AI built for your institution.
Trained on your data.
Running on your infrastructure.

95% accuracy on institution-specific tasks. Generic LLMs average 70%.

Generic AI answers in generalities. A model trained on your institution answers with the precision your clients, your compliance team, and your operations actually require.

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Purpose built SLM - trained on your data
95%
Purpose built SLM - trained on your data
70%
A generic AI doesn't know your credit policy. It doesn't know your AML thresholds. It doesn't know how your relationship managers describe products to clients, or which exceptions your compliance team allows, or what your onboarding process actually looks like in practice. No matter how well you prompt it, a generic model is working from general knowledge - not yours. The gap between what it produces and what your institution actually needs is not a configuration problem. It is a fundamental limitation of models that were never trained on your data.

What do we build

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Purpose-built small language model

A 1–20 billion parameter model designed specifically for your institution's domain. Small enough to run on a single GPU. Precise enough to outperform models many times its size on your specific tasks.

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Fine-tuning on your data

Your model is trained on your policies, your products, your historical interactions, and your institutional knowledge — so it answers the way your best people answer, at any scale.

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On-premise deployment

The model runs entirely within your infrastructure. No data leaves your environment. No per-query costs. No third-party dependencies to manage under DORA or the EU AI Act.

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Ongoing maintenance and retraining

As your products evolve, regulations change, and new data accumulates, we retrain and maintain the model so accuracy holds over time — not just at launch.

How it works

(01)

Process and data assessment

We map the workflows, data sources, and institutional knowledge that will define the model's scope. This is where we identify the highest-value starting point for your organisation.

(02)

Model development and fine-tuning

We build and train a purpose-built SLM on your data — iterating until accuracy, tone, and institutional fit meet your requirements.

(03)

On-premise deployment

We deploy the model within your infrastructure, integrate it with your existing systems, and validate performance in your production environment.

(04)

Maintenance and retraining cycles

We manage ongoing model performance — monitoring accuracy, retraining on new data, and adapting the model as your institution evolves.

Ready to Own Your AI?

Stop renting generic models. Start building specialized AI that runs on your infrastructure, knows your business, and stays under your control.

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