Model training
Your expertise, in a model.
Turn domain knowledge into a model built for your workload. We shape the data, run the training, and measure what changed — with the weights and evaluation tools to keep building.
Find your starting point
When to bring us in.
Start with the challenge in front of you. We'll shape the work around it.
What you get
Tangible work.
Lasting capability.
Custom training and fine-tuning for your domain, with curated data, baseline comparisons, evaluated weights, and a reproducible handoff.
A useful model is more than a checkpoint. Know what it learned, how it was measured, and how to run it.
A dataset built for the task
Curated examples, documented preparation, and separate training and evaluation sets. Where gaps remain, we scope synthetic generation and validation around your domain.
Model artifacts and run history
The agreed weights or adapters, configurations, and training logs, with base-model dependencies and licensing requirements documented for your team's next run.
An evaluation you can rerun
A task-specific harness, baseline comparisons, and an error analysis. See where the model improved, where it regressed, and which behaviors still need work.
A practical deployment handoff
Inference examples, environment requirements, known limitations, and recommended follow-up work. Agree on any serving optimization or integration work as part of the scope.
How we work
A clear path. At every step.
From the first brief to the final handoff, here's how the work takes shape.
Before we begin
The details
that matter.
More on scope, collaboration, and what comes next.
Discuss your requirementsIs training the right starting point?
We begin with the behavior you want to change. Prompting or retrieval may address the task without training; fine-tuning becomes a candidate when you need more consistent task behavior. The scope should explain why training is worth testing against a simpler baseline.
Do we need a finished training dataset?
No. A description of the workflow and representative examples are enough to start scoping. We assess coverage and quality before deciding what to curate, label, or generate. Data preparation and any synthetic data work are defined in the engagement.
How will we know the model is better?
We agree on task-specific measures and a held-out evaluation set before training. The handoff compares the selected model with the baseline, including error patterns and regressions. A training run is an experiment; improvements are measured rather than assumed.
What do we receive, and what can we use?
The handoff specifies weights or adapters, configurations, logs, and evaluation assets. Usage rights depend on the selected base model, dataset permissions, and engagement terms. We establish those boundaries during scoping so your team knows how it can deploy and extend the result.
Can you help get the model into production?
We plan the training around your deployment constraints and include the agreed handoff instructions. If the model needs quantization, serving optimization, or infrastructure work, we can scope that alongside training or as a follow-on inference engagement.
Keep building