[ 01 ]
Data and a measurable baseline
Review usable data, permissions, quality, and coverage. Separate development examples from evaluation cases and establish the performance of a suitable starting model.
[Custom LLM Development & Training]
Custom language models.
Built for your work.
Language models adapted to your domain and tasks through data preparation, custom training, fine-tuning, evaluation, and private deployment.
[ Where This Fits ]
Built around the requirement.
Specialized terminology, document formats, and business decisions need more than a generic chat interface. We build and evaluate the model workflow against examples of the work you need it to perform.
- Assistants that need to understand a company's terminology and approved information.
- Document extraction, classification, or drafting with consistent output requirements.
- Private model deployment with a repeatable process for evaluating later updates.
[ What We Build ]
The system, end to end.
[ 02 ]
Adaptation and training
Use domain fine-tuning, custom training, retrieval, or a combination suited to the task. Training scope follows the available data, compute budget, deployment licence, and measured benefit.
[ 03 ]
Serving and integration
Build the model service and connect it to the application workflow. Define access, input and output formats, review steps, model versions, and the deployment environment.
[ Example System Design ]
An assistant for technical service reports.
An example system design: approved reports teach the expected terminology and structure, while a controlled reference library supplies current equipment information.
[ 01 ]
Prepared examples
Authorized records are cleaned and split into development and held-out evaluation sets.
[ 02 ]
Model adaptation
Fine-tuning targets the report format and terminology; retrieval supplies current references.
[ 03 ]
Workflow integration
The application produces a draft with source context for a person to review.
[ 04 ]
Versioned releases
A candidate model must pass agreed checks before it replaces the current version.
[ Acceptance ]
Agree what success means.
- Compare the adapted system with its baseline on held-out, representative tasks.
- Test factual support, output format, unsuitable inputs, and cases where the model should defer.
- Measure response time, resource use, and repeatability in the intended deployment environment.
[ Source & Handover ]
Ownership and continued development.
Scope can include commissioned training and inference code, data preparation tools, evaluation cases, configuration, and the model weights or adapters permitted by the selected licences. The agreement defines ownership, data handling, and the process for retraining or support.
Project delivery and ownership[ Project Questions ]
Before we begin.
- Do we need to train a model from scratch?
- That depends on the problem. We assess whether adapting an existing model, retrieving company information, or a larger training project is justified by the data, budget, and acceptance criteria.
- What data do we need to provide?
- Useful inputs include representative tasks, approved answers or outputs, terminology, and authorized source material. An initial data review identifies missing coverage and what preparation is needed.
- Can our team run and maintain the result?
- Yes. Private deployment and technical handover can be part of the commission. We specify the infrastructure, software dependencies, model licences, and maintenance responsibilities before delivery.
[ Contact ]
Start with the requirement.
Share the task, a description of your available data, and how you would judge a useful result. Confidential examples can follow an agreed sharing process.