Adapt AI to your data: preparation, evaluation, production rollout, monitoring, and continuous improvement. Useful, measured, and maintainable AI.
Mission
Adapt AI to your data (embeddings, semantic search, RAG, classification…) and bring it to production: preparation → evaluation → go-live → monitoring → continuous improvement.
Step 1 — Data
- —Collection, cleaning, deduplication
- —Anonymization if needed (GDPR)
- —Schema, sources of truth, access rules
- —(Optional) annotation and ground-truth datasets
Step 2 — Baseline (pragmatic)
- —Embeddings, index, and retrieval engine
- —System prompts and response rules
- —Initial measurement (quality, cost, latency)
Step 3 — Quality (measured)
- —Test sets and metrics
- —Error analysis (hallucinations, low recall, ambiguities)
- —Robustness tests and guardrails
Step 4 — Production (MLOps)
- —API, caching, indexing, security
- —Traceability (logs, sources, replay)
- —Monitoring for latency, cost, failure rates
Step 5 — Continuous improvement
- —User feedback
- —Index updates, new tests, prompt iterations
- —Threshold and policy tuning
Expected outcome
A useful and maintained AI system: reproducible, auditable, and able to improve without drifting.