RAG & retrieval
Chunking, indexing, and re-ranking tuned to your corpora and SLAs.
Applied machine learning
Intelligence that sits inside your products—not beside them.
From retrieval workflows to operational models, we tie outputs to your data estate, policies, and audit expectations—not generic prompts.
Problems we solve
- Prototypes that cannot meet latency, cost, or compliance limits.
- Hallucination risk without evaluation harnesses and fallbacks.
- Disconnect between data pipelines and production inference.
Our approach
Define the decision surface, ground it in retrievable evidence, instrument quality, then scale inference with clear rollback and human-in-the-loop paths.
Capabilities breakdown
Agents & orchestration
Deterministic steps where needed; autonomy only where earned.
MLOps
Versioned artifacts, drift monitoring, and deployment aligned to SRE practice.
Safety & governance
Policies, red-team probes, and operational guardrails tied to roles.
Technologies
Representative stack—client environments vary.
- Python
- PyTorch
- PostgreSQL
- Kubernetes
- Azure
- Google Cloud
Related case studies
Clinical data fabric for a health network
Leaders needed a single place to ask operational questions without copying PHI into yet another warehouse.
- −61% time to answer recurring operational queries
- +27% trials started after cleaner eligibility pulls
- 0 critical findings on external architecture review
Customer 360 for a multi-brand telco
Care and marketing both needed the same customer truth—with different purposes and tight governance.
- −23% repeat contact rate in pilot regions
- +31% campaign build speed with governed fields
- AAA internal audit rating for consent handling
Engagement models
Fixed Price
Well-defined scope, predictable budget. Ideal for projects with clear requirements.
Time & Material
Flexible scope with transparent billing. Best for evolving requirements and R&D.
Dedicated Teams
Full-time engineers embedded in your workflow. Scales with your roadmap.
FAQs
Do you train foundation models from scratch?
Rarely. Most value comes from integration, evaluation, and cost-aware inference on top of strong baselines.
How do you handle PII?
We map data classes, minimize retention, and enforce access patterns that align with your regulator context.