Get in touch The Custom Model Factory.
Construct Labs offers the full stack for enterprise custom models on open-source bases. One pipeline from harness to training, serving, and continual learning in production.
Most agents call a scatter of raw tools and APIs, each with its own schema and its own failure modes. We build a single interface across your systems instead, one surface the agent operates through. That interface also has to hold up as a training environment, so a custom model can later be trained directly on top of it, tuned to your task and your systems.
The harness gives the agent one interface to work through, but an untrained model still has to reason its way through that interface from scratch on every call. This stage trains the model on the task and on the harness together, so tool choice and task execution become learned behavior instead of something re-derived each time.
A single custom model on its own GPU rarely runs efficient. It cannot fill a large enough batch on its own. We serve every custom model as a small adapter on top of a shared base, so many tenants' models batch together and inherit the economics of a much larger deployment. Autoscaling then tracks demand across that shared pool, since bursts from different customers rarely land at the same time.
A model trained once stays frozen at that point, while user behavior, data, and tooling keep moving underneath it. We keep training after deployment on the signal production traffic already generates, corrections, edits, accepts, and rejects, each one a record of how you want the task done. We also carry that signal onto a stronger open base model as one comes out, so you move to the better base instead of staying locked into the one you started on.
For a customer's internal retrieval agent we trained a small open model on the task with reinforcement learning, inside a frozen copy of their stack. On held-out questions it beats Opus 4.8 with 80.5% versus 75.5% Pass@1, at roughly 25x lower cost per output token and three times the generation speed, with no measurable regression on general capabilities.
Read the case study →Our engineers work with your teams to train a model for your tasks. Whether you build agents on a costly frontier dependency or automate workflows across a large organization, we design the environment and the rewards together and train until the model beats the frontier on your task.
We host your custom models on our infrastructure, and you query them through one API. Shared serving pools and autoscaling absorb bursty production traffic, so you get dedicated-model quality at the lowest cost per token, without operating GPUs yourself.
Deployment, with training that never stops. We keep tuning the deployed model on your production feedback, so it stays in sync as your data, tools, and requirements change instead of slowly drifting out of date.
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