From fraud detection models that flag suspicious transactions in under 200 milliseconds to document classifiers that cut manual review time by 80%, our London team ships production-grade AI your business can rely on.
We started Mastermind AI Hub because we kept seeing the same pattern: companies buying expensive AI platforms they never fully used. Dashboards gathered dust. Models drifted. Nobody owned the pipeline.
Our fix is simple. We embed with your team for a two-week discovery sprint, map the data you actually have, and build the narrowest model that moves a metric you care about. No science-fair demos. If a rule-based approach works better than deep learning, we say so.
The team is twelve people: six machine-learning engineers, two data engineers, two product designers and two project leads. Everyone has shipped at least three production models before joining. We work from Clerkenwell, ten minutes from Old Street, and take on six to eight projects at a time so nobody is spread thin.
Six core capabilities, each backed by engineers who have done this work at scale before.
We train regression and classification models on your historical data to forecast demand, churn probability or equipment failure windows. Most clients see usable predictions within four weeks of kickoff, with weekly retraining pipelines running on your own cloud account.
Sentiment analysis for customer reviews, entity extraction from contracts, or chatbot backends that answer questions from your knowledge base. We fine-tune open-source transformer models so you own the weights and avoid per-token API costs at scale.
Quality inspection on manufacturing lines, shelf-stock monitoring in retail, or medical image triage. We handle annotation, model training, edge deployment on NVIDIA Jetson or cloud inference via TensorRT, and ongoing accuracy monitoring.
Good models need clean pipelines. We build ETL flows in Apache Airflow or Prefect, set up feature stores, and create data-quality checks that alert your team before bad data reaches a model. The goal: one command to reproduce any training run.
We audit existing models for bias, write model cards documenting training data and known limitations, and prepare documentation aligned with the EU AI Act risk categories. If your model touches personal data, we run DPIA workshops with your legal team.
After launch we monitor model drift, retrain on fresh data and manage A/B rollouts. Our standard retainer includes weekly performance reports, on-call incident response within two hours, and quarterly model reviews to decide whether an upgrade is justified.
Four things clients mention most when they refer us to colleagues.
Every engagement starts with a two-week sprint capped at a flat fee. By the end you have a validated proof of concept, a data-readiness report and a cost estimate for production. No open-ended research bills.
Code, model weights, training data pipelines: all of it lives in your repository from day one. If you want to bring work in-house after six months, you can. We even run a handover workshop at no extra charge.
We don't staff projects with juniors supervised by one lead. Every engineer on your project has at least five years of ML experience and has deployed models handling real traffic. That means fewer review cycles and faster iteration.
Before writing a line of code we agree on a success metric: false-positive rate, processing time, cost saved per month. If the model does not hit the target in the agreed timeframe, we extend the engagement at our cost until it does.
Real feedback from teams we have worked with over the past three years.
"They built a demand-forecasting model for our warehouse network in six weeks. Stock-outs dropped 34% in the first quarter after deployment. The weekly retraining pipeline has been running without intervention for nine months."
"We needed an NLP pipeline to extract key clauses from 15,000 lease agreements. Their team delivered a model with 94% F1 score and a review interface our legal assistants actually enjoy using. Turnaround was five weeks."
"Their computer-vision system inspects 2,400 parts per hour on our assembly line. Defect escape rate went from 1.2% to 0.15%. The ROI paid for the project in under three months."
Whether you have a clear ML brief or just a hunch that your data could work harder, reach out. We respond to every enquiry within one business day.