Most AI projects stall between proof-of-concept and production. We focus on the part nobody talks about: getting a model into your live systems, monitored and maintained, within weeks rather than quarters.
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Four areas where we spend most of our time. Each one solves a specific bottleneck we see again and again in mid-sized organisations.
We train regression and classification models on your historical data, test them against hold-out sets, and package them as REST endpoints your existing apps can call. Typical turnaround is four weeks from kick-off to a staging environment you can test yourself.
From ticket classification and contract parsing to customer-intent detection in chat logs, we fine-tune transformer-based language models on your domain vocabulary. The models run on your infrastructure or ours, depending on your data-residency requirements.
Quality-control cameras on a production line, aerial imagery analysis for agriculture, document OCR for back-office automation: we handle annotation, model training and edge deployment. Our standard pipeline supports ONNX export for Jetson and similar devices.
A model is only as good as the data feeding it. We build extraction, transformation and loading pipelines that keep your feature stores fresh, monitor for data drift, and alert you before accuracy degrades. We work with Airflow, dbt, and Spark, among others.
Five concrete steps, each with a deliverable you can review before we move on.
We spend one to two days examining your existing data sources: schema quality, volume, labelling coverage, and any regulatory constraints. You receive a written report scoring each source on readiness.
Many AI projects fail because they optimise the wrong metric. We define the target variable, agree on success thresholds, and document edge cases before anyone writes a line of training code.
Within two weeks we deliver a working prototype: a notebook or lightweight API backed by a baseline model. This lets your team test assumptions early and give feedback while changes are still cheap.
We containerise the model, write integration tests, set up CI/CD, and connect monitoring dashboards. Latency budgets, failover behaviour and rollback procedures are documented in a runbook your ops team can follow.
After launch we track prediction drift, data-quality anomalies and throughput. Retrain triggers fire automatically when accuracy drops below the threshold you chose in step two. We stay available for quarterly model reviews.
Numbers we can share publicly, with client permission.
A clothing retailer asked us to reduce garment returns caused by wrong sizing. We trained a collaborative-filtering model on purchase and return history. Within three months of deployment, return rates on recommended sizes dropped by 34 percentage points compared to the control group.
A regional insurer processed claims manually, averaging 22 minutes per document. Our NLP classifier now routes 78% of incoming claims to the correct department in under 40 seconds, freeing adjusters to focus on complex cases.
Sensor data from 140 delivery vans fed a gradient-boosted model that predicts component failures seven days ahead. Unplanned breakdowns fell by 61% in the first year, saving roughly £120,000 in towing and emergency-repair costs.
A food-packaging plant needed to catch seal defects before products left the facility. Our convolutional-network system, running on two edge GPUs, inspects 1,200 units per minute and flags defects with 97.2% accuracy, verified over a six-month audit window.
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.
53 Hillside Road, Waters-under-Funk, Wales, ST20 6UX, United Kingdom
