TradeLedger — Trade Reconciliation Dashboard for Independent RIA Firms
Independent RIAs spend hours every month matching Schwab and Fidelity trade exports to their portfolio records in Excel — a job so painful they sometimes hire a part-time analyst just to do it. TradeLedger auto-ingests custodian CSV exports, matches trades to portfolio records, flags discrepancies, and delivers a white-label PDF report in minutes.
Difficulty
intermediate
Category
Finance
Market Demand
High
Revenue Score
7/10
Platform
Web App
Vibe Code Friendly
No
Hackathon Score
5/10
Validated by Real Pain
— sourced from real community discussions
Wealth management reconciliation analysts are hired specifically to manually match custodian trade data to portfolio records using Excel and Bloomberg terminals — a fully automatable workflow with no affordable software targeting independent RIAs.
What is it?
Independent Registered Investment Advisors managing $50M-$500M AUM are too small for Bloomberg reconciliation tools and too large to stay in spreadsheets. They download CSV exports from Schwab, Fidelity, or Pershing, manually pivot-match them to their own records in Excel, and pray nothing slips through. TradeLedger ingests those CSVs, runs exact and fuzzy-match reconciliation, surfaces unmatched trades with a classification reason, and exports a branded PDF for compliance records. No Bloomberg required, no enterprise contract, no 6-month onboarding. Buildable in 3 weeks with a CSV parser, a match engine, and Claude for exception reasoning.
Why now?
Claude structured output mode now produces reliable JSON exception classifications from unstructured trade data, eliminating the need for custom NLP training that previously made this product category expensive to build.
- ▸Drag-and-drop CSV ingestion from Schwab, Fidelity, or Pershing exports.
- ▸Exact and fuzzy trade matching engine with discrepancy classification.
- ▸Claude-powered exception notes explaining each unmatched trade in plain English.
- ▸White-label branded PDF report with match rate, exceptions list, and resolution status.
Target Audience
Independent RIA firms with 1-5 staff managing $50M-$500M AUM, approx. 15,000 registered in the US.
Example Use Case
Sarah, a solo RIA managing $120M AUM, uploads her monthly Schwab CSV, gets a reconciliation report in 4 minutes instead of 3 hours, and forwards the PDF to her compliance consultant without reformatting anything.
User Stories
- ▸As a solo RIA, I want to upload my Schwab monthly CSV and get a reconciliation report in minutes, so that I stop spending half a day on manual Excel matching.
- ▸As a compliance consultant, I want a branded PDF reconciliation report from my RIA client, so that I can review exceptions without reformatting their spreadsheet.
- ▸As an RIA principal, I want plain-English explanations for unmatched trades, so that I can resolve exceptions without re-reading Bloomberg documentation.
Done When
- ✓CSV upload: done when user drags a Schwab CSV and sees a normalized trade table rendered on screen within 10 seconds.
- ✓Match results: done when table displays matched trades in green and unmatched in red with a discrepancy type label per row.
- ✓Exception notes: done when each unmatched trade shows a one-sentence Claude-generated explanation visible inline in the table.
- ✓PDF export: done when user clicks Download Report and receives a PDF containing firm name, run date, match rate percentage, and full exceptions list.
Is it worth building?
$299/month x 15 RIA firms = $4,485 MRR at month 4. Realistic via cold outreach to NAPFA-listed advisors.
Unit Economics
CAC: $150 via LinkedIn outreach and NAPFA forum posts. LTV: $3,588 (12 months at $299/month). Payback: 1 month. Gross margin: 90%.
Business Model
SaaS subscription
Monetization Path
14-day free trial. $299/month per firm. Annual discount at $2,700/year.
Revenue Timeline
First dollar: week 3. $1k MRR: month 2. $5k MRR: month 5.
Estimated Monthly Cost
Claude API: $30, Vercel: $20, Supabase: $25, Stripe fees: $15. Total: ~$90/month at launch.
Profit Potential
Full-time viable at $5k-$10k MRR serving 20-35 RIA clients.
Scalability
High — add direct Schwab API integration, multi-custodian consolidation, and white-label reselling to compliance consultants.
Success Metrics
Week 2: 3 RIA beta users upload real CSV files. Month 1: 3 paid clients at $299/month. Month 3: $3k MRR.
Launch & Validation Plan
Post in NAPFA advisor forums and r/FinancialPlanning offering free first reconciliation run on their own Schwab CSV to validate pain and accuracy.
Customer Acquisition Strategy
First customer: reach out to 15 NAPFA-listed solo RIAs via LinkedIn offering a free reconciliation run on their last monthly Schwab export. Ongoing: NAPFA community forum posts, compliance consultant referral partnerships, FPA conference sponsorship.
What's the competition?
Competition Level
Low
Similar Products
Orion Portfolio Solutions targets large RIAs at $1k+/month. Tamarac requires full CRM adoption. TradeLedger targets solo and small RIAs who just need monthly reconciliation without switching their entire stack.
Competitive Advantage
No Bloomberg contract, no enterprise sales, no setup fee — upload a CSV and get a report in under 5 minutes at 5% of the cost of enterprise tools.
Regulatory Risks
RIA compliance data must be stored securely — document data retention and deletion policies. Do not position as SEC-required compliance software without an attorney review of marketing copy.
What's the roadmap?
Feature Roadmap
V1 (launch): CSV upload, match engine, Claude exceptions, PDF export, Stripe billing. V2 (month 2-3): multi-custodian view, Fidelity and Pershing normalizers, email report delivery. V3 (month 4+): direct Schwab API, white-label reselling, compliance consultant tier.
Milestone Plan
Phase 1 (Week 1-2): CSV normalizer, match engine, results table live. Phase 2 (Week 3-4): Claude exceptions, PDF export, Stripe billing. Phase 3 (Month 2): 3 paying RIA clients onboarded.
How do you build it?
Tech Stack
Next.js, Claude API, Supabase, Stripe, react-pdf — build with Cursor for reconciliation logic, v0 for dashboard table UI.
Suggested Frameworks
Next.js App Router, Papa Parse for CSV, Drizzle ORM
Time to Ship
3 weeks
Required Skills
CSV parsing, Next.js, Claude API, Supabase, PDF generation.
Resources
Papa Parse docs, Claude API structured output docs, react-pdf docs, Supabase quickstart.
MVP Scope
app/page.tsx (landing), app/dashboard/page.tsx (upload + results table), app/api/upload/route.ts (CSV parse + normalize), app/api/reconcile/route.ts (match engine), app/api/report/route.ts (PDF generation), lib/normalizer.ts (custodian CSV normalizer), lib/matcher.ts (fuzzy match logic), lib/claude.ts (exception explanation), lib/db/schema.ts (Drizzle schema), components/MatchTable.tsx, .env.example.
Core User Journey
Upload custodian CSV -> view match table with exceptions -> review Claude exception notes -> download branded PDF -> share with compliance consultant.
Architecture Pattern
User uploads custodian CSV -> Papa Parse normalizes columns -> match engine runs exact then fuzzy pass -> unmatched rows sent to Claude for plain-English exception note -> results stored in Supabase -> PDF generated via react-pdf -> user downloads report.
Data Model
Firm has many ReconciliationRuns. Run has many Trades. Trade has one MatchResult with status, discrepancy type, and AI exception note.
Integration Points
Papa Parse for CSV parsing, Claude API for exception reasoning, Supabase for data storage, Stripe for billing, react-pdf for PDF generation, Resend for trial expiry emails.
V1 Scope Boundaries
V1 excludes: direct custodian API connections, multi-custodian consolidation view, team accounts, mobile app, automated scheduling.
Success Definition
An RIA with no founder involvement uploads a real Schwab CSV, receives a fully classified reconciliation report, and upgrades to paid after the trial.
Challenges
Each custodian has slightly different CSV column names and date formats — build a normalizer layer before the matcher or you will rebuild it for every client. Distribution is the real challenge: RIA trust is earned slowly, so target NAPFA community forums and compliance consultant referrals, not cold ads.
Avoid These Pitfalls
Do not hardcode Schwab column names — every custodian and every export setting produces different headers, so build a configurable normalizer from day one. Do not skip the PDF export — compliance consultants will not adopt a tool that cannot produce a downloadable record. Acquiring first 10 paying RIAs will take 4x longer than building — plan for 6 weeks of outreach.
Security Requirements
Supabase Auth with Google OAuth. RLS on all firm and run data. CSV files deleted from storage after PDF generation. Rate limiting 30 uploads/hour per firm. GDPR deletion endpoint required.
Infrastructure Plan
Vercel for Next.js, Supabase for Postgres and file storage, GitHub Actions for CI, Sentry for errors, Vercel Analytics.
Performance Targets
50 DAU at launch. CSV parse and match for 1,000 trades under 5 seconds. PDF generation under 3 seconds. Page load under 2s.
Go-Live Checklist
- ☐Data retention policy documented.
- ☐Stripe billing tested end-to-end.
- ☐Sentry error alerts live.
- ☐CSV normalizer tested on real Schwab and Fidelity exports.
- ☐Custom domain with SSL active.
- ☐Privacy policy and data handling terms published.
- ☐3 RIA beta users validated PDF output.
- ☐Rollback to prior deployment documented.
- ☐Launch post drafted for NAPFA forums and r/financialplanning.
First Run Experience
On first run: a sample Schwab CSV reconciliation for a fictional firm is pre-loaded showing 47 matched trades and 3 flagged exceptions with Claude notes visible. User can immediately click Download Sample PDF to see the output format. No manual config required: demo runs entirely on seed data without any CSV upload.
How to build it, step by step
1. Define Drizzle schema: Firm, ReconciliationRun, Trade, MatchResult with fields for status, discrepancy type, and exception note. 2. Build lib/normalizer.ts with column-mapping configs for Schwab, Fidelity, and Pershing CSV formats. 3. Build /api/upload route using Papa Parse to normalize uploaded CSV into a standard Trade array. 4. Build lib/matcher.ts running exact match on trade date and amount, then fuzzy match on ticker symbol. 5. Pipe unmatched trades to Claude API requesting a one-sentence exception classification per trade. 6. Store full run results in Supabase with RLS per firm. 7. Build MatchTable.tsx showing green matched and red unmatched rows with exception notes inline. 8. Build /api/report route using react-pdf to generate a branded PDF with firm name, date, match rate, and exceptions list. 9. Add Stripe Checkout for $299/month after a 14-day free trial. 10. Verify: upload a real or synthetic Schwab CSV, confirm match table renders correctly, download PDF, and confirm Stripe billing completes.
Generated
May 15, 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.