What we build
Most AI work fails between the notebook and production. We start from the decision the system has to support, then build backwards: data pipeline, retrieval or training strategy, evaluation harness, then the interface.
- Retrieval-Augmented Generation over private document sets, with hybrid (vector + keyword) retrieval and reranking
- Supervised fine-tuning and LoRA adapters where prompt engineering has hit a ceiling
- Classical ML for tabular prediction — gradient boosting, calibration, feature stores
- Agent workflows with explicit tool schemas, retries and human approval gates
How we prove it works
Every engagement includes an evaluation set built from your real queries before we write model code. We report accuracy, groundedness and latency against that set on every change, so 'it feels better' is never the acceptance criterion.
- Golden-set evaluation with regression gates wired into CI
- Groundedness and citation checks on every RAG response
- Latency budgets tracked at p50, p95 and p99
- Drift monitoring on inputs and outputs after launch
Reference point
GeneRisk AI — our DNA-sequence cancer risk classifier — reaches 97.37% accuracy on its held-out test set and serves predictions through a Flask API behind a React dashboard. The same evaluation discipline applies to client work.
What you get
- Evaluation harness and golden dataset you keep
- Model or pipeline deployed in your cloud account
- Inference API with authentication and rate limiting
- Runbook covering retraining, rollback and cost controls
How the engagement runs
Typically a 3–6 week build for a first production system, then bi-weekly iteration sprints. Scope is fixed per sprint; the evaluation set decides what ships.