Grounded systems, not demonstrations.
Prototypes impress in a demo and collapse against real data.
- Prototypes impress in a demo and collapse against real data.
- Model output cannot be traced to a source, so it cannot be trusted for decisions.
- Sensitive corpora cannot legally be sent to third-party providers.
- Retrieval-grounded architectures where every answer is attributable to a document.
- Evaluation harnesses that treat prompt and model changes as code changes.
- On-device and self-hosted inference paths where data residency or connectivity demands it.
- Explicit refusal behaviour and calibrated confidence in the interface.
What you receive.
Retrieval and ingestion pipeline
Evaluation dataset and regression suite
Inference deployment (hosted or local)
Citation and confidence surfacing in the UI
Cost and latency budget per request
How the engagement runs.
- 01
Discovery
Constraints, stakeholders and the real definition of done.
- 02
Architecture
Written decisions, boundaries and trade-offs before code.
- 03
Build
Incremental delivery against a reviewable specification.
- 04
Verification
Automated tests, load profiles and security review.
- 05
Handover
Documentation, runbooks and an operable deployment.
Tools used, and why.
- Python
- PyTorch
- ONNX Runtime
- pgvector
- Hybrid retrieval
- Cross-encoder re-ranking
- FastAPI
- Local inference
What changes afterwards.
- An AI feature whose answers can be verified, measured and defended.
- Predictable cost and latency instead of an unbounded provider bill.
Common questions.
Can inference run without internet access?
Yes. Quantised on-device models are used where connectivity is unreliable, with a server path reserved for enrichment and low-confidence cases.
How is hallucination handled?
Generation is constrained to retrieved context, answers carry citations, and questions without sufficient support are declined rather than improvised.