From the data source to the agent in production.
Most engagements end with a repo and a handover doc. Ours end with the system running, your people using it, and us still on the hook.
One team across the whole path — architecture, ingestion, query, agents, and the months after.
Most AI projects deliver a proof of concept. Almost none survive contact with production. We only count the ones that do.
Take one, or both. The second gets much cheaper once the first exists.
01 · The foundation
Data leaving the source and landing organised, incremental and queryable in your bucket. Four weeks to production, with your team trained to run it afterwards.
02 · The outcome
Agents and applications on frontier models, wired to the real data over MCP. If the data layer already exists, this front starts in week one.
Concrete, in this order. Each week ends with something running. If your lake already exists, the clock starts at week 4.
We inventory your sources, map the questions the business actually asks, and design the bucket layout and IAM boundaries around them. This is also where we decide where AI belongs — and where it doesn't.
Architecture, access model, agent integration plan
Every source configured in Pipe — databases, APIs, spreadsheets — landing in your bucket as partitioned Parquet. Incremental, scheduled, monitored. We write the custom connectors you need.
Ingestion running against production sources
Lens on top of the lake: SQL for engineers, notebooks for analysts, plain English for everyone else. We build the first curated views and teach your team to build the rest.
Self-service analytics, curated views, first training
Claude, Cursor or your own agents connected over MCP with governed, read-only access. No retrieval pipeline, no vector store — the agent reads the same tables your analysts do, and we validate answer by answer against the SQL.
Agents live, answers validated against real data
We monitor the pipelines, onboard new sources as the business grows, tune queries, and keep the agents accurate as the models change. You make decisions; we keep the system standing.
Monitoring, new sources, agent tuning, continuous training
The full build, or one piece of it.
Companies with no lake yet
Architecture, every pipeline, query layer, two training sessions and 30 days of operational support. Production in four weeks.
Get startedSources nobody supports
Singer-compatible taps for internal APIs, proprietary databases and SaaS tools with no connector. With incremental replication, schema discovery, tests and docs.
Get startedTeams with data but no model
Problem framing, data audit, feature engineering, training and evaluation — predictions land back in the lake as tables your team can query with SQL.
Get startedTeams that want to own it
Hands-on workshops on lake architecture, Pipe and Lens, and agent integration — run against your own data, not a toy dataset.
Get startedCompanies whose data is already in order
From use case to running agent: scope, governed access over MCP, prompts and tools, and an evaluation battery that compares the agent's answer with the equivalent SQL.
Get startedCompanies with a product to build
Building and shipping an application on frontier models — architecture, integration with your systems, observability, and the path to production.
Get startedCompanies deciding before they invest
Two weeks looking at your processes and your data to say where AI pays for itself, where it doesn't, and in what order to build. Ends with a plan, not a report.
Get started