We design machine learning systems for mid-size UK companies. Not proofs of concept that gather dust, but production models that run every day, process real transactions, and pay for themselves within months.
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Demand forecasting, churn prediction, credit scoring. We train gradient-boosted and neural models on your historical records and serve predictions through a REST API your existing software can call. Typical accuracy improvements over rule-based systems sit between 18 and 35 per cent.
Invoices, contracts, medical letters, compliance forms. Our OCR and NLP pipeline extracts structured data from messy documents, classifies them, and routes them into your workflow. One logistics client cut manual data entry by 80 per cent within the first quarter.
We map relationships across your data: products linked to suppliers linked to defect reports linked to customer complaints. Graph queries surface patterns that flat tables hide. Fraud detection, supply chain risk, and recommendation engines all benefit from this approach.
Quality inspection on production lines, vehicle damage assessment for insurers, shelf-stock monitoring in retail. We fine-tune convolutional and transformer-based vision models on your labelled images and deploy them to edge devices or cloud endpoints depending on latency requirements.
Before any model can train, data needs to flow reliably. We build ETL pipelines with Apache Airflow or Prefect, set up data warehouses on BigQuery or Snowflake, and write the tests that catch schema changes before they break downstream processes. Clean plumbing makes good models possible.
Already have models but struggle to keep them current? We audit your ML infrastructure, introduce CI/CD for model artefacts, and set up drift-detection alerts. Retraining runs automatically when performance drops below agreed thresholds, with human approval gates for high-stakes predictions.
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AI Core Synthesis started in 2021 when two machine learning engineers got tired of watching promising prototypes die in staging environments. The gap between "works in a notebook" and "runs in production" was costing companies months of wasted effort and thousands of pounds.
Today we are eight people: five ML engineers, two data engineers, and one project manager who keeps everyone honest about deadlines. We work from 45 Swaniawski Grove, Upper Beier End, England, FB4 1YV, though most of our collaboration happens over Git and video calls.
Every project we take on must pass a simple test: can we define a measurable outcome before we write the first line of training code? If the answer is no, we help the client reframe the problem until it becomes testable, or we recommend they spend their budget elsewhere. That policy has lost us a few deals. It has also meant that 97 per cent of the clients who do start with us stay for at least two years.
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// get in touch
A short paragraph is enough. What data do you have, what decision do you want to automate, and what does a good outcome look like? We reply within two working days.