We've built AI and data systems for clients when the demo isn't the point — when it has to ship, survive real user load, and return honest answers. Complex RAG, Model Context Protocol integrations, conversational analytics, and Looker BI that executives actually read.
Most RAG demos work on clean wikis. Ours works on actual enterprise content — PDFs with tables, Jira tickets with threads, Confluence trees that never got tidied. Hybrid retrieval, reranking, chunking strategies tuned per corpus, citations every user can verify, and offline evaluations that block bad releases.
We build MCP servers and agents that connect models to the tools users already live in: GitHub, Slack, BigQuery, Looker, internal APIs. Proper auth scoping, per-tool audit trails, and sandboxed execution — agents that can act, not just chat.
A chat-first analytics layer over your warehouse. Users type a question in plain English, get back a chart, a number, and a citation to the exact SQL that produced it. Semantic layer enforced, so "revenue" always means the same thing.
We design Looker semantic models and dashboards that stand up to scrutiny — version-controlled LookML, strict access grants, explorer guardrails, and Looker-embedded applications when self-serve isn't enough.
