AI Development Agency in Edinburgh

Edinburgh has more AI and informatics research talent per square mile than anywhere else in the UK, and a financial sector with the data to use it. The gap is rarely the model — it is everything required to run it reliably. That gap is what we build.

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The Edinburgh landscape

Strong models, thin production engineering

Most Edinburgh projects that reach us have already proved the idea. What they need is the surrounding system: data contracts, monitoring, governance and a deployment story.

  • A concentration of AI, informatics and robotics research

    The research output here is genuinely world-class, and it arrives as notebooks, not services. Our work usually begins by establishing what the method actually guarantees, then rebuilding everything around it — data contracts, evaluation, versioning, monitoring.

  • Asset management, banking and insurance

    Edinburgh's financial sector holds decades of structured data and a low tolerance for opaque automation. Anything that influences a customer outcome has to be explainable and reconstructable, which shapes the model choice as much as the accuracy target.

  • Public and health data programmes

    Regional data initiatives make linked health and administrative data available for research under tight controls. Products built on that footing must treat consent and identifiability as architecture, not policy documents.

Why Edinburgh teams bring us in

We are usually the team that comes after the proof of concept. The honest version of that work is unglamorous: pinning down data quality, writing evaluation that runs continuously, versioning models so a result can be reproduced twelve months later, and putting a human decision point where the consequences warrant one.

  • AI and data science teams moving research into production
  • Asset managers, banks and insurers automating regulated processes
  • University spin-outs and deep-tech startups
  • Health and public data research programmes
  • SaaS companies embedding machine learning into an existing product

What we build for Edinburgh organisations

The engineering that stands between a working model and a working service.

Machine learning engineering

Training pipelines, feature stores, evaluation harnesses and model versioning that make a result reproducible long after the person who produced it has moved on.

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AI for financial services

Automation of regulated processes with explainability, reconstructable decisions and reporting a supervisor can follow.

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Data platforms and pipelines

Ingestion, quality checks and lineage — because almost every failing model we are handed is a data problem wearing a model costume.

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Language interfaces done carefully

Retrieval-grounded assistants with citation, refusal behaviour and evaluation, built for cases where a confident wrong answer causes real harm.

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Product build from research

Taking a validated method to a supportable product: interfaces, tenancy, deployment, and the operational surface a research prototype never has.

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Sector focus

Where our Edinburgh work concentrates

Three routes from research to something that can be operated.

Research to production

Taking a method that works in a notebook and making it survive real inputs, real load and real staff turnover.

Typical start: technical due diligence, 2-3 weeks

Regulated financial automation

Automating decisions where an explanation is a legal requirement, with evidence generated by the system rather than assembled by hand.

Typical start: decision and audit mapping, 3 weeks

Sensitive data platforms

Linked health and administrative data under strict controls, with identifiability managed explicitly at every stage.

Typical start: governance and data model workshop, 3-4 weeks

Compliance posture

Governing AI systems, not just building them

Automation that affects people needs to be explainable and reversible. We build that in rather than documenting around it.

Explainable decision paths
Inputs, model version and outputs logged so any individual decision can be reconstructed later.
Human decision points
Automation stops short of consequential decisions unless there is a reviewed case for going further.
UK GDPR and automated processing
Lawful basis, minimisation and the safeguards that apply when processing meaningfully affects an individual.
Model versioning and reproducibility
Data, code and model artefacts pinned so a result can be regenerated months later.
Continuous evaluation
Drift and performance monitored in production, with alerting when behaviour moves outside its tested range.
Scottish research governance
Approvals and identifiability controls scoped against the Scottish route where health or public data is involved.

Frequently
Asked Questions

Seeking basic information? Our FAQ section is a ready reckoner with precise answers to the most probable queries.

We have a working model. What do you actually add?

The surrounding system. Data contracts and quality checks, an evaluation harness that runs continuously, model and dataset versioning, deployment, monitoring for drift, and a defined human decision point. In our experience that is between two thirds and nine tenths of the work required to make a model into a service.

How do you handle explainability for financial services?

We log inputs, model version and outputs for every decision, prefer model families whose behaviour can be explained where the outcome affects a customer, and generate the evidence a supervisor would ask for as a system output rather than a manual exercise.

Can you work with linked health or administrative data?

Yes, under the relevant approvals. We treat identifiability as an architectural property: what is linked, at what level, retained how long, visible to whom. Those constraints go into the data model rather than into a policy document that the code ignores.

Do you build large language model features?

Where they fit. We ground them in retrieval over your own content, require citation, design explicit refusal behaviour, and evaluate against a held-out set before launch and continuously afterwards. Where a confident wrong answer causes real harm, we will say so and recommend a narrower design.

Can you help a spin-out with technical due diligence?

Yes. We assess what the research code guarantees, what it silently assumes, and what it would cost to make supportable — useful before a funding round as well as before a build.

Do you work with the rest of Scotland from Edinburgh?

Yes. We deliver across Scotland and scope health work against the Scottish governance route rather than the English one.

From notebook to production service.

Show us the model. We will tell you honestly what it takes to run it.

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Powering Your Solutions With

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TypeScript
Python
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HL7 FHIR
Flutter
TypeScript
Flutter
Python
Selenium
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HL7 FHIR
TypeScript