Practical Artificial Intelligence that fits your business

We build models, pipelines and automated workflows for companies that want measurable results from their data, not a science project that never ships.

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Data scientist sketching neural network architecture on glass whiteboard
47
Models deployed to production
12
Industries served
93%
Client retention rate
6 weeks
Average first delivery

What we actually build

Every project starts with your data and your bottleneck. We pick the simplest technique that solves the problem, whether that is a fine-tuned language model or a logistic regression.

Predictive analytics

We train regression and classification models on your historical records to forecast demand, churn, equipment failure or credit risk. Most projects use gradient-boosted trees or lightweight neural nets. You get a live dashboard and an API endpoint your existing software can call.

Document intelligence

Invoices, contracts, medical letters, planning applications: we extract structured data from unstructured files. Our pipeline combines OCR, named-entity recognition and a validation layer that flags low-confidence extractions for human review. One insurance client cut manual processing time from fourteen hours a week to ninety minutes.

Custom language models

Off-the-shelf chatbots hallucinate because they know nothing about your domain. We fine-tune open-weight language models on your internal knowledge base, product catalogue or support ticket archive so the answers are grounded in fact. Retrieval-augmented generation keeps the model honest.

Workflow automation

Sometimes the biggest gain is not a fancy model but a well-designed pipeline. We connect your CRM, accounting tool and email with automated triggers, data validation and exception handling. Where a decision step needs intelligence, we slot in a lightweight classifier. Where it does not, a simple rule does the job.

Why most AI projects fail, and how ours don't

Research from multiple industry surveys puts the failure rate of enterprise AI projects between 70% and 85%. The reasons are almost always organisational, not technical.

Common traps

  • Starting with a technology ("we need a chatbot") instead of a business question
  • Training on messy, unlabelled data and hoping the algorithm will sort it out
  • Building a prototype that never reaches production because nobody planned for integration
  • Ignoring data governance until a regulator asks questions
  • Treating the launch as the finish line, with no monitoring or retraining schedule

How we work differently

  • We define the metric you care about before writing a single line of code
  • Our data audit takes two to five days and produces a written quality report
  • Every model ships with an API, monitoring dashboard and a retraining playbook
  • We document data lineage and consent pathways from day one
  • Post-launch support includes monthly performance reviews for the first year

From first call to live system

A typical engagement runs six to twelve weeks. Larger programmes with multiple models follow the same phases but cycle through them iteratively.

Discovery

We interview stakeholders, map data sources and agree on a success metric. This phase produces a one-page project brief and a rough cost estimate.

Data preparation

Our engineers clean, label and version your data. If volumes are small, we design augmentation strategies or identify external datasets that can fill the gap.

Build and validate

We train candidate models, run cross-validation and test against a held-out set. You see results in a shared notebook before anything goes near production.

Deploy and monitor

The chosen model is containerised, deployed behind an API and connected to your systems. Alerts fire if accuracy drifts below the agreed threshold.

Edinburgh rooftops at golden hour representing our Scottish base

Common questions

How much data do we need before a project is viable?+

It depends on the task. A classification model for five categories might work well with a few thousand labelled examples. A generative language model fine-tune usually needs tens of thousands of tokens of domain text. During the discovery phase we assess what you have and tell you honestly whether it is enough, or what it would take to get there.

Do you only work with large companies?+

No. About a third of our clients have fewer than fifty employees. Smaller companies often benefit more from automation because every hour saved is felt immediately. We scale the scope and price to match.

Where does the model run?+

We default to UK-based cloud infrastructure to keep data within the jurisdiction. If you have an on-premises requirement, we can deploy to your own servers. GPU instances are provisioned only when the workload justifies the cost; many production models run fine on CPU.

What happens after the project ends?+

You own the model weights, the code and the documentation. We offer optional support contracts that cover retraining, monitoring and ad-hoc improvements. If you prefer to bring maintenance in-house, we run a handover workshop with your engineering team.

How do you handle sensitive or personal data?+

We sign a data processing agreement before any data leaves your systems. Where possible we work with anonymised or synthetic datasets during development. Our pipeline logs every transformation applied to personal data so you can demonstrate compliance with UK GDPR.

Talk to us

Describe what you are trying to achieve and we will get back to you within one working day with an honest assessment of whether AI is the right tool.

Contact details

388 Freeman Rise, Castle Wilkinson End, Scotland, ZZ67 4ZG, United Kingdom

+44 340 411 3064

[email protected]

Our office in Castle Wilkinson End, Scotland