ReportMine — Financial Table Extractor for Annual Reports That Never Live on the Same Page
Independent research analysts spend hours hunting financial tables buried in 200-page PDFs where the income statement is on page 47 one year and page 112 the next. ReportMine uses Claude's document vision to find, extract, and structure every financial table across 1,000+ reports automatically. Your Bloomberg terminal costs $24k/year — this costs $299/month.
Difficulty
intermediate
Category
Finance
Market Demand
High
Revenue Score
8/10
Platform
Web App
Vibe Code Friendly
No
Hackathon Score
🏆 7/10
Validated by Real Pain
— sourced from real community discussions
Analyst built a custom pipeline to extract financial tables from 1,000+ annual reports where the table location changes every year — currently the only options are expensive enterprise vendors or painful manual scripts.
What is it?
The real pain is not parsing one PDF, it is building a reliable pipeline across hundreds of annual reports where nothing is standardized. FactSet and Bloomberg solve this but charge enterprise prices that exclude boutique RIAs, independent analysts, and small hedge funds. ReportMine accepts bulk PDF uploads or SEC EDGAR links, runs Claude's vision API to locate and extract financial tables regardless of page position, and outputs clean JSON or CSV with confidence scores. Analysts get structured income statements, balance sheets, and cash flow tables in minutes instead of days. Why buildable right now: Claude's document vision handles multi-page PDF context reliably as of mid-2026, and the SEC EDGAR API is free and stable. Precedent exists with AnnualParse in the idea archive but this goes 10x deeper with bulk pipeline, structured schema mapping, and a confidence audit layer.
Why now?
Claude's multi-page document vision became reliable enough for financial PDF extraction in early 2026, and SEC EDGAR's free API makes bulk ingestion trivially cheap — the cost barrier that kept this as an enterprise-only product is gone.
- ▸Bulk PDF upload or SEC EDGAR URL fetch with queue-based processing (Claude vision per document)
- ▸AI table locator that finds income statements, balance sheets, and cash flow tables regardless of page position
- ▸Structured CSV and JSON export with confidence score per extracted cell
- ▸Audit log showing which page each table was found on for analyst verification
Target Audience
Independent equity research analysts and boutique investment firms — roughly 15,000 in the US alone — who cannot afford FactSet or Bloomberg terminal pricing.
Example Use Case
Sarah, a solo equity analyst covering 40 mid-cap stocks, uploads 200 annual reports on Sunday night and has clean, structured financial tables waiting in her spreadsheet by Monday morning — saving 12 hours per week.
User Stories
- ▸As an independent equity analyst, I want to upload 50 annual report PDFs and receive structured financial tables as CSV, so that I can build my models in hours instead of days.
- ▸As a boutique RIA researcher, I want confidence scores on each extracted cell, so that I know exactly which figures need manual verification before I trust the data.
- ▸As a solo analyst, I want to paste an SEC EDGAR URL and have the report auto-fetched and extracted, so that I never have to manually download PDFs again.
Done When
- ✓Upload: done when user drags 5 PDFs onto the upload zone and sees 5 jobs appear in the queue within 10 seconds.
- ✓Extraction: done when a completed job shows a table preview with income statement rows correctly labeled and page source visible.
- ✓Export: done when clicking Download CSV produces a file that opens in Excel with headers and numeric values in the correct cells.
- ✓Billing: done when user hits the 10-PDF trial cap, sees an upgrade prompt, clicks it, completes Stripe checkout, and immediately resumes processing.
Is it worth building?
$299/month x 20 customers = $5,980 MRR at month 3. $599/month x 50 customers = $29,950 MRR at month 8. Math assumes cold outreach to analyst communities at 8% conversion.
Unit Economics
CAC: $80 via direct community outreach. LTV: $3,588 (12 months at $299/month). Payback: 0.3 months. Gross margin: 82%.
Business Model
SaaS subscription
Monetization Path
14-day free trial with 10 PDF cap, then $299/month Analyst or $599/month Firm tier with team seats.
Revenue Timeline
First dollar: week 3 via pre-sell beta. $1k MRR: month 2. $5k MRR: month 4. $15k MRR: month 10.
Estimated Monthly Cost
Claude API: $80 at 500 reports/month, Vercel: $20, Supabase: $25, Cloudflare R2: $10, Stripe fees: $15. Total: $150/month at launch.
Profit Potential
Full-time viable at $8k-$20k MRR with 15-35 firm customers.
Scalability
High — add EDGAR auto-fetch, XBRL parsing fallback, and team collaboration to move upmarket.
Success Metrics
Week 2: 3 beta analysts using it. Month 1: 5 paying customers. Month 3: $5k MRR. Retention above 85% at 90 days.
Launch & Validation Plan
Post a Loom of the tool extracting 10 annual reports in 2 minutes to r/SecurityAnalysis and FinancialModelingWorld Discord — collect 20 email signups before writing a line of code.
Customer Acquisition Strategy
First customer: DM 30 analysts on r/SecurityAnalysis and FinancialModelingWorld Discord offering 3 months free for weekly feedback. Ongoing: LinkedIn content targeting CFA charterholders, SEO targeting 'annual report financial data extraction', ProductHunt launch.
What's the competition?
Competition Level
Medium
Similar Products
FactSet (charges $50k+/year, enterprise only), Intrinio (coverage gaps for smaller companies), Docparser (generic, not finance-aware) — none combine bulk pipeline with AI table location for sub-$500/month.
Competitive Advantage
10x cheaper than FactSet, works on any PDF with no standardized format required, ships structured output in minutes not days.
Regulatory Risks
Low regulatory risk. Not providing financial advice — only data extraction. GDPR: user-uploaded PDFs must be deletable on request.
What's the roadmap?
Feature Roadmap
V1 (launch): bulk PDF upload, Claude table extraction, CSV export, Stripe billing. V2 (month 2-3): EDGAR auto-fetch, confidence audit UI, JSON API. V3 (month 4+): team seats, custom schema mapping, scheduled monitoring.
Milestone Plan
Phase 1 (Week 1-2): upload pipeline and Claude extractor working end-to-end with one real annual report. Phase 2 (Week 3-4): dashboard, export, Stripe billing live with 3 beta analysts. Phase 3 (Month 2): 5 paying customers and EDGAR URL fetch shipped.
How do you build it?
Tech Stack
Next.js, Claude API (document vision), Supabase, Cloudflare R2 for PDF storage, Stripe — build with Cursor for API routes, v0 for dashboard UI
Suggested Frameworks
LangChain for document chunking, pdf-parse for pre-processing, Zod for schema validation
Time to Ship
3 weeks
Required Skills
Claude API PDF vision, Supabase storage, Next.js API routes, CSV export logic.
Resources
Anthropic docs for document vision, SEC EDGAR API docs, Cloudflare R2 quickstart.
MVP Scope
app/page.tsx (landing + upload UI), app/api/extract/route.ts (Claude vision pipeline), app/api/jobs/route.ts (queue polling), app/dashboard/page.tsx (results table), lib/db/schema.ts (jobs, results, users), lib/claude/extractor.ts (PDF table extraction logic), lib/edgar/fetch.ts (EDGAR URL fetcher), components/ResultsTable.tsx (data grid), seed.ts (3 demo reports pre-extracted), .env.example (required env vars)
Core User Journey
Upload PDF batch -> job runs -> download structured CSV with confidence scores -> upgrade to paid when trial limit hit.
Architecture Pattern
PDF upload -> Cloudflare R2 -> extraction job queued in Supabase -> Claude vision API processes pages -> structured tables stored in Postgres -> CSV download served to user.
Data Model
User has many Jobs. Job has many Documents. Document has many ExtractedTables. ExtractedTable has many Rows with confidence scores.
Integration Points
Claude API for document vision extraction, Cloudflare R2 for PDF storage, Supabase for database and auth, Stripe for billing, SEC EDGAR API for direct URL fetching, Resend for job completion emails.
V1 Scope Boundaries
V1 excludes: real-time EDGAR monitoring, XBRL parsing, team collaboration, custom schema mapping, API access for programmatic use.
Success Definition
A paying analyst uploads 100 reports overnight, gets clean structured tables by morning, and renews without any founder contact.
Challenges
Distribution is the hardest problem — financial analysts trust established data vendors and are slow to adopt new tools. Getting the first 3 paying customers via warm intros to analyst communities is the make-or-break step, not the technical build.
Avoid These Pitfalls
Do not try to handle every edge case in PDF formatting before launch — ship with confidence scores so analysts know what to manually verify. Do not price below $199/month or you attract tire-kickers not serious analysts. Finding first 10 paying customers takes 3x longer than building the product.
Security Requirements
Supabase Auth with Google OAuth, RLS on all tables, PDFs scoped to uploading user only, rate limit 20 uploads/hour per user, GDPR deletion endpoint required.
Infrastructure Plan
Vercel for Next.js, Supabase for Postgres and auth, Cloudflare R2 for PDFs, GitHub Actions for CI, Sentry for error tracking, estimated $150/month infra at launch.
Performance Targets
100 DAU at launch, extraction jobs complete under 90 seconds per 200-page PDF, dashboard loads under 2s, queue polling every 5 seconds via Supabase realtime.
Go-Live Checklist
- ☐Security audit complete.
- ☐Payment flow tested end-to-end.
- ☐Sentry error tracking live.
- ☐Vercel monitoring configured.
- ☐Custom domain with SSL active.
- ☐Privacy policy and terms published.
- ☐3 beta analysts signed off.
- ☐Rollback plan: revert to previous Vercel deploy.
- ☐Launch post drafted for r/SecurityAnalysis and ProductHunt.
First Run Experience
On first run: 3 pre-extracted demo annual reports (Apple, Tesla, Microsoft) are visible in the dashboard with full table previews. User can immediately download a CSV from a demo report and see exactly what extraction looks like. No manual config required: demo data is seeded, Claude key only needed when user submits their own upload.
How to build it, step by step
1. Define Zod schema for ExtractedTable with row, column, value, confidence, pageNumber fields. 2. Set up Supabase project with jobs, documents, extracted_tables tables and RLS enabled. 3. Build Cloudflare R2 upload endpoint that stores PDFs and creates a job record. 4. Write lib/claude/extractor.ts that sends PDF pages to Claude vision with a prompt targeting financial table detection. 5. Build a job processor that iterates PDF pages, calls extractor, and stores results in Postgres. 6. Create app/dashboard/page.tsx showing job queue status and results preview table using v0. 7. Add CSV and JSON export endpoints at app/api/export/[jobId]/route.ts. 8. Integrate Stripe checkout for $299/month plan with usage-based PDF cap enforcement. 9. Add Resend email notification when job completes. 10. Deploy to Vercel and walk through uploading a real annual report end-to-end without any manual steps.
Generated
June 9, 2026
Model
claude-sonnet-4-6
Disclaimer: Ideas on this site are AI-generated and may contain inaccuracies. Revenue estimates, market demand figures, and financial projections are illustrative assumptions only — not financial advice. Do your own research before making any business or investment decisions. Technology availability, pricing, and market conditions change rapidly; always verify details independently.