Governed lakehouses, model ops, and responsible AI — built for enterprises that need provenance, not promises. We design the platform, ship the models, and operate both with lineage, bias monitoring and human-in-the-loop controls.
Every Data & AI engagement bundles the platform, the models and the governance — because models without lineage are liabilities, and lineage without models is a dashboard.
Medallion lakehouse with lineage, catalog and policy enforcement — the foundation under everything downstream.
Semantic layers, governed metrics, and self-serve BI — so every number on the CEO's dashboard traces back to a trusted source.
Model training, deployment, monitoring and drift detection — the boring infrastructure that turns notebooks into reliable capability.
RAG, fine-tuning and agent systems with retrieval grounded in your catalog — not a wrapper on a public endpoint.
Bias testing, explainability, policy gates and clear appeal pathways for humans affected by model decisions.
Domain-aligned ownership with central platform governance — federated, interoperable, and socio-technically realistic.
Every data program runs on the same spine: build trust first, then models, then agents. We don't ship LLMs on a spreadsheet.
Data estate audit, use-case shortlist, and a responsible-AI risk screen. Outputs a sequenced roadmap with a "do nothing" option on the cover page.
Stand up the lakehouse, catalog, lineage and access-policy spine. Ship the first trusted data products in production, not in a staging bucket.
Ship the first two production models — with eval harnesses, monitoring and appeal pathways — and the RAG system grounded in your catalog.
MLOps, drift monitoring, governance reviews and responsible-AI committee support — fully managed or co-managed with your analytics team.
Pooled across Data & AI engagements delivered since 2020.
We pick data stacks by residency, data gravity and team readiness — and hold every choice to the same lineage and policy bar.
A top-20 global bank engaged us to rebuild its model-governance backbone — moving from fragmented notebooks to a centralized, jurisdictional registry with policy gates baked into deployment.
We stood up the registry, migrated the top 400 models under lineage, automated model cards, and delivered regulator-ready evidence packs on a button press.
Neither religiously. We staff certified engineers on both, and pick by data gravity, existing licensing, team readiness and residency. We'll tell you honestly when the choice doesn't matter.
RAG-first, grounded in your governed catalog, with eval harnesses and human-in-the-loop controls before any agent touches a production workflow. We decline "wrap an LLM over prod" asks.
In a centralized registry with automated model cards, policy gates in CI/CD, and drift monitoring bound to business-owner alerts. Governance is a platform feature, not a quarterly audit ritual.
We build for jurisdictional residency from day one. Our reference architectures keep PII in-region, with cross-region work limited to tokenized aggregates under explicit agreements.
Yes. Our responsible-AI framework is an active mapping of both, refreshed every quarter as secondary legislation lands. Every deployed model has a model card, a risk tier, and a defined appeal pathway.
Absolutely — and it's in the SOW. Every engagement includes a pairing window and a handover packet. Many clients co-manage long-term, with our team staying on MLOps while theirs owns modeling.
Data doesn't travel alone. Most programs bundle at least one of the below, staffed by the same senior team.
Share a brief, a regulatory deadline, or a model that's worrying you. A senior partner responds within one business day with a calibrated engagement shape.