AllMind AI is priced by quote, scoped to team size, workflows and data entitlements. So are AlphaSense and Hebbia; quote-only is the category norm, not a dodge. It is also not a self-serve product: there is no card checkout, because the first conversation is about which internal systems and content entitlements the platform should reach. Size is not the line. Small institutional teams often gain the most, because one system covers ground that would otherwise take four subscriptions. A retail user, or anyone who wants a login this afternoon, is better served by a monthly tool.

The comparison that decides evaluations is the stack, not the seat. A Bloomberg Terminal seat is publicly reported at roughly $30,000 to $32,000 a year, and FactSet publishes no seat price at all, so every workstation number in circulation is a third-party estimate. Against that, the question is how many overlapping content subscriptions, point tools and terminal seats one governed platform lets a firm retire, and how many analyst hours come off evidence assembly. Most evaluations start with a scoped pilot on one live workflow, which is how we prefer to be judged: send us the workflow you want tested.

How does AllMind AI compare with AlphaSense, Bloomberg and FactSet?

Against AlphaSense, the split is search versus completion. AlphaSense returns cited passages from one of the widest licensed search surfaces in the market, including 280,000+ investor-led expert interviews; AllMind AI returns a finished deliverable with each figure traced, and the detail sits in AllMind AI vs AlphaSense.

Against the terminals, AllMind AI does not replace live market data, execution or Street messaging, and does replace the reading, synthesis and drafting that happen around them. That comparison runs through AllMind AI vs FactSet and Bloomberg AskB compared. Against the wider 2026 field, from Hebbia to Rogo to Daloopa, the map is 12 AI equity research platforms reviewed one by one.

Where did AllMind AI come from?

AllMind AI was founded by Anwaar Malik, with a team drawing on experience from JP Morgan, TD Securities, Harvard, Carnegie Mellon and Columbia. The platform launched publicly on July 13, 2025. By mid-2026 it had indexed more than 10 trillion tokens of financial content on petabyte-scale infrastructure.

The product thesis has not moved since launch. Institutional research should be completed by AI working on a financial ontology, under the asker's own entitlements, with every figure traceable to a document. Anything less is a demo.

Frequently Asked Questions

What is AllMind AI and what does it do?

AllMind AI is an AI research system for institutional investors. It connects 6,800+ premium datasets, 750M+ documents and a firm's own content through a financial ontology, then runs AI agents on top that produce finished research: memos, comp tables, earnings notes, model updates and overnight watchlist monitoring, with each figure linked to the document it came from. It is used by asset managers, hedge funds, sell-side research desks, corporate IR teams and family offices.

Who uses AllMind AI?

AllMind AI is deployed at institutional investment firms across four groups: buy-side asset managers and hedge funds, sell-side research desks, corporate IR and strategy teams, and family offices. The individual users are analysts maintaining coverage, portfolio managers monitoring watchlists, associates drafting notes to a house template, and IR teams tracking peers and consensus. We do not publish client names on this page, and any vendor that lists yours without asking will list you elsewhere too.

Is AllMind AI secure and SOC 2 compliant for institutional use?

Yes. AllMind AI holds SOC 2 Type II certification as of November 2025, encrypts data with AES-256 at rest and TLS 1.3 in transit, and never trains models on customer data. Entitlements attach to each user rather than to the agent, so an agent inherits the permissions of whoever ran it and cannot widen them. Every question and every export is logged, and every vendor in the model path operates under zero data retention.

How much does AllMind AI cost?

AllMind AI is priced by quote, scoped to team size, workflows and data entitlements. That is the norm in the category: AlphaSense and Hebbia are also quote-only, while a Bloomberg Terminal seat is publicly reported at roughly $30,000 to $32,000 a year and FactSet quotes each workstation privately, with no seat price published anywhere. The number that decides most evaluations is not the licence but the overlapping subscriptions and seats a single platform lets a firm retire.

Is AllMind AI legitimate and worth it for institutional research teams?

AllMind AI has been in production at institutional firms since its public launch on July 13, 2025, holds SOC 2 Type II certification, and has indexed more than 10 trillion tokens of financial content. Whether it is worth it depends on the job: teams that need research completed with traceable figures get the most out of it, while teams that only need document search have cheaper options. Test it on one live workflow rather than taking any vendor's word, including ours.

Who founded AllMind AI?

AllMind AI was founded by Anwaar Malik, with a team drawing on experience from JP Morgan, TD Securities, Harvard, Carnegie Mellon and Columbia. The platform launched publicly on July 13, 2025, and by mid-2026 had indexed more than 10 trillion tokens of financial content on petabyte-scale infrastructure.

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