Generative AI, built to survive production
Generative AI for environments where being wrong is expensive. We do not compete on speed to demo; we compete on getting a model into production that still holds up when an auditor asks how it reached a decision.
What we build
The technologies we specialise in
Five model families do most of the work in enterprise Generative AI. Which one fits is an engineering decision, not a preference.
| Large Language Models | GPT variants and open-weight equivalents for natural language understanding and production, grounded against your own sources. |
|---|---|
| Transformer models | Beyond text: applied to vision and multi-modal tasks where sequence modelling wins. |
| Diffusion models | High-fidelity generation of text, image or sound by simulating a random diffusion process. |
| Generative Adversarial Networks | Synthetic data where authentic data is scarce, restricted, or cannot leave a boundary. |
| Variational Autoencoders | Unsupervised learning for enhancement and restoration tasks on existing datasets. |
A method built for projects that must not fail
What an engagement produces
| Typical first engagement | A discovery and feasibility assessment, ending in a prioritised use-case roadmap your board can hold us to. |
|---|---|
| Deliverables | Architecture, evaluation harness, model cards, risk register, and a running system in your environment. |
| Runs on | AWS, Microsoft Azure, Google Cloud or IBM watsonx, inside your tenancy. |
| Governance | Aligned to ISO 42001 and your internal risk framework; ISO 27001-certified delivery. |
| Commercials | TO BE SUPPLIED BY NQ |
Where this has run
Fraud detection in financial transactions
Real-time transaction monitoring where every decision stays reconstructable from the evidence that produced it.
AI employee assistant and contact centre automation
Agentic retrieval over internal knowledge, with human escalation designed in.
KYC due diligence automation
Document-heavy onboarding compressed without losing the audit trail.
Related services
Frequently asked
How long before we see something running?
A discovery and feasibility assessment typically concludes with a prioritised roadmap, and a first production-grade use case follows from there. The honest answer depends on the state of your data and how many approvals sit between a model and a real user; we tell you which of those is the constraint before you commit.
Do our data or documents leave our environment?
No. We deploy inside your tenancy on your hyperscaler of choice. Where a hosted model is used, the boundary and retention terms are agreed in writing before any data moves, and recorded in the risk register.
How do you stop the model inventing answers?
Retrieval against controlled sources, citations back to the originating document, evaluation harnesses that test for it before release, and human escalation on the paths where being wrong is expensive. Fabrication is a design problem, not a model quirk.
What does ISO 42001 alignment mean in practice?
An AI management system: documented model inventory, risk assessment per use case, defined human oversight, monitoring in production and periodic review. It is the difference between one working model and an organisation that can run many of them safely.
Will you work with our existing teams?
Yes. We build with your engineers so the system stays maintainable after we leave, and the evaluation harness is handed over with it.
Is this the right instrument for your problem?
A feasibility assessment tells you whether Generative AI fits, what it would cost to govern, and what would have to be true about your data first.