Document intelligence
Extract structured data from invoices, contracts and regulatory filings using fine-tuned transformer models. Our pipelines handle scanned PDFs, handwritten notes and multi-language layouts without manual templates.
Welsh-founded, globally deployed
We write AI software that actually ships
Most AI projects stall in the proof-of-concept stage. We build production-grade intelligent systems — from document understanding pipelines to real-time anomaly detection — and we stay until they are running in your stack, not ours.
See what we can build together
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// capabilities
Intelligent systems, not science experiments
Every engagement starts with a measurable business outcome. We then work backwards to the simplest model architecture that achieves it — because the best AI software is the kind your team can maintain after we leave.
Predictive analytics
Forecast demand, churn probability or equipment failure windows with ensemble models that retrain automatically as your data shifts. We integrate directly with your data warehouse — no CSV uploads required.
Real-time anomaly detection
Monitor transaction streams, sensor feeds or network traffic for deviations that matter. Our streaming architecture processes events in under 50 milliseconds, flagging genuine anomalies while suppressing false positives.
Conversational AI
Deploy domain-specific chatbots and voice agents grounded in your knowledge base. We use retrieval-augmented generation to keep answers accurate and hallucination rates below one percent in production.
Computer vision
Quality inspection on manufacturing lines, shelf-stock monitoring in retail, or aerial survey analysis — our vision models run on edge devices or in the cloud depending on your latency and bandwidth constraints.
MLOps and model governance
Already have models but struggling to keep them healthy? We build monitoring dashboards, automated retraining pipelines and audit trails that satisfy regulators — so your data science team can focus on new problems instead of firefighting old ones.
// evidence
Numbers from the field
Logistics provider — South Wales
A regional parcel distributor was losing roughly eight percent of revenue to mis-sorted shipments. We deployed a computer-vision sorting assistant that reads damaged or partially obscured labels in real time. Within ten weeks the error rate dropped to under one percent, and the system paid for itself inside the first quarter.
87%Fewer mis-sorts
10 wkConcept to production
3.1×First-year ROI
Financial services firm — London
Compliance analysts were spending over forty hours per week reviewing transaction alerts, most of which were false positives. Our anomaly-detection model, trained on five years of labelled data, reduced the alert volume by sixty-three percent while catching two previously undetected fraud patterns in its first month of operation.
63%Fewer false alerts
2New fraud patterns found
40 hrsWeekly analyst time saved
// how it works
From problem statement to production in five stages
We follow a deliberately linear process because scope creep kills AI projects faster than bad data does. Each stage has a defined exit gate — if the numbers do not justify continuing, we tell you before you waste another pound.
Discovery audit
We review your data estate, interview stakeholders and define a single measurable KPI the project must move.
Rapid prototype
A lightweight model trained on a sample of your data, tested against the KPI, delivered in two weeks or fewer.
Production build
Infrastructure, API design, security review and integration with your existing systems — typically four to six weeks.
Monitored launch
We deploy behind a feature flag, watch live metrics for two weeks and tune thresholds before full rollout.
Handover and support
Documentation, runbooks, training sessions and an optional retainer for ongoing model health checks.
// questions
Things people usually ask
No. Many of our clients have no in-house data scientists at all. We handle everything from data engineering through model deployment. If you later want to build an internal team, we can structure the handover to support that transition, including pair-programming sessions and architecture walkthroughs.
Most real-world data is messy. Our discovery audit explicitly assesses data quality and coverage gaps. In many cases we can augment sparse datasets with synthetic data generation or transfer learning from pre-trained models, reducing the volume of labelled examples you need by an order of magnitude.
Discovery audits are fixed-fee. Production builds are scoped as fixed-price milestones after the prototype stage, so you always know the total commitment before signing off. We do not bill by the hour because it creates perverse incentives to move slowly.
Absolutely. We deploy on AWS, Azure, GCP or on-premise infrastructure depending on your requirements. We also support hybrid setups where sensitive data stays on your servers while compute-heavy training runs in the cloud behind a VPN tunnel.
Every deployment includes a monitoring layer that tracks prediction drift, data distribution shifts and latency. When metrics cross a threshold, the system can trigger automated retraining or alert your team. Our optional support retainer covers quarterly model health reviews and priority bug fixes.
We sign a data-processing agreement before any data transfer. All data is encrypted at rest and in transit, access is logged and auditable, and we delete project data within thirty days of engagement close unless you instruct otherwise. We are registered with the ICO and comply fully with UK GDPR.
// talk to us
Start a conversation
Whether you have a well-defined brief or just a hunch that AI could help, drop us a line. We respond within one business day — no chatbot gatekeepers, no sales funnel, just a real person who understands the technology.
Visit us
673 The Glebe, Upton Ritchie-Danielington, Wales, XB88 8GY, United Kingdom
Ring us
+44 20 4724 5936
Email us
[email protected]