We build predictive models, language processors and vision systems for companies that need results measured in weeks, not quarters. Based in Scotland, working across the UK and Europe.
Talk to our engineers
Each engagement starts with a question your data can answer. Here are the areas where we have the most field experience.
Demand forecasting, churn prediction, dynamic pricing. We train gradient-boosted and deep-learning models on your transactional data, then wrap them in an API your existing stack can call. Typical accuracy uplift over rule-based systems: 18–34%.
Defect detection on manufacturing lines, document digitisation, medical-image triage. Our team has deployed convolutional and transformer-based architectures for clients processing between 5,000 and 2 million images per day.
Sentiment analysis, entity extraction, contract clause tagging, automated summarisation. We fine-tune large language models on domain-specific corpora so the system speaks your industry's vocabulary from day one.
Low-latency serving on Kubernetes or serverless runtimes. We handle model versioning, A/B testing infrastructure, and monitoring dashboards that alert you before accuracy drifts below threshold.
Feature stores, ETL pipelines, data-quality gates. A model is only as reliable as the data feeding it. We build the plumbing that keeps inputs clean and auditable, usually with dbt, Airflow and BigQuery or Snowflake.
Two-day on-site sessions where we audit your data assets, identify the three highest-ROI use cases, and produce a roadmap with cost estimates. You leave with a concrete plan, not a slide deck full of buzzwords.
We listen. You describe the business problem, the data you have, and the outcome you need. No sales pitch on our end, just questions.
Our engineers access a sample of your data under NDA, assess quality, volume and labelling gaps, then write a feasibility memo with expected accuracy ranges.
We train an initial model, expose it through a staging API, and let your team poke at it. Feedback loops are short: daily stand-ups, shared notebooks, visible metrics.
Load testing, failover configuration, monitoring hooks. The model moves into your cloud account so you own every byte. We set up retraining schedules and drift alerts.
Optional retainer for model retraining, new feature engineering, and quarterly performance reviews. Most clients keep us on for the first year, then bring the work in-house.
We replaced a rule-based routing engine with a gradient-boosted model trained on 14 months of shipment data. Mis-routes dropped by 41%, saving the client roughly £320,000 per year in re-handling costs.
A convolutional neural network inspects every sheet at line speed (1.2 m/s). False-positive rate sits at 0.7%, well below the 2% threshold the quality team set. The system paid for itself within five months.
Our model flags abnormal radiographs so reporting radiologists see urgent cases first. Median time-to-report for critical findings fell from 11 hours to under 90 minutes during the pilot.
An ensemble of LightGBM and a small transformer processes 8,000 transactions per second with a 94.6% recall at a 0.3% false-positive rate. The bank's fraud losses decreased by 29% in the first quarter after launch.
Describe your problem in a few sentences. An engineer (not a salesperson) will reply within one working day with an honest assessment of whether AI is the right tool.
9 Gudrun Parade, Upper Robel, Scotland, ZV32 6CC, United Kingdom