We move models from research notebooks to regulated production. Generative AI, RAG systems, predictive models, NLP pipelines, AI-driven testing — built with the eval, monitoring, and governance that makes them trustworthy in healthcare, finance, and telecom.
The hard part of AI isn't the model — it's the production system around it. Every capability below is something we've shipped beyond a proof-of-concept.
RAG architectures with grounded retrieval, prompt-engineering frameworks that scale to thousands of templates, evaluation harnesses, jailbreak defenses, cost ceilings. Closed and open-weight models.
Tabular ML for fraud, churn, propensity, demand forecasting. XGBoost / LightGBM in production with feature stores, monitoring, retraining pipelines that catch drift before users do.
Entity extraction, document classification, summarization, semantic search. Custom embedding models when off-the-shelf isn't enough. Language-aware pipelines for multi-region deployments.
LLM-based test generation for legacy systems, property discovery via fuzzing-with-AI, regression test suites that grow themselves. Especially valuable when undocumented systems need coverage fast.
Feature stores (Feast, Tecton), training orchestration (Kubeflow, Argo), model registries (MLflow), deployment with canary + shadow traffic. Reproducibility and lineage built in, not bolted on.
Offline evaluation harnesses, A/B and shadow testing, hallucination detection, bias monitoring. Output explainability for regulated environments. Audit trails that satisfy compliance reviews.
Four phases. Production from day one — no PoC theater.
Two-week analysis of use case, data, regulatory perimeter. We tell you upfront if AI isn't the right answer — and what is. Output: feasibility report with cost / accuracy / risk matrix.
End-to-end production-shape prototype on real data — not a demo, an actual call into your system. Eval harness from day one. We measure what we'll be measuring at scale.
Pipelines, monitoring, model registry, deployment infrastructure. Shadow traffic before live. Canary rollout with kill-switch. Cost ceilings enforced at the gateway.
30/60/90-day operate-with engagement. Your team owns the model lifecycle by day 91. Drift dashboards in place, retraining cadence defined, eval suite running.
What we ship to production. Models change quarterly; the platform around them shouldn't.
Three representative engagements. Names anonymized.
30-minute call, senior ML engineer, no slides. We'll tell you on the first call whether your problem needs AI at all — and what it'll really cost.