Best AI for SEC Filing and 10-K Analysis

The best AI for SEC filing and 10-K analysis in 2026 is Kepler, because it reads the whole filing (statements, footnotes, exhibits and XBRL) and lets you click any number back to the line and the version it came from. We read every page of 26M+ SEC filings across 20+ form types before you ask, and build statements from XBRL with no model in the path, as filed or as restated.

This guide covers 10-Ks, 10-Qs, 8-Ks, proxies, S-1s and foreign-issuer filings, for analysts, PMs and diligence teams. It starts with what a tool has to get right on filings, then ranks the tools against it.

What an AI tool has to get right on SEC filings

Summarizing a 10-K is easy for any modern model. The hard parts are the ones an analyst gets blamed for:

• Footnotes and exhibits. Debt maturities, lease commitments, segment notes, contingencies, related-party terms and the credit agreement filed as an exhibit sit far from the headline numbers. A tool that retrieves a handful of passages per question will often miss them.
• Scale and units. Every XBRL fact carries a scale (thousands, millions) and a unit. A figure read at the wrong scale is off by 1,000x and still reads plausibly in a sentence. Our guide to what XBRL is explains the tagging.
• As filed vs as restated. Companies restate prior periods and reclassify lines “to conform to the current period presentation.” FY2023 revenue in the FY2023 10-K and FY2023 revenue in the FY2025 10-K can differ, and each is correct for its date (how to track restatements and reclassifications).
• Text vs XBRL. The HTML statement, the XBRL tag and the figure quoted in MD&A occasionally disagree. A good tool shows the conflict and lets you decide.
• Completeness. “Every company in our coverage that disclosed a material weakness this year” needs every filing in the universe read. A top-ten search result can’t answer it.

The forms that matter most

Form	What’s in it	What to ask AI for
10-K	Annual report: audited statements, footnotes, risk factors, MD&A	Ten-year statements, footnote figures, risk-factor changes year over year
10-Q	Quarterly report: unaudited statements and MD&A	Quarterly segment data, new disclosures, follow-through on guidance
8-K	Material events: results, deals, auditor and executive changes	Earnings releases (Item 2.02), auditor changes (Item 4.01), vote results (Item 5.07)
DEF 14A	Proxy: pay, board, shareholder proposals	Pay versus performance, peer groups, related-party transactions
S-1 / F-1	IPO registration statement	Unit economics, cohorts, use of proceeds, share structure
20-F / 6-K	Annual and current reports for foreign private issuers	The same work for non-US filers, often under IFRS
13F, Forms 3/4/5, 13D/G	Institutional, insider and activist ownership	Who owns what, and who is buying or selling

How we picked

We scored each tool on the five problems above, plus two practical ones: whether it can show a number as originally filed and as restated, and whether a citation lands on the exact line or only on the document. We build Kepler, so it appears first and is marked as ours. Every fact about another vendor links to its source, whether the vendor’s site, a press release or public reporting, as of November 2026.

Quick comparison

Tool	Best for	Statements and XBRL	How it shows sources
Kepler	Complete, provable answers across 20+ form types	Built from XBRL in code; as filed or as restated, on any date	Exact page, line or cell; formula for every calculation
AlphaSense (with BamSEC)	Searching filings next to broker research and expert calls	Financial data and Canalyst models	Exact text snippet
Daloopa	Source-linked historicals for models	Historicals and KPIs, each linked to its document	Every data point links to its source
Fiscal.ai	Low-cost filings research for individual analysts	Standardized financials; segments and KPIs for 2,300 companies	Source table image and PDF (add-on)
Hebbia	The same questions across a peer set’s filings	Extracted per question	Sentence-level (per Hebbia)
Bloomberg ASKB	Terminal users	Bloomberg fundamentals	Linked sources
Claude / ChatGPT for Financial Services	Reading and drafting in a general assistant	Via licensed connectors or included filings	Varies; ChatGPT traces figures to tables and passages
EDGAR full-text search	Free search of filing text	XBRL via free SEC APIs	The filing itself

The 8 best AI tools for SEC filing analysis

1. Kepler (our pick)

We build Kepler.

Best for: analysts, PMs and diligence teams who read filings for a living and need complete answers across every form type, with each number traced to the line and the version it came from.

Filings are where we started. We hold 26M+ SEC filings across more than 20 form types, from 10-Ks, 10-Qs and 8-Ks to proxies, S-1s, 20-Fs, 6-Ks, 13Fs and insider filings, with the full text and the XBRL for each. Extraction models we train read every page at ingest, tables, footnotes and exhibits included, and keep each fact with its value, scale, unit and passage. In our research note Stop Paying to Forget, 99.4% of tagged facts across 40 held-out filings were read at the right scale, the error class (thousands read as millions) that slips past a quick glance.

Financial statements assemble from the filer’s own XBRL in code, with no model in the path. Our paper Reclassified to Conform, also on arXiv, shows why tags alone fall short: 194 of 200 companies reshaped a statement in a way XBRL tags can’t follow, so we rebuild each history from the periods that filings repeat. You can pull any line as filed, as restated or as it stood on any date, and every cell opens to the exact page and line. When a filing’s text and its XBRL disagree, you see both values with a flag.

In Kepler, your firm’s ontology ties it together. Our forward-deployed engineers map each issuer, subsidiary, segment and security to one record alongside your own notes and models, and apply your definitions every time. Agents read new filings for your coverage as they hit EDGAR and alert you with the exact sentence: a new risk factor, a changed revenue recognition policy, an auditor change. The same answers reach Excel, Word, Claude, ChatGPT and your own systems through our API and MCP. Our S-1 breakdowns on our research page, including Anthropic’s October 2026 filing, show the output: every figure in the note links to its passage in the filing. Our engineers built this layer before at Palantir, and they connect your sources and build your ontology on site in the first week.

Why it’s our pick for SEC filings

• 20+ form types, full text and XBRL, read in full before you ask.
• Statements from XBRL with no model in the path, as filed or as restated, on any date.
• Every number opens to its page and line; calculated figures show the formula and each input.
• Complete lists across filings, such as every 8-K reporting a say-on-pay vote below 70% support.
• Conflicts between text and XBRL, or between two filings, are shown and flagged.
• Agents on new filings for your coverage, with alerts that quote the change.
• Your filing-review checklist saved once as a playbook and run on every 10-K in coverage, in your format.
• We route each step to the model best suited to it, with small models we train reading every filing and frontier models answering the question and writing it up.
• Same question, same answer, logged with its lineage under your firm’s permissions. We hold SOC 2 Type II.

Questions analysts ask Kepler about filings

• “Restatements and off-balance-sheet commitments in the last five 10-Ks.”
• “Alphabet’s income statement for ten years, as originally filed and as restated, side by side.”
• “Which companies in our coverage changed auditors or added a going-concern paragraph this year?”

“I can’t put a number in front of a client if I can’t show where it came from.” (Managing Director, Multi-billion dollar fund)

Not the right fit if: you only look up the occasional filing, which EDGAR’s free search handles well, or you’re reading 10-Ks for a personal portfolio.

Open a 10-K you know well at app.kepler.ai and click any number, or book a demo.

2. AlphaSense (with BamSEC)

Best for: searching filings alongside broker research, expert calls and news.

AlphaSense searches filings in the same index as transcripts, broker research and its library of more than 300,000 expert interviews, and Generative Search cites the exact snippet behind each answer. The 2024 Tegus acquisition brought two filings-adjacent products. BamSEC, a filings and transcripts reader, still runs as its own product, with a free tier and BamSEC Pro published at $69 a month billed annually. Canalyst Models, now sold inside AlphaSense, are pre-built, drivable models, more than 4,000 of them. Work Products (July 2026) pull financials, filings and comps into Excel and PowerPoint with source links.

For filings work: fast search across every form next to qualitative content that filing-only tools don’t carry, and BamSEC for an affordable reader.

Trade-offs: its AI works over its library and the documents you connect, while Kepler adds your firm’s ontology and builds every figure from the filing in code, cited to the line. AlphaSense doesn’t publish prices for the full platform; SpendHound reports average spend of $10,131 a year for smaller customers and $139,455 for enterprise customers.

What users say: AlphaSense is “really hit and miss depending on the sector,” a hedge fund analyst wrote on Wall Street Oasis in 2025.

3. Daloopa

Best for: analysts who want filing data already in model form, with each point linked to its source.

Daloopa extracts fundamentals, segment data, KPIs and guidance from filings and decks for 6,000+ listed companies, as far back as 14 years, and ties every figure to the document behind it. An MCP connector carries the data into Claude, ChatGPT, Microsoft 365 Copilot, Gemini Enterprise and Perplexity, and Scout, its Excel agent, works on the data inside the workbook.

For filings work: the historicals a model needs, auditable cell by cell.

Trade-offs: it covers the numbers it extracts, so footnote language, risk factors, proxies and ownership filings need another tool. See Daloopa alternatives for options.

4. Fiscal.ai

Best for: individual analysts and small teams who want standardized financials and filings on a budget.

Fiscal.ai, formerly FinChat, is a public-company research terminal with standardized financials going back 20+ years, filings, transcripts and an AI copilot. Segment and KPI data cover the largest 2,300 companies globally, and a Sourcing to Filing add-on links each financials and ratios data point to an image and PDF of the source table. Its API and MCP server work in Claude, ChatGPT, Gemini Enterprise and Microsoft 365 Copilot, and its data is included in ChatGPT for Financial Services. Terminal Pro was reported at $39 a month, paid annually, in October 2026.

For filings work: quick, cited answers and clean financials for a single analyst.

Trade-offs: built for individuals, so there’s no deployment on your firm’s own documents or definitions.

5. Hebbia

Best for: asking one set of questions across a whole peer set’s filings.

Hebbia’s Matrix lines up filings as rows against questions as columns, and agents answer each cell with sentence-level citations (per Hebbia). It covers filings from the US, Europe, the UK and Australia and New Zealand plus transcripts, and added Canadian SEDAR+ filings in August 2026. Matrix 2.0 (August 2026) produces memos, decks and models, and Headless Hebbia, an API and MCP server, followed in October.

For filings work: comparing disclosures across many filers at once, such as revenue recognition policies across a sector.

Trade-offs: the model reads each cell’s documents question by question, so figures are extracted on each run, and pricing isn’t published.

6. Bloomberg Terminal with ASKB

Best for: Terminal users who want filings in the same conversation as market data.

Bloomberg made ASKB generally available on October 15, 2026. Its agents work over Bloomberg’s data, news, Bloomberg Intelligence and outside research, and return the BQL behind an answer for use in Excel. Bloomberg’s fundamentals already standardize filings data for the Terminal.

For filings work: filings next to prices, estimates and news, inside the tool a desk already uses.

Trade-offs: priced per Terminal (a single Terminal was reported at $31,980 a year for two-year contracts starting in 2025).

What users say: “Opening pages is not very intuitive: there is no real ‘back’ button.” (a financial analyst reviewing the Terminal on Capterra, February 2026)

7. Claude for Financial Services and ChatGPT for Financial Services

Best for: reading, summarizing and drafting from filings in an assistant the whole firm already uses.

OpenAI’s ChatGPT for Financial Services, launched September 10, 2026 as a separate plan for eligible firms, includes public company filings, Quartr transcripts and international filings, Fiscal.ai and Daloopa data, and OpenAI says its citations trace figures to specific tables and passages. Anthropic’s Claude for Financial Services reaches filing and fundamentals data from FactSet, S&P Capital IQ, Daloopa, Morningstar, LSEG, Moody’s and others through your own licenses, and Claude for Excel, PowerPoint and Word became generally available in May 2026.

For filings work: a capable reader for a single filing or a short set.

Trade-offs: used on their own, both models compose the numbers in each answer and know your definitions only from what you connect. Kepler runs inside both. Our guide Can ChatGPT do financial analysis? walks through where general assistants hold up and where they break.

8. SEC EDGAR full-text search (free baseline)

Best for: anyone who needs to find a phrase across filings without paying for a tool.

The SEC’s EDGAR full-text search covers every filing submitted electronically since 2001, exhibits included. All terms are required by default, and it supports exact phrases, OR, NOT and NEAR operators, trailing wildcards on word stems, and filters for form type, company or person, location and date. The SEC also publishes XBRL company facts and frames through free APIs on data.sec.gov that need no key and usually update within a minute of a filing.

For filings work: the first place to check whether a phrase appears anywhere in the record.

Trade-offs: it finds documents. Reading, extracting and computing are on you.

How to test any AI tool on a 10-K

Run these on a company you know well before you trust a tool with live work. Our guide on how to check AI-generated numbers has a longer checklist, and how to analyze a 10-K with AI covers the sections worth reading and the questions to ask of each.

1. Ask for a footnote figure. Operating lease payments due in year three, or the interest rate on a specific note. Check that the citation lands on the footnote table.
2. Ask for a restated year. Request revenue for a year that was later reclassified, both as originally filed and as restated.
3. Set a scale trap. Ask for a figure reported in thousands by one company and in millions by its peer, and compare.
4. Ask for a complete list. “Every 8-K in this sector reporting a material cybersecurity incident under Item 1.05.” Spot-check for misses with EDGAR full-text search.
5. Ask twice. The same question should return the same numbers with the same sources.
6. Click ten citations. Check figure, period, unit and formula each time.

For model building on top of filings, see our best AI tools for equity research.

Kepler is live at app.kepler.ai. Open a filing, click any number and see the line it came from, or book a demo to see it on your coverage.

Frequently asked questions

Can AI read a 10-K accurately?

Kepler can: extraction models we train read every page of the 10-K at ingest, code builds the statements from the filing's XBRL with no model in the path, and every number links to the page and line it came from. A general assistant reads a few retrieved passages and writes the figures itself, so it can miss footnotes, misread scale or mix restated and original periods.

What is the best free tool for searching SEC filings?

SEC EDGAR full-text search. It is free, searches the text of electronic filings from 2001 onward, supports exact phrases and OR, NOT and NEAR operators, and filters by form type, company and date. The SEC also publishes XBRL data through free APIs on data.sec.gov. Neither one analyzes or computes anything for you, which is where AI tools come in.

What is XBRL, and why does it matter for AI analysis of filings?

XBRL is the machine-readable tagging the SEC requires on financial statements, so each reported number carries a tag, a period, a unit and a scale. Building statements from XBRL in code avoids the model retyping numbers. Tags alone aren't enough, though: companies rename, merge and reclassify lines over time, and a tool has to follow those changes to give a consistent history.

What does 'reclassified to conform to the current period presentation' mean?

It means the company changed how it presents a line or segment and restated prior periods to match. The same year's figure can then differ between an older filing and a newer one, and both are correct for their date. Our paper Reclassified to Conform found that 194 of 200 companies reshaped a statement this way, which is why we let you pull any line in Kepler as filed or as restated.

Can ChatGPT or Claude analyze SEC filings?

Yes, for reading and summarizing. ChatGPT for Financial Services includes SEC filings, and Claude for Financial Services connects to licensed filing and fundamentals data. Both are frontier models on their own, so the model still writes each number and learns your firm's definitions only from what you provide. Kepler runs inside both, returning numbers computed from the filing and cited to the line.