sujalmallick/probity-ai

Probity

Evidence before payment.

Small businesses pay invoices every day, and a single changed bank account or copied invoice can cost them dearly. Probity investigates each invoice before you pay it. It reads the invoice, compares it with your own records (vendors, verified bank accounts, past invoices, purchase orders) and with outside sources, and shows you the evidence for every concern it finds. A pure-code risk engine turns verified findings into a score. Low-risk invoices can be cleared automatically; anything unusual is held for a person to decide, with the reasons.

The AI can investigate, but it cannot change the risk score. Agents find signals, every claim needs evidence, code computes the score, and a human decides the payment.

Try it live: https://probity-3xgk.onrender.com. Sign up with your email and you get your own empty workspace. It's on free hosting, so the first visit after a quiet spell can take about a minute to wake up.

Who it's for

• The accounts person (accountant): uploads invoices, keeps the vendor list and past invoices up to date, and fixes misread fields.
• The approver: reviews held invoices, approves or rejects with a reason, and confirms changes with the vendor through a channel already on file.
• The vendor: never logs in. They may get a short, neutral email asking them to confirm details, sent only after an approver approves it.

How a real invoice flows

1. Upload. The accountant uploads a PDF or an emailed invoice.Probity reads the fields: vendor, GSTIN, invoice number, dates, amounts and bank account. 2. Investigate. Agents check, among other things: - Is this a vendor we know?- Is this the bank account we verified for them?- Is the price normal for them?- Have we seen this invoice before?- Does it match the purchase order?- How old is the sender's web domain?- Is there anything public about them?3. Verify. Every finding must point to evidence: the invoice value and the record it was compared with.

Findings without evidence are dropped. 4. Score. Pure code adds up the verified findings into a 0 to 100 score: LOW, MEDIUM, HIGH or CRITICAL. 5. Gate. If everything checked out, the invoice is cleared.If anything is unusual, or a required check could not be verified, it's held for a person, who sees exactly why. 6. Decide. The approver approves, rejects, asks the vendor to confirm, or asks for a deeper investigation.Probity recommends;it never pays and never accuses anyone. 7. Remember. The outcome is saved, and the vendor's next invoice is checked with that history in mind.

Design rules

- The AI investigates;pure code computes the risk score. The risk engine has no AI input and no way for text to change a number. - Every claim needs evidence. No evidence means no claim.Unverified findings add zero points. - "Could not verify" is never "fine". When a check can't run (missing key, no history, network failure), Probity says so, adds no points, and holds the invoice if the check was required.It never invents a result. - The system never pays anything. It recommends;people decide. - A human always decides anything that isn't clearly low-risk.

Large or critical cases need two approvers. - Vendor replies stay unverified until an approver confirms them through a channel already on file (a known phone number, a bank letter, in person), never through details from the email itself. - Neutral wording. Probity reports anomalies and recommends a hold.It never calls anything "fraud".

Architecture

flowchart LR
    W[Web app
React + Vite] -->|/api/v1| A[API
FastAPI]
    A --> G[Agents
LangGraph]
    G --> R[Risk engine
pure code]
    A --> DB[(PostgreSQL
row-level security)]
    G --> DB
    A -.-> C[Clerk
sign-in]
    G -.-> L[AI: Claude
or Gemini]
    G -.-> T[Tavily
web search]
    G -.-> RD[RDAP
domain age]
    A -.-> E[Resend
email]

Agents in order: document reader → planner → vendor investigator + transaction analyst → web researcher (when needed) → evidence verifier → risk engine → case analyst → policy gate. Details: docs/Architecture.md.

Quick start

You need Git, Python 3.12+, Node 22, Docker Desktop, an AI key (Anthropic, or Google Gemini) and a free Clerk development app. On Windows:

git clone -b real-data https://github.com/sujalmallick/probity-ai.git
cd probity-ai
powershell -ExecutionPolicy Bypass -File scripts\dev.ps1

-b real-data matters: that branch has the current code (see Branches and deployment). The first run sets things up and tells you which keys to add. Add them and run it again, then open http://localhost:5180.

Full step-by-step guide (Windows, macOS, Linux): docs/SETUP.md. Getting the keys: docs/API_KEYS.md. Deploying: the live app runs on Render + Neon (infra/render/README.md). There's also a Docker + Cloudflare alternative: infra/cloudflare/README.md.

Current status

Probity is a hackathon prototype. It works end to end on real data, but it hasn't been audited for production use.

Working - Upload text-based PDFs, emailed invoices (.eml) and text files. - The full agent pipeline, with "could not verify" handling and labelled fallbacks when the AI is unavailable. - The risk score with evidence for every point, auto-clear rules, approvals (including two-approver cases) and written reasons. - Vendor list with verified bank accounts, domains and contacts;

CSV import of vendors, past invoices and purchase orders. - Neutral vendor emails (Resend, allowlist while testing), vendor replies, out-of-band confirmation. - Roles (viewer, accountant, approver, owner), case memory, audit log, PDF/JSON export, and workspace isolation in the database. - A choice of AI provider: Anthropic Claude (default) or Google Gemini. - Past invoices and purchase orders by CSV import or one at a time (API).Records an accountant adds count in comparisons only after an approver approves them. - Upload safety without a heavy virus scanner: every PDF is rewritten without active content (JavaScript, embedded files, auto-actions) before it's stored;

hostile PDFs are refused.ClamAV can still be added (CLAMAV_HOST). - Usage limits per workspace per day and per case (investigations, AI tokens, web searches, upload size).Hitting one stops the work cleanly and names the limit. - A per-agent trace for every case and automatic sanity checks (score = verified points, no point without evidence, no "all clear" without a search…);any problem holds the invoice.Failed cases can be retried from the start. - python -m probity.check tests every integration for real, including a database create/read/update/delete round trip;

python -m probity.sanity re-checks stored cases. - Run Probity on your own labelled invoices and get a report: benchmark/real/README.md. - A production Docker stack (API, worker, scheduler, web, Cloudflare Tunnel;R2 for files): see infra/cloudflare/README.md.

Planned / not yet supported - Scanned PDFs and photos: refused today.There's no OCR. - GST registry verification: there's no free official API.Probity checks the GSTIN's format and checksum only, and shows the registry status as "could not verify".You can record what you saw on the GST portal by hand;it's labelled "Entered manually". - Email providers other than Resend (no SMTP);bounce and out-of-office handling for vendor replies. - Screens for some API features: approving past invoices and POs, adding single ones, the per-agent trace, retrying a failed case, notifications, the invoice-risk policy.

The API for each is in place (docs/API_CONTRACT.md). - Frontend tests and a linter. - Automatic deletion of cases past the retention period (8 years by default;an owner can list them).See docs/PRIVACY.md.

Known limitations - Without approved history, verified bank accounts and a Tavily key, many checks say "could not verify", so a new workspace holds most invoices. - Non-INR invoices are held, because prices can't be compared. - A background worker (Redis + Celery) is optional. By default investigations run inside the API process, which is fine for one machine. - Without ClamAV, a PDF that matches known malware but has no active parts is stored (cleaned) rather than named as malware.

Full list: docs/FEATURES.md.

Branches and deployment

your branch  →  pull request into real-data  →  tests pass, review  →  merge  →  Render deploys  →  live in a few minutes

• Changing files on your computer never changes the live site. Only a commit pushed to real-data on GitHub does.
• Pushing to any other branch is safe. That includes your own branch and main; nothing is deployed.
• Don't push straight to real-data. Work on your own branch and open a pull request, so the tests run first.

How it's hosted: - Render (free plan) runs one web service: the API, plus the built web app on the same address. It's set up by render.yaml and infra/render.Dockerfile. - Neon holds the PostgreSQL database and the uploaded invoice files. - Clerk handles sign-in. - The live settings and keys are set in Render's dashboard, never in the repo.

Step-by-step setup: infra/render/README.md.

Documentation

Contributing

Friends and newcomers are welcome. Start with CONTRIBUTING.md: it has starter tasks, the workflow, and the checks to run before a pull request. Pull requests go into real-data. To report a security problem privately, see SECURITY.md.

License

Probity is open source under the MIT License. You can use, copy, change and share it, as long as you keep the copyright and license noti

... (truncated at the site's per-page length limit; see the source link below for the full text.)