Predictive analytics
Demand forecasting, churn prediction, equipment failure alerts. We pick the right algorithm for your data volume: gradient-boosted trees for tabular data under a million rows, neural architectures when you have more.
Artificial Intelligence that fits your actual business
We build prediction models, automate document workflows, and connect your existing systems with machine learning. Based in Scotland, working with companies that have real data and real deadlines.
Send us your data challenge
73
Models deployed since 2019
14
Industries served
97.2%
Average uptime on production models
6 wks
Median time from kick-off to first prototype
How a project moves from idea to production
Every engagement follows five distinct phases. We skip nothing, but we keep each phase tight so you see working software quickly.
1. Data audit
We spend two to five days inside your existing data infrastructure. What tables exist? What's missing? Where does quality break down? You get a written report with a feasibility score for each potential AI use case, ranked by expected return.
2. Problem framing
Most AI projects fail because the question is vague. We translate your business goal into a measurable prediction target. For example, "reduce churn" becomes "predict which customers will cancel within 30 days, with at least 82% precision." That precision threshold comes from a cost-benefit calculation we do with your finance team.
3. Rapid prototyping
Within three to six weeks we train a first model on your historical data. You can test it against recent cases you already know the answer to. If accuracy is below the agreed threshold, we iterate on features and model architecture before spending another pound.
4. Integration and deployment
The model connects to your live systems through a REST API or a batch pipeline, depending on latency requirements. We handle containerisation, monitoring dashboards, and alert rules so your ops team knows immediately when model performance drifts.
5. Ongoing tuning
Data changes. Markets shift. We retrain models on a schedule that matches your data velocity, whether that's weekly or quarterly. You own the code and the trained weights. If you ever want to bring maintenance in-house, we hand over documentation and run a knowledge-transfer workshop.
What we build
Six core capabilities. Each one has been delivered at least a dozen times across different industries.
Document intelligence
Invoices, contracts, survey responses: we extract structured fields from unstructured documents using fine-tuned language models. One logistics client reduced manual data entry by 89% in the first quarter after deployment.
Computer vision
Quality inspection on production lines, aerial survey analysis, retail shelf monitoring. We train object-detection and segmentation models on your labelled images, typically needing 500 to 2,000 annotated samples to reach usable accuracy.
Knowledge graphs
When your data lives across ten different systems, a knowledge graph stitches entities together so queries that used to take hours run in seconds. We use Neo4j or Amazon Neptune depending on your cloud preference.
Data pipeline engineering
Raw data in, clean features out. We design ETL and streaming pipelines in Apache Spark or dbt, with data-quality checks at every stage. No model is better than the pipeline feeding it.
AI governance and compliance
Model risk documentation, bias audits, explainability reports. If you operate in a regulated sector, we prepare the evidence pack your compliance team needs before any model goes live.
Measured outcomes from recent projects
We share numbers, not adjectives. Each figure below comes from a client engagement completed between 2022 and 2025.
Scottish food distributor
Demand forecasting model cut weekly food waste by 31%. The model predicts order volumes per SKU per depot, seven days ahead, using weather data, historical sales, and local event calendars. Payback period was eleven weeks.
Insurance claims handler
Document extraction pipeline processes 4,200 claim forms per day with 94.6% field-level accuracy. Two full-time data-entry roles were redeployed to customer-facing work. Processing time per claim dropped from 22 minutes to under three.
Offshore wind maintenance firm
Vibration-sensor anomaly detection model flagged a gearbox fault 18 days before scheduled inspection. The early warning avoided an estimated £140,000 in unplanned downtime. Model retrains monthly on new sensor readings.
Regional retail chain
Customer churn model identifies at-risk loyalty-card holders with 86% precision at 30-day horizon. Targeted retention offers now reach the right customers, reducing annual churn rate from 24% to 17%.
Common questions
Straight answers. If yours isn't here, use the contact form below.
A data audit starts at £3,500. A full prototype-to-production engagement for a single model typically falls between £18,000 and £55,000, depending on data complexity and integration requirements. We quote fixed-price after the audit phase.
No. We work inside your cloud environment or on-premises infrastructure. For the audit phase, anonymised sample data is usually enough. Production training runs on your hardware or your cloud account so data never leaves your control.
Most data is. The audit phase identifies gaps and quality issues. Sometimes the right first project is building a data pipeline rather than training a model. We will tell you that honestly, because a model trained on bad data wastes everyone's time.
That is the preferred arrangement. Your developers know the codebase and the deployment environment. We pair with them during integration, review pull requests together, and document everything so they can maintain the system after handover.
Food and beverage distribution, insurance, renewable energy, retail, property management, legal services, aquaculture, construction, healthcare logistics, fintech, public-sector housing, agriculture, maritime shipping, and higher education.
The median payback period across our completed projects is four months after the model goes live. Some projects, especially document automation, pay for themselves within weeks because the labour savings are immediate and easy to measure.
Send us your data challenge
Describe what you are trying to predict, automate, or understand. We will reply within one working day with an honest assessment of whether AI is the right tool.
Get in touch directly
95 Hegmann Lea, Cruickshank-on-Reichel, Scotland, RK99 7YM, United Kingdom
