Adoption layer
Forward deployed engineering
Policy that lives in a document does nothing. Our engineers sit with your team and encode your actual policy against your actual systems, then stay until it runs.
How an engagement works
We start with one decision that matters and is measurable. Our engineers work alongside your compliance, risk or clinical people to turn written policy into compiled constraints, connect the Memory agent to the systems that hold your evidence, and define what the evaluator scores against using your own rubrics.
Two design partner implementations have run this way, a telco decision engine and a medical device business, at four to six weeks each.
Why the whole industry is moving this way
The largest AI companies have concluded the same thing and are funding it directly. OpenAI launched a deployment company with more than four billion dollars of initial investment and around 150 forward deployed engineers. Microsoft’s Frontier Company has 6,000 industry and engineering experts embedded with customers. AWS committed one billion dollars to forward deployed engineering with an explicit customer self-sufficiency goal. Anthropic has both an enterprise services company and a partner network with an initial 100 million dollar commitment for 2026.
Sources: company announcements, 2026. Figures are directional and mix direct corporate investment, joint-venture capital and partner funding. They are not additive.
Model access alone does not solve data integration, workflow redesign, evaluation, governance or adoption. Deployment has become part of the product.
What you get
A running deployment on one decision, the policy encoded and version controlled, evaluation rubrics tied to your KPIs, the full reasoning trace available to your reviewers and auditors, and your team able to operate and extend it.