Data Foundry
Bring order to documents, tables, transactions and operational records. Curate, label and validate the data a specialised model needs.
Bespoke models
Built for your business.
Not everyone.
A small model for a specific job. Built from the knowledge your business already has, tested against the decisions it needs to make, and designed to run on your own machines.
What we build
The model is only
part of the work.
Useful AI begins with the right data, the right expertise and a way to measure what works. We bring those pieces together around one defined business problem.
Evaluation Lab
Test answers against real tasks, edge cases and failure conditions. Report accuracy, uncertainty, weak spots and measurable acceptance criteria.
Expert Network
Involve domain specialists in reviewing examples, scoring model behaviour and defining what a good decision looks like.
Model Adaptation
Fine‑tune, constrain and package smaller models for a narrow task, language, workflow or operating environment.
Synthetic Intelligence
Turn useful patterns in legacy enterprise data into synthetic training and test datasets, with privacy checks, fidelity evaluation and expert review. Use those datasets for supervised fine‑tuning, and where appropriate, reinforcement learning with verifiable rewards.
Stand‑in rowsSynthetic
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c7f2e946LS6£3,204.18
1a90d331BS3£388.70
e04b7c61CF24£26,511.09
From legacy to learning
Decades of records.
A more useful model.
Your ERP, CRM, documents and transaction history may contain patterns that a general model has never learned. We turn approved sources into a governed learning pipeline.
Understand
the recordsInventory sources, map relationships and define permitted uses.
Prepare
and protectClean, minimise, classify and apply privacy controls.
Generate
synthetic dataCreate representative examples and controlled edge cases.
Train
and refineFine‑tune with reviewed examples; use reward‑based optimisation when suitable.
Test
before releaseCheck utility, leakage risk, failure cases and human acceptance.
Synthetic data is not automatically anonymous or safe. Every engagement requires risk assessment and validation. Reinforcement learning is an optional optimisation method, not a prerequisite for synthetic data generation.
The right fit
Small enough to run here.
Specific enough to matter.
We focus on a clearly bounded task: extracting information from legacy documents, classifying claims, supporting service decisions or interpreting operational records. We choose the model, dataset and evaluation method around that job, not the other way around.
Banking and insurance
Document handling, controlled classifications and workflow assistance.
Manufacturing and logistics
Technical records, exception handling and operational decisions.
Public sector and legal
Traceable document processing and domain‑specific evaluation.
Retail and telecommunications
Specialised customer operations and structured workflows.
How we work
One job. Clear evidence.
A model you can own.
Each engagement starts with a defined outcome and ends with a documented decision on whether the model is ready for its intended use.
Define the job
Agree the task, data boundaries, target hardware and success criteria.
Build the evidence
Curate examples, involve specialists and create evaluation datasets.
Adapt the model
Choose an appropriately sized model, fine‑tune and iterate against tests.
Prove the fit
Document scores, limitations, privacy checks and deployment requirements.
Ways to engage
Start with the question.
Scale with the evidence.
Fixed scope
Evaluation Sprint
Independent task‑based evaluation of a model, including benchmark design, failure analysis and an improvement plan.
A focused first engagement
Project delivery
Data & Synthetic Project
Legacy data mapping, dataset preparation, synthetic generation and quality and privacy assessment for an agreed use case.
A governed data foundation
Longer‑term collaboration
Bespoke Model Programme
Model adaptation, expert feedback, iterative evaluation and private deployment planning for a specialised workflow.
From prototype to operationBuilt with boundaries
Your data stays
under your control.
Data access, retention, intellectual property, deployment location and review responsibilities are defined for each engagement. We can design for customer‑controlled infrastructure and restricted environments where the technical and contractual requirements permit.
- Approved sources only
- Data use and permissions established before development.
- Privacy and leakage testing
- Evaluate synthetic outputs before they are released or reused.
- Measured limitations
- Report weak spots as clearly as strengths.
- Human approval
- Keep consequential release and workflow decisions accountable.
What should your
model know?
Tell us about the task, the data you already have and where the model needs to run. We will help define a practical first step.