krummholzBESPOKE AI TRAINING
Train for the work
you actually do.
A model should earn its place in your workflow. We work with companies and individuals to scope training around a specific task, the data they are allowed to use and a result they can measure.
Start with a task.
Customer-service responses, specialist terminology, document classification and structured extraction each need different training examples and tests. Tell us what the model needs to do, where the current approach fails and what a useful result would look like.
We can discuss adapting an open-weight model to your work. The choice of base model, training method and deployment environment depends on the task, available data, licenses and compute budget.
Choose what needs training.
Some problems are better addressed with retrieval, clearer instructions or conventional software. We compare those options before recommending fine-tuning. Frequently changing facts can stay in a retrieval system rather than being baked into model weights.
Training is useful when a model needs consistent behavior on a well-defined task. It does not replace access controls, current source material or review of consequential decisions.
FROM SCOPE TO EVALUATION
Know what changed.
Test whether it helped.
- Define the job.Agree on the intended users, workflow, output format, failure cases and acceptance tests before selecting a training run.
- Review the data.Establish its source, permissions and suitability. Separate training and evaluation examples, check for duplication and identify information that should not be included.
- Compare adaptation options.Assess base models and methods such as supervised fine-tuning or adapters against a baseline. Model and dataset licenses remain part of that decision.
- Evaluate the result.Measure the proposed workflow on held-out examples, including errors, unsupported answers and cases that need a human handoff. Check the target hardware and runtime separately.
- Plan deployment and maintenance.Discuss local, private infrastructure or hosted options, versioning and rollback. The project scope sets the deliverables, support and any ongoing costs.
Your data needs
a clear agreement.
Private-data training requires explicit permission and separate project terms. Data access, processing locations, retention and deletion need to be settled before a dataset is transferred or a training run begins.
Please describe the type and approximate size of your data in the first enquiry. Do not send confidential files, passwords, personal records or other sensitive material through the contact form.
Useful evidence,
including the limits.
We want to know whether the adapted model improves the task you care about. A useful evaluation compares it with the starting model and practical alternatives, using examples it did not train on.
We do not promise a particular accuracy, training cost or completion date before reviewing the scope. Our published training results describe specific experiments; they are not a performance guarantee for a new project.
PROJECT ENQUIRIES
Tell us what
you are building.
Include the task, the data you can lawfully use, the hardware or deployment environment you have in mind and any budget or timing constraints. We can use that to discuss a workable scope.
Bespoke training is separate from our planned dynamic-weights personalization and public model releases. An enquiry does not reserve compute or commit either party to a project.