We are a small engineering team based in England. We build machine learning systems, deploy them into production, and keep them running. No slide decks. No proof-of-concept graveyards.
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Every project starts with a question: what decision are you trying to automate, and what data do you already have? Here is where most of our work falls.
Time-series forecasting, churn prediction, demand planning. We train gradient-boosted and deep-learning models on your historical data, validate them against holdout sets, and ship them behind an API your existing systems can call. Typical turnaround from raw data to first production endpoint: six to eight weeks.
Reduced forecast error by 31% for a UK logistics firmObject detection, defect classification, OCR for scanned documents. We fine-tune convolutional and transformer-based vision models on your labelled images. If you don't have labels yet, we handle annotation in-house with a two-round QA process that keeps label accuracy above 97%.
Automated visual inspection for a manufacturing line in BirminghamSentiment analysis on customer reviews, entity extraction from contracts, automated ticket routing. We build pipelines around both open-source transformer models and commercial LLM APIs, depending on your data sensitivity and latency requirements. Retrieval-augmented generation setups are something we have deployed for four clients so far, each with a different document corpus.
Entity extraction pipeline processing 12,000 contracts/dayAlready have a model but it drifts, breaks, or nobody trusts the outputs? We set up automated retraining schedules, data-quality checks, and alerting. Our monitoring stack catches feature drift before accuracy degrades. We run everything on your cloud account so you own the infrastructure from day one.
Cut model-incident response time from 9 hours to 22 minutesMost AI consultancies follow a waterfall process that drags on for months. We work in two-week sprints with a demo at the end of each one.
We spend the first week inside your data warehouse, CRM exports, or whatever you have. The goal is a one-page summary: what questions the data can answer today, what gaps exist, and a rough accuracy ceiling. This step is free for projects above £15k.
Two weeks. We build a working model on a sample of your data and present results in a Jupyter notebook you can re-run yourself. If the numbers aren't useful, we stop here and you owe nothing beyond the sprint fee.
The model gets containerised, tested against edge cases, wrapped in an API, and deployed to your cloud (AWS, GCP, or Azure). We write the integration code that connects it to your existing application or dashboard.
Dashboards for prediction volume, latency, and drift metrics go live. We train one or two people on your team to interpret the alerts. After handover, we offer a monthly retainer for ongoing retraining and support, or you can run it entirely in-house.
We have worked with 23 organisations since 2019. Eighteen of them are still on retainer. Here is what they tell us matters.
Fixed-price sprints. No ambiguous "discovery phases" that balloon into six figures. You approve each sprint before it starts, and you see working software at the end of it.
Code, models, data pipelines, documentation. All of it lives in your Git repository from sprint one. We never hold IP hostage to a support contract.
No juniors learning on your project. Our team of seven all have at least five years of production ML experience. You talk directly to the person writing the code.
It depends on the task. For tabular prediction (churn, demand), a few thousand rows with clean labels is often enough to beat a rule-based baseline. Image classification usually needs at least 500 labelled images per class. NLP tasks can sometimes work with fewer than 200 annotated examples if we fine-tune a pre-trained language model. During the data audit we give you a concrete number.
Yes. About a third of our clients run models on internal hardware for regulatory or latency reasons. We package everything in Docker containers and provide Ansible playbooks for deployment. GPU scheduling is handled through Kubernetes or, for simpler setups, a systemd service with a basic queue.
Python is the primary language for model training (PyTorch, scikit-learn, XGBoost). Production APIs are usually FastAPI or Flask behind Gunicorn. For heavier inference loads we sometimes write C++ inference servers using ONNX Runtime. Data pipelines run on Apache Airflow or Prefect, depending on complexity.
We sign a data processing agreement before any data transfer. All work happens inside your infrastructure or a dedicated, encrypted cloud environment. We never copy data to personal machines. For projects involving PII, we apply differential privacy techniques or synthetic data generation during the prototyping phase so the model never memorises individual records.
A single prototype sprint is £8,000. Full projects (audit through production deployment) range from £20,000 to £90,000 depending on data volume, model complexity, and integration requirements. Monthly monitoring retainers start at £1,500. We are happy to scope a project over a 30-minute call before any commitment.
Describe what you are trying to solve. We will reply within one working day with an honest assessment of whether AI is the right tool for it.
91 Prospect Place, Great Predovicdon, England, VG0 9JC, United Kingdom
