From a clean data foundation to an LLM in production.
Most enterprise AI projects fail at the seams — between the model and the data, between the prototype and the operating system. We've shipped against the seams. Our practice covers data architecture, model development, evaluation, deployment, monitoring, and the boring-but-critical governance underneath.
RAG, agents, copilots — built with retrieval, evals, and guardrails.
Forecasting, classification, anomaly detection in production.
Lakehouse, warehouse, streaming. Snowflake, Databricks, BigQuery.
Model registry, evals, lineage, fairness audits, SOC 2 alignment.
The path from idea to production AI.
A six-step path we run on every AI engagement. Most clients reach production in 90 to 180 days.
Outcomes, constraints, data audit
Stack selection, eval plan, infra
Working spike against real data
Pipelines, guardrails, monitoring
Staged rollout with kill-switch
24×7 monitoring + drift response