"We replaced three manual reporting steps with a single AI pipeline. The team got Friday afternoons back."Operations lead, Cardiff logistics firm
"Their document classifier cut our intake processing from forty minutes per case to under five."Practice manager, Swansea legal group
"No jargon, no hype. They showed us a working prototype in ten days."Finance director, Bristol SaaS company
Most organisations know they should be using artificial intelligence somewhere. The question is where, and whether the cost of getting it wrong outweighs the benefit of getting it right. We build AI software that answers both questions at once: small, testable systems deployed against a single bottleneck, measured against a single metric you already track.
We are based in Wales. Our clients range from 15-person specialist firms to 200-seat operations teams across the UK. The common thread is data they already collect but struggle to act on quickly enough. Inventory forecasts that arrive a week late. Support tickets that sit in a queue because nobody can categorise them fast enough. Compliance documents that need a human eye on every page.
Each engagement starts with a two-week diagnostic. We look at your data, your tooling, and the one process that hurts the most. If AI is the wrong answer, we say so. If it is the right answer, you see a working model before you sign a long-term contract.
Not every inefficiency needs a neural network. Here is how we evaluate fit before writing a single line of code.
You need enough historical examples for a model to learn patterns. For classification tasks, that usually means at least a few hundred labelled records. For forecasting, twelve months of weekly data points is a reasonable floor.
AI pays for itself when the decision it automates happens often. A routing choice made fifty times a day is a better candidate than a strategic call made once a quarter.
Some mistakes are cheap to fix; others are not. We map the cost of a wrong prediction against the cost of the current manual process. If the margin is thin, we add a human-review step and build confidence thresholds into the system.
Our models connect to your existing stack via REST APIs, scheduled batch jobs, or direct database hooks. If your systems are locked behind legacy interfaces, we scope the integration work separately so there are no surprises.
Someone on your side needs to own the output. We train that person during deployment and provide documentation written for non-engineers. No PhD required.
We quote fixed prices for diagnostics and prototypes. Production deployments move to monthly retainers with defined scope. You will never receive a surprise invoice.
Extraction, classification, and summarisation of contracts, invoices, compliance filings, and correspondence. We use a mix of fine-tuned language models and rule-based post-processing to hit accuracy targets above 95% on structured document types.
Time-series models trained on your sales, logistics, or resource-usage data. Outputs feed directly into your ERP or planning spreadsheet. One retail client reduced overstock write-offs by 22% in the first quarter after deployment.
Ticket triage, intent detection, and suggested responses for support teams. The system learns from your resolved tickets and improves as your agents accept or reject its suggestions.
Real-time monitoring of transaction streams, sensor feeds, or log data. Alerts fire when patterns deviate from learned baselines. Useful for fraud screening, equipment maintenance, and quality control.
When off-the-shelf tools do not fit, we train models from scratch on your data. Computer vision for product inspection, NLP for domain-specific text, recommendation engines for internal knowledge bases. The scope is set during the diagnostic.
A 30-person law practice in South Wales was spending roughly six hours a day sorting incoming case documents by type and urgency. We trained a document classifier on 1,200 labelled samples from their archive. Processing time dropped to forty minutes a day, and the accuracy rate after three months sat at 96.3%.
A distributor near Newport needed better weekly stock predictions for 4,000 SKUs. Their old method was a spreadsheet updated monthly. Our time-series model, retrained weekly on fresh sales data, cut stockout events by 31% and freed the planning team to focus on supplier negotiations instead of guesswork.
A Bristol SaaS company routed every support email manually. We built an intent classifier that suggests a category and priority level. Agents override when needed, and the model learns from corrections. After eight weeks, 78% of suggestions were accepted without changes, shaving about ninety seconds off each ticket.
The industry has a habit of selling transformation. We sell reduction: fewer manual steps, fewer errors, fewer hours spent on tasks a model can handle. Transformation is a side effect, not a promise.
Every project we take on has a single success metric agreed before work begins. It might be processing time per document, forecast accuracy measured as MAPE, or the percentage of support tickets correctly routed on first pass. If we do not hit the target within the agreed timeframe, we extend the engagement at no extra cost until we do, or we refund the prototype fee.
We do not chase trends. Large language models are useful for some tasks and wasteful for others. A well-tuned gradient-boosted tree will outperform a billion-parameter model on many tabular datasets, and it costs a fraction to run. We pick the tool that fits the problem, not the one that sounds impressive in a pitch deck.
A quick self-check. If you can answer yes to at least three of these, a conversation with us is likely to be productive.
Tell us what you are working on. We will reply within one business day with an honest assessment of whether we can help.
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Last reviewed: January 2026.
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