SquadPay
Making settling shared expenses less awkward.
Live: squadpayy.vercel.app
Splitting the bill is easy. Asking friends to pay you back is awkward.
SquadPay handles the tedious part (reading a receipt, doing the tax math,
working out who owes what) so the only thing left is a low-friction
payment request — not a manual accounting chore.
This isn't presented as "an AI app." Gemini is one step in a five-step
product flow, sitting behind two independent guardrail layers and a
mandatory human review screen before anything it produces becomes a real
number someone gets asked to pay. The full product/AI-PM case study —
problem, decisions, evaluation, failure modes, guardrails, what shipped
and what deliberately didn't — is in
docs/ai-pm/case-study.md.
The core flow
Receipt photo (or manual entry, always available)
│
▼
AI-assisted extraction — Gemini reads items, quantities, tax, total
│
▼
Review — every field editable; two independent checks flag anything
that doesn't add up or looks implausible, in plain language
│
▼
Split — deterministic arithmetic, zero AI involvement at this step
│
▼
Payment request — drafted for WhatsApp; SquadPay requests, never moves money
Who this is for
Groups of friends, roommates, or trip companions who split shared costs
regularly and currently do the accounting by hand — a calculator, a notes
app, a mental tally — and find that asking people to actually pay up is
the most uncomfortable part of the whole process.
AI, evaluated honestly
SquadPay's receipt extraction was evaluated against 15 real,
publicly-sourced receipts — not a curated demo set — and the results are
published as-is, including the failures:
- 11 of 15 receipts extracted successfully (4 failed: 1 rate-limit, 3
receipts with discounts the schema correctly refuses to guess at).
- Averaged over successful extractions: 84% item accuracy, 100% quantity
accuracy, 91% price accuracy, 73% tax accuracy, 91% total accuracy.
- Only 33% (5/15) were fully correct end-to-end by a strict bar.
- The one finding that mattered most: the evaluation's single
existing safety check (reconciliation) reported "correct" on a receipt
where every number was actually wrong by 1000× — because the error was
applied uniformly, the AI's math agreed with itself while being
completely wrong. That's why a second, independent, deterministic
guardrail layer exists (
lib/domain/anomaly-detection.ts) — reconciling
arithmetic is not the same as being right.
- The product has 69 unit and component tests (69/69 passing) covering the
split engine, reconciliation, anomaly detection and the review notices —
a product-test result, not a measure of AI accuracy.
A second real evaluation run has since completed too — same dataset, same
scoring, stored separately so the two never get conflated. No regressions,
no cherry-picking. Full breakdown, both runs: eval/ANALYSIS.md.
Methodology, how to reproduce it, and its stated limitations:
eval/README.md.
What's real vs. what's a hypothesis
This is a working, deployed product with one small-sample user-validation
round behind it — not yet a product with measured business outcomes.
A first round of real user testing across 12 known people found the core
problem is genuinely felt (9/12 showed real hesitation around asking
friends to repay them) and confirmed the most-requested next feature
(UPI/GPay integration — expected by all 12, deliberately deferred) — see
docs/ai-pm/user-validation-results.md.
Anything about adoption, retention, or measured time saved is still a
hypothesis, not a result — see
docs/ai-pm/business-hypotheses.md.
Project docs
Running it locally
npm install
npm run dev
Open http://localhost:3000. Copy .env.example
to .env.local and fill in Supabase + GEMINI_API_KEY to move past demo
mode — every value is optional in development; omitting any of them falls
back to a local mock store / mock receipt extraction so the UI stays fully
usable without external services.
npm run typecheck # tsc --noEmit
npm run lint # eslint
npm test # vitest — pure-logic tests only, zero API calls
npm run build # next build
Receipt-OCR evaluation is a separate, explicit, developer-triggered
command (never run automatically — see eval/README.md's API/quota-safety
section):
npm run eval:receipt -- receipt-01 # one receipt
npm run eval:receipts # all 15
Built on Next.js (App Router), TypeScript, Tailwind v4, and Supabase.
