Sample report · redacted
This is a real report from September 28, 2026, for a seed-stage financial-data company we approached cold. Its name, domain, founder and product names are replaced. Every question, number, competitor and quotation is as measured.
You get one of these for your own category, free, whether or not we end up working together. It is five pages as a PDF; this page is the whole of it. What it is not: a score, a forecast, or a promise that anything will move.
how the numbers are made → Methodology · the same test on us → Lab
We ran all 20 questions on September 28, 2026, once with Claude Opus 5 and once with OpenAI GPT-6 Sol (via Codex), both with web search on. That produced 40 live answers and 292 citations. The site and third-party checks were done the same day. Nothing in this report is modeled.
This is a one-day snapshot; Gemini, ChatGPT and Perplexity were not tested. A † marks a question where at least one answer did not search the web. GPT-6 Sol searched on every question except the bare brand name; Claude searched on 12 of the 17 questions that leave out the company’s name. Claude’s citations come from the source list at the end of each answer, GPT-6 Sol’s from links inside its answers.
Company A was recommended on 5 of 20 questions. On five questions that each describe a product it sells (SEC filings with standardized statements, delisted fundamentals, operating KPIs, pay-as-you-go credits and real-time filing alerts), none of the 10 answers named it.
The most frequent recommendation was not a vendor. On 12 of the 17 questions without the company’s name, the answers sent buyers to the SEC’s free EDGAR API.
| Company A | Financial Modeling Prep | sec-api.io | SEC EDGAR (free) | |
|---|---|---|---|---|
| Recommendation rate | 25% (5 / 20) | 40% (8 / 20) | 30% (6 / 20) | 60% (12 / 20) |
| Mention rate | 45% (9 / 20) | 65% (13 / 20) | 30% (6 / 20) | 75% (15 / 20) |
| Non-brand questions named | 6 / 17 | 12 / 17 | 6 / 17 | 13 / 17 |
| Own domain cited, non-brand | 8 of 269 citations | 24 | 13 | 26 |
Company A starts with real assets. Asked its own homepage line, how to connect an agent to the stock market, Claude recommended it first, from memory. GPT-6 Sol picked it first for clean JSON with a link to the source filing, and quoted its pricing page exactly. Both engines tied the founder’s two repositories to the company, and both called it legitimate. The site lets every major AI crawler in and serves llms.txt, markdown copies of each page and an OpenAPI spec.
Each question received two answers. The questions follow the homepage, pricing page, docs, blog and Y Combinator launch post.
| # | Question | Company A | Who gets recommended |
|---|---|---|---|
| 1 | Best stock market data API for AI agents? | Absent | Polygon / Massive (both), FMP, Alpha Vantage |
| 2 | Which providers have an official MCP server for Claude or ChatGPT? | Claude: recommended. GPT-6 Sol: listed, 4th of 7 | Alpha Vantage, FMP, Massive, EODHD; FactSet, LSEG for funds |
| 3 | Best API for SEC filings and standardized statements in an LLM app? | Absent | SEC EDGAR, sec-api.io, Intrinio, FMP |
| 4 † | Stock API with fundamentals for delisted companies, for backtests? | Absent | Sharadar, first in both |
| 5 | APIs for operating KPIs and management guidance? | Absent | Daloopa, LSEG, FactSet, Visible Alpha, Bloomberg |
| 6 | Pay-as-you-go stock data API for a solo developer? | Absent | Databento, first in both |
| 7 | Alerts within seconds of an 8-K, Form 4 or 13D filing? | Absent | sec-api.io, in both; SEC feeds |
Questions 3 to 7 each match a product Company A sells; none of the 10 answers named it. Its site says its data is “survivorship-bias-free for backtesting,” lists KPI, guidance and non-GAAP endpoints, processes filings “within seconds of publication,” and sells $20 of “Pay-as-you-go” credits.
Question 6 went to its own supplier. Both engines picked Databento; the company’s markdown homepage says its stock prices come from Databento.
| # | Question | Company A | Who gets recommended |
|---|---|---|---|
| 8 † | My agent needs prices, statements and SEC filings. Easiest way to connect it to the stock market? | Claude: recommended first. GPT-6 Sol: absent | GPT-6 Sol: FMP, plus SEC EDGAR |
| 9 | Bloomberg and FactSet cost too much. Clean JSON with a link to the source filing? | GPT-6 Sol: recommended first. Claude: absent | Claude: SEC EDGAR, sec-api.io, DataCedar, EvidInvest, FMP |
| 10 † | My agent gets revenue and EPS wrong. Fundamentals checked against SEC filings? | Absent | SEC EDGAR Company Facts, in both |
| 11 | [The founder’s two open-source projects] ask for a data key. Which provider, and a cheaper option? | Recommended in both | Company A; forks using Finnhub and Alpaca |
| 12 | Consumer investing app: which APIs allow redistribution without an enterprise contract? | GPT-6 Sol: recommended (Scale, $2,000/month). Claude: absent | Databento, SEC EDGAR, Intrinio, Twelve Data |
Questions 8, 9 and 10 restate the company’s own pitch. Each engine named it once, never on the same question. On question 8, Claude answered from memory and called it “purpose-built for this exact trio.” GPT-6 Sol searched and chose FMP. On question 9, GPT-6 Sol said “For a startup agent, I’d start with [Company A]”; Claude named DataCedar and EvidInvest, which “explicitly store filing_date + accession + source_url per normalized row.” On question 10, both pointed to the SEC’s free Company Facts API.
Question 11 names the founder’s repositories, and both engines tied them to the company.
| # | Question | Company A | Who gets recommended |
|---|---|---|---|
| 13 | FMP vs Polygon vs Alpha Vantage for fundamentals and SEC filings? | Absent | FMP, first in both; Polygon / Massive |
| 14 † | Parse EDGAR with edgartools, or pay for a data API? | Absent | "Start with edgartools and SEC EDGAR" (GPT-6 Sol); Claude the same |
| 15 † | Cheaper alternatives to Bloomberg, FactSet or Capital IQ for a small fund? | Absent | Polygon, Sharadar, FMP, sec-api.io, EDGAR, Koyfin |
| 16 | Equibles vs EODHD vs FMP: which MCP server works best with Claude? | Absent | Claude: EODHD. GPT-6 Sol: Equibles |
Question 15 is the launch post’s own framing: “Bloomberg, FactSet, and S&P spent 30 years building market data for humans staring at terminals.” Claude named more than 20 alternatives, GPT-6 Sol six. Neither included Company A.
| # | Question | Company A | Who gets recommended |
|---|---|---|---|
| 17 | Most accurate LLM for extracting KPIs from earnings releases? Is there a benchmark? | Claude: [its own benchmark] cited. GPT-6 Sol: absent | GPT-6 Sol: RedCrown, HiFi-KPI, FinanceBench |
The two engines gave opposite answers to question 17. Claude: “there is one benchmark that targets exactly this task,” and named the company’s own. GPT-6 Sol: “I found no public benchmark that compares Claude Opus, Claude Sonnet, and GPT head to head on that exact task.”
| # | Question | Company A | Who gets recommended |
|---|---|---|---|
| 18 † | "Company A" (the bare name) | Claude: correct company, listed first. GPT-6 Sol: read as a generic phrase | GPT-6 Sol asked what the user wanted to do with the phrase |
| 19 | Company A reviews: is it legit? | Legit, 2 of 2 | Both: few independent reviews |
| 20 | How much does the Company A API cost? | Priced, 2 of 2 | GPT-6 Sol matches the pricing page; Claude adds a free tier and old per-request rates |
With “reviews” or “API” added, both engines found the right company. Claude called the founder’s repositories “the strongest single signal here.” No answer confused it with the founder’s 2024 Python library of the same name.
| Brand | Recommended on | What the answers drew on |
|---|---|---|
| SEC EDGAR APIs (free) | 12 / 20 | sec.gov documentation, 26 citations |
| Financial Modeling Prep | 8 / 20 | Its developer docs, 24 citations; the MCP page alone 6 |
| Polygon.io / Massive | 8 / 20 | massive.com docs, 10 citations |
| sec-api.io | 6 / 20 | Its docs, 13 citations; won both filing-alert answers |
| Company A | 5 / 20 | Its pricing page; the founder’s repositories on question 11 |
FMP, Polygon / Massive, Alpha Vantage and EODHD are Tier 1. The SEC’s free API, which Company A itself parses, was recommended more often than any of them.
| MCP server on GitHub | Stars, Sep 28 | Answers recommending it, questions 1, 2 and 16 (of 6) |
|---|---|---|
| Company A’s MCP server | 2,300 | 1 (Claude, question 2) |
| massive-com/mcp_massive | 392 | 2 |
| daniel3303/Equibles | 231 | 2 |
| alphavantage/alpha_vantage_mcp | 212 | 3 |
| EodHistoricalData/EODHD-MCP-Server | 21 | 3 |
The sharpest case is question 1, which nearly repeats the homepage title. Both answers recommended Massive, FMP and Alpha Vantage, not Company A, whose repository has more stars than the Massive, Alpha Vantage and EODHD servers combined. That repository was last updated June 5, 2025.
Equibles is the closest Tier 2 company: an MCP-first API for filings, KPIs and ownership. It publishes a “[Company A] Alternative: Equibles Compared” page, which Claude cited on question 19.
We could read 19 of the 23 “best of” and comparison pages cited on non-brand questions. 16 were published by vendors; 8 rank the publisher first. Three mention Company A, all written by competitors: one ranks it first by GitHub stars, one seventh, and one lists it at “Free–$49/mo,” a price that is not on its site. Company A has two blog posts and no comparison page.
The 34 answers to the 17 non-brand questions carried 269 citations, 185 from Claude and 84 from GPT-6 Sol.
| Source type | Share | Examples in the answers |
|---|---|---|
| Vendor sites, docs and vendor-written lists | 62.1% | FMP, sec-api.io, EODHD, Lambda Finance |
| SEC, exchanges, government | 11.9% | sec.gov |
| Other GitHub repos, press, blogs, directories, research | 19.3% | Community MCP servers, Medium, arXiv |
| Company A and the founder’s repositories | 6.7% | Pricing page, the two repositories |
| Source | Controllability | Non-brand | Brand | Status |
|---|---|---|---|---|
| companya.example and its docs | Highest | 8 | 7 | Pricing page cited 5 times; none of the 57 docs pages on non-brand questions |
| The founder’s two repositories | Highest | 7, all on question 11 | 2 | About 90,000 stars between them; one README line each |
| Company A’s MCP server | Highest | 1 | 0 | Last updated June 5, 2025 |
| Directory listings, YC, founder’s LinkedIn, partner docs | High | 2 | 5 | PulseMCP and ChatGPT listings on question 2; the rest only on question 19 |
| G2, Trustpilot, Capterra, Product Hunt | High | 0 | 0 | All blocked direct checks |
Owned source share: 6.7% (18 of 269 non-brand citations). Without question 11, which names the founder’s repositories, it is 3.5% (9 of 255). On the three brand questions it was 61% (14 of 23).
GPT-6 Sol’s price answer matched the pricing page line for line, and it read the terms correctly: redistribution only on the Scale plan. Claude gave the coverage as “27,000+ active and delisted US tickers”; no answer repeated the “global” claim on the pricing FAQ.
| Claim | Where | What Company A’s own files say |
|---|---|---|
| A "$0" free tier | Question 20 | Its skill.md: "There is no free tier"; every page’s JSON-LD gives a price of 0 |
| Per-endpoint prices, $0.01 to $0.10 | Question 20 | Match the rate card in its llms-full.txt; the pricing page shows only plans |
| Five tickers "with no key at all" | Question 11 | Keyless requests return 401 |
| "options, crypto" among its datasets | Question 18 | Docs: options "are not yet available"; the API index still lists crypto |
The site’s machine-readable files disagree with each other. The pricing page lists Scale at $2,000 a month; its markdown copy calls it a “custom price”; llms-full.txt still lists two plans that no longer exist.
“Trusted by 1,000+ customers” appears in the YC launch post, not on the company’s own site. The review pages the answers found were an aggregator, a competitor’s comparison and a blog post by a friend of the founder. Claude: “There is no real independent review corpus.”
Buyers in this test asked for delisted fundamentals, operating KPIs, pay-as-you-go access and filing alerts. Company A sells each of these.
Leave your work email and website and we run your buyer questions the same way. It takes us a day, it is yours to keep, and if the answer is that you are named everywhere already, the report says that.
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