Work records captured
Average task accuracy on held-out evals
Lower cost per task than GPT-6 Astra
Every model is benchmarked against Claude Opus 5.5, Claude Fable 5.1, GPT-6 Astra and GPT-6.1 Sol on your own held-out tasks before it ships.
Founders and engineers stay embedded until the workflow runs in production, typically four to six weeks from kickoff.
Every verified outcome goes back into training through BackContext, so each model version is better than the last.
Have more questions?
Which service should we start with?
Most teams start with BackContext and forward-deployed engineering. BackContext records the work, our engineers map the workflow, and the first model is in production four to six weeks later.
Can we use BackContext without a custom model?
Yes. BackContext works on its own as the context layer for every agent: journal, skills, vault, approvals and audit trail. The training data accumulates either way.
How do you prove a model is ready?
Held-out test sets built from your verified outcomes, side by side against Opus 5.5, Fable 5.1, GPT-6 Astra and GPT-6.1 Sol, with a quality gate that blocks any release that regresses.
Where do the models run, and who owns them?
In your AWS, GCP or Azure account, inside your VPC, or on Applied Crew–managed inference. The weights are yours either way.
What kind of work is a good fit?
A few hundred specific tasks your team runs thousands of times a day, such as matching candidates, extracting orders or screening documents. That is where a model of your own beats a generalist.
