Agentic systems
Production agents with evals, tool contracts, and a human in the loop until the loop earns its removal.
Production studio
Build systems the next decade will still thank you for.
Optimistic does not mean naive. It means we ship agents, models, and data platforms for the version of the future we want to inhabit.
Evals first
Nothing scales that cannot be scored.
Human in the loop
Until the loop proves it can leave.
Boring infrastructure
Surprising outcomes, quiet ops.
Capabilities
The work we take, and the work we refuse.
Data for models
Knowledge layers and unstructured pipelines so retrieval, memory, and training data actually match the work.
Applied machine learning
Forecasting, ranking, and NLP that ship past the notebook — measured, monitored, and owned.
Reliability / AIOps
Drift, cost, latency, and failure modes treated as product work, not an afterthought ticket.
Selected work
Quiet systems, visible results.
Insurance
Claims triage that waits for a human only when it should
First-pass routing in production, with an audit trail on every override.
Industrial
A plant knowledge layer instead of a chatbot over PDFs
Technicians retrieve procedures with source spans, not hallucinated steps.
Capital markets
Research agents bound to a signed corpus
Drafts land in the existing memo workflow, never as a side channel.
Approach
Start with the decision, not the model.
We scope from the moment a person has to act. Then we choose architecture, data, and evaluation so that action gets cheaper, safer, and more repeatable.
- 01
Name the decision and the cost of being wrong.
- 02
Measure the current path before proposing a new one.
- 03
Ship a narrow loop with an explicit override.
- 04
Only then widen tools, traffic, and autonomy.
Bring a real problem.
If it only exists as a slide, we will send you back to write a brief. If it exists in production pain, we should talk.