§pecs
From the new specification to a certified release — with AI that shows its work.
An AI review assistant for ITSG's system review of German payroll software,
built at Futury Build Days #3 (26–27 Oct 2026) · challenge sponsor ITSG · tech sponsor hessian.AI
Guided demo · Pitch deck · Live app (our hackathon server, real AI)
Quick start · Architecture · How it works · Trust model · Docs
📖 Contents
💡 About
Every payroll program in Germany (SAP, DATEV, Lexware, ~100 in total) must pass a system review by ITSG before it may send reports to the social insurance institutions (§95b SGB IV). Each year the government publishes a new version of the specification — hundreds of pages — and today experts read it by hand, write test cases by hand, and vendor engineers type them into their software by hand.
§pecs automates that loop for income replacement benefits (Entgeltersatzleistungen, DTA EEL):
- Import the new specification PDF next to last year's version.
- Detect changes — the official change log, reconciled with a section-by-section diff (it catches changes the log forgot).
- Understand each change with an LLM that quotes the spec word for word; an examiner approves, edits or declines it.
- Generate test cases whose expected results are computed by tested code, not by the AI.
- Hand over a package with an AI recap to the vendor's developers.
- Compare the vendor's DTA output file field by field, explain every difference in words, and issue a sealed certificate.
[!NOTE]
The background — who ITSG is, the challenge, the glossary and the data format — is in docs/. Start with the problem and our solution.
✨ Highlights
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| 🔍 Change detection that doesn't trust the log | The change log called §3.12 "editorial"; the diff found that the 6-month rule flipped at its boundary. |
| 🧾 Every claim is quoted | The AI must quote the spec verbatim; quotes are checked against the PDF text before anything is shown. |
| 🧮 Expected values from tested code | A small, safe formula language and an oracle compute every expected result; the AI's own number must match it. |
| 👩⚖️ Human in the loop | Nothing reaches a developer before an examiner has approved the reading of the change and verified the test case. |
| 🔒 No answers leak to vendors | The server strips expected values — even from the AI's chain of thought — before a developer sees a case. |
| 📜 Audit trail | Every action lands in a hash-chained audit log; reports and certificates are sealed with SHA-256. |
| ⚡ Built for a room full of users | A fair scheduler shares the AI workers across everyone's runs, with automatic back-off when the gateway is busy. |
| 📦 Tiny footprint | Python standard library + pypdf, two single-file HTML apps, one Docker container. |
📸 Screenshots
| Changes and review | Test-case review |
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| The change log says editorial; the AI shows the rule flipped, with before/now and the quote. | One case per rule. The expected result is computed by the oracle; checks G1–G5 are shown per case. |
| Developer checklist |
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| What the vendor's developers receive: an AI recap of what changed and what to do, and one row per test case. |
🚀 Quick start
Open demo/index.html in a browser. It is a single file with the slides and the guided demo; press D to start the demo, → to advance, P for the narrator window. The last slide shows the QR code to the live app. Details: demo/README.md.
Needs Python 3.10+ and a hessian.AI key.
cp pipeline/.env.example pipeline/.env # then put the key after HAI_API_KEY=
python -m pip install -r pipeline/requirements.txt
python pipeline/server.py
Open http://localhost:8787/ for the live app, or http://localhost:8787/demo/ for the guided demo on recorded data. On Windows you can also double-click pipeline/start.cmd; on macOS/Linux run sh pipeline/start.sh. Sign in as istg (ITSG examiner), sap or lexware (vendors).
This is the beginning of the README. Read the rest at the original. Read the full README ↗
