Most AI projects don't fail because of the model. They fail because the data underneath it isn't ready, the retrieval is sloppy, and nobody built for what happens after launch. We build AI that survives contact with production — grounded in real data, evaluated against real questions, and engineered to keep working long after the pilot ends.
Beyond the chatbot. LLM-powered tools that actually replace work.
Most "AI features" are a text box bolted onto a dashboard. We build LLM applications that automate complex business processes end-to-end — document review, case triage, report generation, research synthesis — grounded in your enterprise knowledge through production-grade retrieval, with guardrails and cost control built in from day one.
The 80% of enterprise data nobody's touching yet.
Most enterprise data isn't in a database. It's in PDFs, scanned contracts, call recordings, CCTV footage, lab images, and handwritten forms. We build AI systems that extract structure, meaning, and signal from all of it — turning unstructured exhaust into queryable, governed assets.
Ask your data a question. Get a real answer.
No SQL. No analyst in the loop. No waiting. We build conversational analytics layers that translate business questions into correct, safe, context-aware queries — powered by multi-agent pipelines that handle routing, decomposition, time resolution, and synthesis the way a senior analyst would.
Act on problems before they happen. Recommend what actually converts.
Classical ML isn't dead — it's what actually runs most of the AI in production today. We build forecasting, classification, and recommendation models that drive measurable business outcomes: churn reduction, revenue uplift, risk scoring, demand planning, fraud detection.
Conversational analytics over thousands of entities and hundreds of governed metrics — with multi-agent pipelines for routing, decomposition, and synthesis.
Open-source models deployed on private GPU infrastructure — for clients where data can't leave the building.
Document, vision, and speech pipelines feeding the same governed data layer — so unstructured inputs become structured assets your analytics can query.
Forecasting, recommendation, and scoring models deployed end-to-end — feature store to inference endpoint to business dashboard.
Our AI work runs on the same pipelines, semantic layers, and governance we build in Data Engineering. No handoffs, no "the data team said it was ready."
Zero proprietary frameworks. Owned by your team the day we leave.