ModelBrew

ModelBrew is a platform for building AI models that stay current and stay governed. You clean your training data, fine-tune an open-weight model, and then keep it up to date over time — teaching it new knowledge, correcting it, and verifiably deleting information — without retraining from scratch and without the model forgetting what it already knew.

Standard fine-tuning is a one-shot event: teach a model something new and it tends to forget or degrade what it learned before, a problem called catastrophic forgetting, and there is no way to prove you removed something later. ModelBrew makes model knowledge continuous and governed, so you can keep adding and removing knowledge over the life of the model, with an audit trail for every change.

Teams that need to keep an AI model accurate and auditable over time, especially in regulated settings like finance, healthcare, and legal, where the ability to update knowledge, delete data verifiably, and produce an audit trail matters.

It is a platform built around three pillars: Clean for data quality, Tune for continual fine-tuning, and the Verifiable Knowledge Layer for a governed, erasable, auditable fact layer. Together they let a team keep a model accurate and compliant across its whole lifecycle.

CRMA, the Constrained Residual Mixing Adapter, is our patent-pending method for teaching a model new things without it forgetting the old things. The original model stays frozen, and new learning goes into a small, mathematically constrained adapter that updates the model's behavior without destabilizing what it already knows. In our preprint benchmarks, removing the constraint causes large drift across a chain of tasks, while CRMA keeps that drift close to zero.

Standard fine-tuning overwrites the model and forgets earlier tasks when you train again. CRMA lets you fine-tune repeatedly and continually, so each new task or fact stacks on top without trampling the last. One model can keep learning over months instead of being frozen at its last training run.

Preventing drift is the core of CRMA. Continual updates are mathematically constrained so the model does not silently degrade as knowledge is added, and before-and-after benchmarks measure that stability directly.

New facts supersede old ones with a timestamp and full history. If a fact changes — a price, a policy, a name — the model updates immediately and stops surfacing the outdated answer, while the change stays on the audit record.

RAG attaches a search index to a model at query time. It has no concept of forgetting, no certified deletion, and no provenance guarantee — deleting a document does not prove the model will stop using that information. The Verifiable Knowledge Layer adds exactly those: certified record erasure, source-cited answers, and a tamper-evident audit trail. It is not just retrieval; it is governed knowledge.

The governed knowledge layer answers with its source, and when it does not have a confident match it says so and abstains rather than guessing. That behavior — declining when it does not know — is a core design goal, and every abstention is logged.