A model shaped around your own data
General-purpose models are built for broad use. Model Adaptation trains or adapts one around your proprietary data, your workload and the outcome you need, then hands it over for you to run.
Scoped to your requirements and quoted per engagement. Most engagements open with a paid proof of concept, deliberately small.
General models are not shaped around you
A general-purpose model is trained for broad use. It has not seen your proprietary data, your specialist vocabulary or the way your organisation actually works.
- Your own datasets and the patterns inside them
- Specialist vocabulary and domain knowledge
- Internal processes and how your teams actually work
- The outputs and the risk profile your organisation actually needs
Model Adaptation is how that proprietary value starts contributing to the system you run, instead of sitting in a database a general-purpose model never reads.
Sized to the gap
You bring the data, the target tasks and the outcome you need. FenxLabs decides how large an intervention the gap actually requires.
- Your proprietary data
- The target tasks
- The outcome you need
Targeted fine-tuning, partial adaptation or full retraining of an open-weight model or another machine learning system, depending on the gap. We run the smallest one that closes it, not the largest one we could sell.
Use the smallest intervention that works
FenxLabs compares adaptation with a different existing model, a routing change and an already-specialised resource before recommending training.
- 01A different existing modelCandidate models tested against your workload, not a published leaderboard. Quality, cost and deployment fit compared side by side, hosted and open-weight alike.
- 02A change to the routeRouting rules that were right in March are not right in November. We re-cut them against what you now run.
- 03A specialised resourceBuilt for one kind of work: code, legal reasoning, retrieval or reranking.
When a requirement survives all three, adaptation is the most direct way to put your own data to work in the system you run.
Your own evaluation sets the proof
Your target tasks and your own evaluation set decide the answer. An adapted model has to improve the required result without adding a deployment or operating burden you would not accept.
No improvement figure is published before an evaluation against your own tasks produces one.
You keep the model and choose where it runs
Us
You
- The weights
- The evaluation set
- The deployment
You keep the weights, the evaluation set and the deployment. Training runs in your environment or in ours. What you are left with is a model that belongs to you.
Model Adaptation does not require ARC. You keep the delivered model and choose where it runs.
Put your own data to work
Thirty minutes with an engineer. Your numbers. A straight answer.