Predictive maintenance
Sensor data from pumps, turbines, or conveyor belts feeds into a model that flags failures 48 to 72 hours before they happen. One client in offshore wind cut unplanned downtime by 38 per cent in the first year.
We build Artificial Intelligence that actually ships
Most AI projects stall at the proof-of-concept stage. Ours don't. We take messy, real-world data and turn it into production systems that run 24/7 without babysitting. Based in Scotland, working with companies across the UK and Europe since 2019.
Talk to our engineers
0Models deployed
0Per cent avg. accuracy
0Industries served
0Median weeks to production
How a project moves from idea to production
We follow a four-phase workflow. Each phase has a clear deliverable and a decision gate, so you never pay for work you can't evaluate.
1. Data audit
We spend the first week inside your existing data: databases, spreadsheets, API logs, sensor feeds. The output is a written feasibility report that tells you what is possible, what is risky, and what is not worth pursuing. No code yet. If the data doesn't support the goal, we say so.
2. Prototype model
Weeks two and three produce a working prototype trained on a cleaned subset of your data. You see real predictions, real accuracy numbers, and a list of failure cases. We present this in a live session so your domain experts can stress-test the results.
3. Production engineering
The model gets wrapped in proper infrastructure: containerised deployment, monitoring dashboards, automated retraining triggers, and API endpoints your existing software can call. This phase typically takes two to three weeks depending on integration complexity.
4. Handover and support
Documentation, training sessions for your team, and a 90-day support window. After that, you can maintain the system yourselves or keep us on a lightweight retainer. We don't build dependencies that force you to stay.
What we build
Six areas where we have deep experience and reference projects we can talk about openly.
Computer vision
Quality inspection on production lines, automated document scanning, aerial image analysis for agriculture. We work with standard industrial cameras, so you don't need specialist hardware.
Natural language processing
Customer support ticket routing, contract clause extraction, sentiment monitoring across social channels. We fine-tune open-source transformer models on your own corpus rather than relying on generic APIs, which keeps sensitive data inside your infrastructure.
Demand forecasting
Retail, logistics, and energy companies use our time-series models to predict demand at the SKU or grid-node level. Typical forecast horizon is 7 to 90 days, updated daily.
Recommendation engines
Content platforms and e-commerce sites hire us to build collaborative and content-based recommenders. We've handled catalogues from 5,000 to 2 million items, with sub-100ms response times at scale.
Data pipeline design
A model is only as good as the data feeding it. We architect ETL pipelines, feature stores, and data validation layers using tools like Apache Airflow, dbt, and Great Expectations, hosted on your cloud or on-prem.
Rooted in Scotland, working everywhere
Our office sits in Turner Common, a quiet corner of Scotland that gives us space to think. Most of our team came from academic research labs at Edinburgh, Glasgow, and St Andrews before deciding they wanted to build things people actually use.
We work with clients across the UK, Germany, and the Nordics. About half our engagements are fully remote; the rest involve periodic on-site weeks. We've found that the combination works better than either extreme.
Common questions
Small projects (single-model deployment, clean data, one integration point) start around £15,000. Larger engagements with multiple models, complex pipelines, and on-prem deployment typically range from £40,000 to £120,000. We quote fixed-price after the data audit phase, so there are no surprises.
Not necessarily. We can work inside your VPN or cloud tenancy, accessing data through secure channels without ever copying it to our own systems. For clients in regulated industries like healthcare or finance, this is the standard arrangement.
Python is the backbone: PyTorch and scikit-learn for modelling, FastAPI for serving, Docker and Kubernetes for deployment. On the data side, we use PostgreSQL, Apache Kafka, and dbt depending on the situation. We avoid vendor lock-in where possible.
Yes, and we prefer it. Embedding with your developers means faster integration and better knowledge transfer. We use your version control, attend your standups, and write code that follows your style guide.
Every system we deploy includes monitoring that tracks prediction accuracy against ground truth. When performance drops below a threshold you define, an automated retraining pipeline kicks in. If the drift is structural (the world changed, not just the data), we investigate and propose a model revision.
Talk to our engineers
Describe the problem you're trying to solve. We'll reply within one working day with an honest assessment of whether AI is the right tool for it.
50 Oaklands, Turner Common, Scotland, MZ94 3WK, United Kingdom