Most AI projects stall between the proof of concept and production. We pick them up at that exact point and get them running inside your existing systems, usually within six weeks.
Based in England, working with teams across the UK and northern Europe since 2019.
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Our process
We have refined this sequence over dozens of engagements. Each stage has a fixed deliverable and a hard deadline so neither side drifts.
5 working days
We review your data infrastructure, existing models (if any), and business objectives. The output is a 12-to-15-page report that maps every gap between where you are and where a production AI system needs to be. No slides, no fluff.
2 to 3 weeks
We build a working prototype against your real data. This is not a Jupyter notebook demo; it is an API endpoint your developers can call. We test it with edge cases drawn from your actual operations so the numbers mean something.
2 to 4 weeks
The model moves into your stack. We handle containerisation, monitoring hooks, and rollback logic. Your ops team gets a runbook written in plain language, not academic jargon. Deployment targets include AWS, Azure, GCP, or on-premises Kubernetes clusters.
Ongoing, monthly retainer
Models decay. Data distributions shift. We set up drift detection, retrain on schedule, and report accuracy metrics every month. If something breaks at 2 a.m., our on-call engineer responds within 30 minutes.
What we do
Already have a model in production? We test it for bias, accuracy degradation, and security vulnerabilities. Our audit covers fairness metrics across protected characteristics as required by the UK Equality Act, plus latency and cost profiling. You receive a graded scorecard and a prioritised fix list.
We build time-series models that predict customer demand, inventory needs, or staffing requirements. Typical accuracy improvement over spreadsheet-based planning: 18 to 34 percent, measured on your last 12 months of actuals. The model retrains weekly on fresh data without manual intervention.
From quality inspection on a manufacturing line to document classification in a legal workflow, we design and deploy vision models tuned to your domain. We handle annotation strategy, model selection (YOLO, EfficientNet, or custom architectures), and edge deployment on NVIDIA Jetson or equivalent hardware.
A model is only as good as the pipeline feeding it. We design ETL workflows, set up feature stores, and connect fragmented data sources into a single, version-controlled lake. Tools we use daily: dbt, Apache Airflow, Snowflake, BigQuery, and Postgres.
Chatbots that actually understand context, summarisation engines for long-form reports, and sentiment classifiers for customer feedback. We fine-tune open-source large language models on your proprietary corpus so the output sounds like your organisation, not a generic assistant.
Common questions
It depends on the task. A demand forecasting model typically needs 18 to 24 months of transactional records. A classification model can sometimes work with as few as 500 labelled examples if we use transfer learning. During the diagnostic audit we tell you exactly what volume and quality threshold your specific use case requires.
Yes. We deploy on AWS, Azure, and GCP regularly. If you run on-premises infrastructure we can work with Kubernetes or Docker Swarm setups. We do not require you to migrate anywhere; the model fits your environment, not the other way around.
The diagnostic audit is a fixed fee of £3,200. Prototype and production phases are scoped per project, usually ranging from £12,000 to £45,000 depending on complexity. Monthly retainers for monitoring start at £1,800. We quote a firm price after the audit, not before.
We do, but only when a chatbot is the right tool. Many companies ask for a chatbot when what they actually need is a better search engine or a structured FAQ. We will tell you honestly which approach saves more support hours, then build that.
All work on regulated data happens inside your own infrastructure. We never copy production data to our machines. For healthcare and financial services clients we follow ISO 27001 controls, sign data processing agreements, and can operate under your organisation's VPN. We have completed projects under FCA and ICO scrutiny without issue.
Get in touch
Tell us what you are trying to solve. We will reply within one working day with an honest assessment of whether AI is the right approach and, if so, what the first step looks like.
487 Gleichner Wood, West Botsford-Miller, England, CK83 3GN, United Kingdom
