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Own the intelligence your product depends on

Turn production traces, evals, and domain workflows into model behavior you can improve, deploy, and measure.

Generic models get everyone to the same starting line. Product advantage starts when your own signal becomes better evals, better datasets, tuned models, workload-shaped serving, and a deployment path you control.

Token Factory helps teams map the loop from product data to model improvement to production serving.

Book a data and model loop workshopExplore post-training

What we map

A practical path from production signal to model advantage.

Data

Which traces, logs, failed tasks, feedback, and domain data can become usable training or eval signal.

Evals

What quality bar defines better behavior for the product.

Training path

Whether fine-tuning, reinforcement fine-tuning, a custom drafter, your own weights, or no training is the right next step.

Serving path

How the resulting model or drafter should be deployed and measured.

Iteration loop

How often to retrain, retest, and redeploy as the workload changes.

Risk

Data boundaries, readiness, scope, and claim limits before going public.

Why custom AI starts with the loop

The model call is not the product moat. Every team can prompt the same frontier models. The advantage comes from the system around the model: production traces, failed tasks, evals, domain knowledge, training workflows, and deployment back into the product.

The loop looks like this:

Capture production traces

Turn failures into evals and datasets

Train or tune the right model artifact

Deploy through the right endpoint path

Measure quality, latency, and economics

Repeat as the product changes

How it works

Map the data and model loop.

Start with one product workflow where model behavior matters.

Identify the available traces, evals, feedback, and privacy boundaries.

Decide whether the next step is better evals, fine-tuning, reinforcement fine-tuning, Custom Speculator, bring-your-own-weights, or no training.

Map deployment through public endpoints, dedicated endpoints, or custom weights where appropriate.

Define the measurement and retraining loop.

What you get with workshop

Turn production signal into a model improvement plan.

What you bring

  • Product workflow or model behavior problem

  • Production traces or failed tasks

  • Eval set or quality definition

  • Current model and provider path

  • Latency, cost, safety, and privacy constraints

  • Training or custom weights requirements

  • Deployment path and production timeline

What you receive

A concrete recommendation for how to turn product signal into better model behavior and how to deploy it safely.
Your data stays yours. We map the data boundaries before anything moves.

Book a data and model loop workshop

Product modules to connect

Data Lab

Explore logs and existing datasets, then turn them into reusable training data.

Post-training

Fine-tune, distill, and adapt open models for production behavior.

Custom Speculator Training

Train workload-specific drafters for faster, steadier inference on known prompt shapes.

Dedicated endpoints

Deploy serious production workloads through the right endpoint path.

Questions and answers

They help, but the first step can be mapping what signal exists today and what should be collected next.

Build the loop your product can learn from

Bring one workflow, the signal you already have, and the behavior you want to improve. We will help map the data, model, and deployment path.

Book a data and model loop workshop