We build Artificial Intelligence that actually ships

Most AI projects stall between proof-of-concept and production. Ours don't. We take models from notebook to live API in weeks, not quarters, so your team sees measurable gains before the budget review.

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High-performance computing infrastructure for Artificial Intelligence workloads
87
Models deployed to production
14
Industries served
6 wk
Average time to first deployment
99.4%
Uptime across hosted endpoints

What we deliver

Four practice areas, each led by engineers who have shipped production AI at scale.

Predictive analytics

We train regression and classification models on your historical data, then wrap them in monitored REST endpoints. Typical use cases include demand forecasting for retail, churn prediction for subscription platforms, and credit-risk scoring. Each model ships with a drift-detection dashboard so you know the moment accuracy starts to slip.

Natural language processing

From intent classification in customer-support tickets to entity extraction from legal contracts, our NLP pipelines handle English, multilingual, and domain-specific text. We fine-tune transformer models rather than prompting generic chatbots, which means higher precision and lower per-query costs once the model is live.

Computer vision

Quality inspection on a manufacturing line, vehicle counting from CCTV feeds, medical-image segmentation: these are the kinds of problems we solve daily. Our vision models run on edge devices when latency matters or in the cloud when you need to process thousands of images per hour.

MLOps and infrastructure

Already have models but struggling with versioning, retraining, or monitoring? We set up CI/CD pipelines for machine learning, containerised serving, and automated retraining triggers. The result is a system your own engineers can maintain long after our engagement ends.

Data science team analysing Artificial Intelligence model results

Outcomes you can measure

We tie every engagement to a business metric before writing a single line of code. If the metric doesn't move, we adjust scope at no extra cost until it does.

Here are a few recent wins from clients who gave us permission to share numbers:

  • A Midlands logistics firm cut mis-routed parcels by 34% using our address-parsing model, saving roughly £180,000 per year in re-delivery costs.
  • An online fashion retailer increased average basket value by 12% after we deployed personalised product-ranking on their search results page.
  • A NHS-adjacent diagnostics lab reduced slide-review time by 40% with our tissue-classification model, freeing pathologists to focus on ambiguous cases.
  • A Manchester fintech reduced false-positive fraud alerts by 58%, which meant fewer blocked transactions and happier customers.

How an engagement works

Five steps from first conversation to production model. No phase lasts longer than three weeks.

Scoping

We review your data, define the target metric, and agree on success criteria in a half-day workshop.

Data audit

Our engineers profile your datasets for quality gaps, bias risks, and labelling needs before any modelling begins.

Prototyping

We build a baseline model, test it against your metric, and share a live demo within ten working days.

Hardening

The model gets containerised, load-tested, and wrapped in monitoring. We write runbooks for your ops team.

Handover

We deploy to your infrastructure, train your staff, and stay available for 30 days of post-launch support.

Questions we hear often

It depends on the task. For tabular prediction problems like churn or demand forecasting, a few thousand labelled rows is usually enough to build a useful baseline. Computer vision tasks typically need at least 500 annotated images per class. During the scoping workshop we assess what you have and, if it falls short, we help you set up a labelling pipeline or augment with synthetic data.
No. About half of our clients are mid-market companies with 50 to 500 employees. The scoping phase scales down for smaller teams: we might run a two-hour call instead of a half-day workshop, and we can host the model on our managed infrastructure so you don't need dedicated DevOps staff.
Most engagements fall between £15,000 and £80,000 depending on data complexity, the number of models, and whether we host the production endpoint. We quote a fixed price after the scoping workshop so there are no surprises. Ongoing hosting and monitoring, if you choose our managed option, is billed monthly.
Yes. You own the trained weights, the training code, and all documentation. We deliver everything in a Git repository with a Docker Compose file so your team can rebuild the environment from scratch. The only exception is any proprietary tooling we license from third parties, which we disclose upfront.
All client data stays within UK-region data centres unless you explicitly request otherwise. We sign a data processing agreement before any transfer, and we delete all copies within 14 days of project completion. For regulated industries like healthcare or finance, we can work inside your own VPC so the data never leaves your network.

Reach out

Whether you have a clear project brief or just a hunch that AI could help, we are happy to talk it through. There is no obligation and no hard sell.

32 McLaughlin Hill, Manchester M1 5AN, Greater Manchester, United Kingdom
+44 161 252 2889
[email protected]