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.
The engagement, at a glance
You bring
Your data + domain
We build
Curate. Train. Evaluate.
You leave with
A specialized model
- Custom training and fine-tuning
- Quality measured on your tasks
- Weights, evaluations, and run logs
Find your starting point
When to bring us in.
Start with the challenge in front of you. We'll shape the work around it.
Teach the model your domain
Adapt a base model to specialized language, task patterns, and the examples your team considers good. Start with a defined workflow and a measurable baseline.
Make outputs more consistent
Train toward the formats, classifications, and response behaviors your product needs. Evaluate both common requests and the edge cases that break downstream workflows.
Build a focused model
Explore a specialized model for a bounded task when a general model carries unnecessary overhead. Model selection accounts for quality, memory, and deployment constraints.
What you get
Tangible work.
Lasting capability.
A specialized model
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.
Define the target
Choose the task, base-model candidates, deployment constraints, and acceptance measures. Establish a baseline before committing compute to training.
Prepare the data
Review permissions, quality, and coverage. Curate the training examples and hold out evaluation data so the comparison remains meaningful.
Train and compare
Run the agreed experiments, inspect failures, and compare candidates against the baseline. Document tradeoffs before deciding which artifact to advance.
Transfer the work
Review results together and deliver the agreed artifacts, evaluation tools, and run notes. Make the next training or deployment decision explicit.
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.
Get started
Your next step.
A clear plan.
Bring us the goal and the constraints. We'll work with you to define the scope, deliverables, and a practical place to begin.
For the first conversation
A useful starting brief.
- The task, representative inputs, and examples of a good output.
- Available data, its usage permissions, and your current baseline.
- Target hardware, quality requirements, and the budget constraints for the run.
Keep building