Patric Fitigau · Romania · Available for contract work

Document automation that cites its sources.

Retrieval systems, caption pipelines and compliance tooling in Python. The design constraint on everything I build is the same: an honest gap is cheap, a confident fabrication is expensive. So the tools abstain when the evidence isn't there.

The second row is the point. Guessing here would put a false statement in front of a prospective customer.

What I build

Work

vendor-security-questionnaire-rag

Measured on 20 CAIQ v4.0.2 questions: 13 usable as drafted, 7 correctly abstained, 0 fabricated.

Turns a vendor security questionnaire into a reviewable first draft from a company's own policy documents. Citation grounding is enforced twice, independently — the second check verifies that every sentence the model cites actually appears in the retrieved evidence, and forces the answer back to "not found" when it doesn't.

anchor-align

Boundary error on untouched words: 4.0 ms, against 370.9 ms for the difflib baseline. Synthetic corpus; real-transcript validation still open.

Recovers caption timing after a human has edited the transcript — surviving both small corrections and whole sentences moved elsewhere — then segments the result into WebVTT cues that obey the standard constraints and never overlap.

eu-ai-newsletter

Built and verified end-to-end; publication scheduled.

Weekly EU AI Act tracking for compliance and legal teams: RSS ingest, deduplication against the five outlets covering the same event, relevance scoring, summarisation, and HTML issue assembly on n8n with a FastAPI sidecar.

How I work

I benchmark before I claim. Every number on this page comes from a harness in the repository that you can run yourself, and the caveats are published next to the results — including the ones that aren't flattering.

When a measurement says a feature makes things worse, it ships disabled. Anchor-align's phonetic matching is off by default for exactly that reason.