PAIN TRIGGERS
LLMs hallucinate when processing long financial documents like 10-Ks, leading to unreliable outputs.
Existing financial data APIs provide only numerical data, lacking deeper qualitative insights into business models and risks.
SaaS· developers building investing toolsPain 7.00/10WTP 7.0/10Market 6.0/10Validation 8.0Confidence 85%Apr 23, 2026
FilingFacts: Structured Qualitative SEC Data API for AI Tools
LLMs struggle to interpret lengthy SEC filings like 10-Ks without hallucinating, and existing financial data APIs lack structured qualitative insights, leaving developers and investors unable to build reliable AI tools or obtain defensible data.
ai-poweredanalyticsapiautomationdata-managementdevelopersfinancefintechsaas
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STAGE 01 · PROBLEM
Is the problem real?
CANONICAL PROBLEM
LLMs struggle to accurately read and interpret lengthy SEC filings like 10-Ks without hallucinating or inventing data, making it difficult for developers and investors to obtain reliable qualitative insights.
FREQUENCY
Multiple repeated complaints in the post and comments.
INTENSITY
Users explicitly describe existing tools as bloated/overkill and mention workaround behavior.
EVIDENCE
LLMs can't read 300-page 10-Ks without hallucinating. I built an API that does it, and cites the filing on every claim.
SaaS13
LLMs can't read 300-page 10-Ks without hallucinating. I built an API that does it, and cites the filing on every claim.
SaaS13
LLMs can't read 300-page 10-Ks without hallucinating. I built an API that does it, and cites the filing on every claim.
SaaS13
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STAGE 02 · CUSTOMER
Who feels this pain?
TARGET USERS
developers building investing toolsFin Tech A I Developers
Software developers and data scientists creating AI-driven financial tools or research assistants that require reliable, structured qualitative data from SEC filings.
Context
Obtain structured, verifiable, and LLM-consumable qualitative data from SEC filings to build investing tools, research assistants, or screeners with defensible claims grounded in source documents.
Relying on manual analysis by analysts who read 10-Ks, which does not scale.
Using expensive institutional tools like Bloomberg and FactSet for qualitative data, despite pricing barriers.
Current Workarounds
Manually parsing 10-Ks to extract qualitative insights for training or validation
Using expensive institutional tools like Bloomberg for limited qualitative data
Relying on LLM outputs despite known hallucination risks
Scraping unstructured data from filings without verifiable citations
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STAGE 03 · MARKET
Where's the gap?
EXISTING SOLUTION GAPS
Bloomberg and FactSet offer qualitative fields but are priced for institutions and lack per-claim citations or LLM-consumable JSON.
Sell-side reports are paywalled, slow, and limited to one company at a time.
Retail tools like SimplyWall provide dashboards but lack queryable structure.
Polygon, FMP, EODHD, and Intrinio focus on numerical data without structural interpretation.
LLM-only approaches lack source grounding, leading to hallucinations.
OPPORTUNITY & VALUE
Why Now
Multiple complaints about LLM hallucinations on long filings and the lack of qualitative data in financial APIs.
Value Proposition
Unlike numerical-only financial APIs or expensive institutional tools, FilingFacts offers affordable, structured qualitative SEC data with verifiable citations tailored for AI tool development.
Product Direction
An API that extracts structured, verifiable qualitative data from SEC filings (e.g., business models, risks, strategies) in LLM-consumable JSON format, with per-claim citations grounded in source documents.
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STAGE 04 · BUSINESS
How does it make money?
MONETIZATION
$99/moUp to 1,000 API calls · per developer or team
Model
SaaS subscription
WILLINGNESS TO PAY
Developers currently pay for numerical data APIs or expensive tools like Bloomberg despite gaps in qualitative insights; the repeated complaint about hallucinations suggests they’d pay for a reliable, structured alternative as evidenced by posts highlighting the gap in current offerings.
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STAGE 05 · EXECUTION
How do you ship it?
MVP PLAN
“Build AI investing tools with grounded SEC insights in 6 weeks.”
An API that extracts structured, verifiable qualitative data from SEC filings (e.g., business models, risks, strategies) in LLM-consumable JSON format, with per-claim citations grounded in source documents.
Core Features
Extracts qualitative data (business models, risks) from 10-Ks as structured JSON
Provides per-claim citations linking to verbatim filing text
API access for developers to query specific companies or themes
Basic hallucination guardrails via source grounding
Weekly Roadmap
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W1-W2
Core extraction engine processes 10-Ks into structured JSON for 10 companies.
- •Build parser for SEC 10-K filings using EDGAR API
- •Extract key qualitative sections (business description, risks)
- •Output structured JSON with basic citation links
2
W3-W4
API supports querying qualitative data for 50 companies with developer docs.
- •Develop RESTful API for querying company-specific qualitative data
- •Add per-claim citation mapping to source text
- •Create initial developer documentation and SDK
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W5
Beta testing with 10 developers ensures data accuracy and usability.
- •Implement basic error handling and hallucination guardrails
- •Recruit 10 FinTech developers for beta feedback
- •Iterate on API based on early user testing
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W6
Public launch with free tier and first paying developer customers.
- •Set up Stripe for subscription billing at $99/mo
- •Launch on r/algotrading and Hacker News with free tier
- •Publish case study of beta developer use case
Launch Strategy
Target developer communities on Reddit (r/algotrading, r/fintech) and Hacker News with a free tier for initial API access, alongside content marketing on building AI financial tools with grounded data.
RISKS & ASSUMPTIONS
Top Risks
Accuracy of qualitative data extraction
Ensuring precise extraction of qualitative insights from varied SEC filing formats may be challenging and could lead to errors or incomplete data.
Developer adoption barrier
Developers may stick to familiar numerical APIs if qualitative data is perceived as a secondary need or too niche for their use case.
Regulatory compliance on data usage
Redistributing SEC filing data, even in structured form, may face legal scrutiny or require specific licensing agreements.
Competition from institutional tools
Established players like Bloomberg may expand into developer-friendly APIs, reducing the unique value proposition.
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STAGE 06 · DECISION
Should you build it?
NEED A CLEARER CALL?
Run an Investment Memo to get a structured Go / No-Go verdict, competitor landscape, unit economics, and a 90-day validation roadmap for this opportunity.
Generate an investment memoWhat this score means
This idea scores in the upper-middle range of opportunities surfaced by MonetScope, with a validation sub-score of 8/10 against 3 independently sourced evidence signals. A "promising" rating usually indicates a real pain has been detected and discussed in the open, but the pipeline did not find enough signal to flag it as urgent or high-frequency. These opportunities can still produce excellent businesses — they often correspond to "boring" problems that established players have ignored — but the founder should expect a longer customer-development cycle to confirm willingness to pay.
Why this matters for SaaS founders
It sits at the intersection of "ai-powered", "analytics", "api", which makes it relevant to a specific subset of founders rather than a generic horizontal opportunity. SaaS opportunities at this stage tend to win on the strength of their initial wedge — a single workflow that the target user runs every week, where the existing solution is either spreadsheets, a clunky incumbent feature, or a manual process they hate. The build cost is moderate; the distribution cost is everything. The MonetScope pipeline surfaces this category alongside other saas signals, which is why it appears here rather than in a generic "trending ideas" feed.
Scores are derived from real forum discussions across Reddit, Hacker News and X, weighted by evidence volume and signal quality. How scoring works
Frequently asked questions
Is "FilingFacts: Structured Qualitative SEC Data API for AI Tools" a real validated startup idea or just an AI-generated suggestion?
MonetScope does not generate ideas from a language model's imagination. Every opportunity on this site is anchored to specific source posts and comments from real public discussions — typically on Reddit, Hacker News, or X — where actual users describe the pain in their own words. The AI's role is structuring, scoring, and grouping those signals into a navigable opportunity, not inventing the problem.
How recent is the underlying data for ai-powered?
MonetScope's spider pipeline runs continuously and surfaces opportunities as new evidence accumulates. The "Updated" date in the header reflects the most recent re-scoring of this specific opportunity. Most saas opportunities visible in the public catalog draw from discussions in the last 30-60 days; older signals are de-prioritized because user pain shifts faster than most founders assume.
What's the difference between "overall score" and "validation score"?
Overall score is a composite across six dimensions — pain, urgency, willingness to pay, market size, defensibility, and execution ease — designed to give a single number for triage. Validation score is narrower: it asks "how cleanly does the same signal repeat across independent sources?" An opportunity can score high on overall but lower on validation when one or two large discussions dominate the evidence; conversely, validation can be high on a smaller-overall idea where the signal is consistent but the addressable market is modest.