AI financial research you can check
Ask about a company, a filing or a market in plain English. The answer comes back with the passages it came from, numbered, so verifying a claim is a click rather than a search. Language models make mistakes, and this is designed around that fact instead of away from it.
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How does AI financial research actually work?
The useful version is retrieval first, generation second. The system finds the relevant passages in filings, transcripts and documents, ranks them, and gives the model only that material to answer from, with instructions to cite what it used. The model is doing reading and summarising rather than recalling. That is what makes an answer checkable, and checkability is the whole difference between a research tool and a plausible-sounding one.
The specifics
Answers with numbered sources
Every factual claim carries a citation, and the citation opens the passage. The numbers in the answer and the numbers on the source cards come from one registry, so they always line up.
It reads the actual filing
Questions about a company pull the relevant sections of the real document rather than answering from training data, which is where a model's confidence and its accuracy diverge most.
Your own documents, searchable by meaning
Upload research, notes and reports and they become part of what can be retrieved and cited, findable months later from any screen.
More on thisA second opinion from a different model
Ask two independent providers the same structured question and compare the answers arithmetically rather than asking a third model whether they agree, since a model asked that question will almost always say yes.
Bull and bear, argued separately
The two sides constructed as distinct cases rather than as a balanced paragraph that commits to nothing.
Autonomous research runs
A question planned into sub-questions, researched across sources, and written up with citations, run on a schedule if you want it to be.
How the work actually goes
- 01
Ask in your own words
No query syntax, no function codes. The question you would ask a colleague is the question that works.
- 02
Watch it retrieve
Sources appear as they are found, so you can see what the answer is going to be based on before you read it.
- 03
Check the citation
Click a numbered reference and read the passage. If a claim is not supported by what it cites, you find out in seconds.
- 04
Keep it
Save the exchange into your library, where it is searchable later alongside everything else you have written.
The model can be wrong, and it is treated that way
Language models make mistakes and anyone claiming otherwise is selling something. Citations exist because of that, not in spite of it. Text from outside the system, including uploaded documents, filings and news headlines, is treated as untrusted input: it is fenced off from the instructions, scanned for attempts to steer the answer, and output is screened before it reaches you. None of that makes the model correct. It makes being wrong visible, which is the property that actually matters.
Frequently asked questions
What is AI financial research?
It is using a language model to read financial documents and answer questions about them. The useful version retrieves the relevant passages first and asks the model to answer only from those, citing what it used. The unreliable version asks the model from memory, which produces fluent answers that cannot be checked and are sometimes wrong in ways that look exactly like being right.
Can AI analyse a company's filings?
Yes, and this is the case it is genuinely good at: finding the relevant passage across hundreds of pages, comparing this year's language against last year's, and summarising what changed. It is much weaker at arithmetic and at judgement, which is why the numbers here come from deterministic engines reading filed data rather than from the model.
How do I know an answer is accurate?
Read the citation. Every factual claim carries a numbered reference that opens the passage it came from. That takes a few seconds and is the only verification method that actually works. A confidence score would be easier to display and far less useful.
Is my data used to train models?
No. Documents you upload are stored against your account, used to answer your questions, and deleted when you delete them, including from the search index. The privacy policy sets out which processors are involved and what each receives.
Which models does it use?
Several, ranked by measured health and routed per question, with automatic failover when one is unavailable. The specific provider matters less than the structure around it: retrieval, citation, and screening of what comes back.
Ask it something you already know the answer to
It is the fastest way to find out whether an AI research tool is worth trusting with something you do not.