Predictive analytics
Forecast demand, churn, or equipment failure using models trained on your historical data. We typically deliver a validated prototype within four weeks, with accuracy benchmarks you can verify against past records.
Why it matters
The gap between AI hype and AI results
Plenty of companies buy AI tools. Few get measurable returns. Here is what usually goes wrong, and how we fix it.
Common pitfalls
- Data scattered across incompatible systems with no clear labelling
- Off-the-shelf models trained on someone else's domain
- Proof-of-concept demos that never reach production
- Internal teams left without documentation or transfer training
Our approach
- A structured data audit before any model work begins
- Custom architectures sized to your actual data volume
- Deployment pipelines with monitoring baked in from day one
- Hands-on workshops so your team can maintain the system independently
What we deliver
Core AI services
Each engagement is scoped around a single measurable objective. We do not sell hours; we sell outcomes.
Natural language processing
Extract meaning from support tickets, contracts, or social feeds. Our NLP pipelines handle Welsh and English text, classify intent, and surface patterns that manual reading would miss entirely.
Process automation
Repetitive decisions eat staff time. We build rule-and-model hybrids that handle invoice routing, quality checks, or scheduling, then flag exceptions for human review. Typical time saving: 60 to 80 percent of manual effort on the target task.
Computer vision
From defect detection on production lines to document digitisation, our vision models run on standard hardware. We fine-tune pre-trained backbones on your labelled images, keeping training costs low while achieving production-grade accuracy.
Data engineering
Good models need clean pipelines. We design ETL workflows, set up feature stores, and unify fragmented databases so your data is model-ready before a single line of training code runs.
How an engagement works
From first call to live system in four stages
No stage begins until the previous one has a signed-off deliverable. You stay in control at every checkpoint.
Discovery
We spend one to two days on-site or remote, mapping your data landscape, interviewing stakeholders, and defining a success metric. The output is a written feasibility report with a go/no-go recommendation.
Prototype
A working model trained on a representative subset of your data. You see accuracy scores, confusion matrices, and a live demo within three to five weeks. If the numbers do not justify proceeding, we stop here and you owe nothing further.
Production
The validated model gets containerised, integrated with your existing stack via API or batch pipeline, and deployed with automated retraining triggers. We handle infrastructure provisioning on your cloud account or on-premises servers.
Handover
Your team receives full documentation, a runbook for common failure modes, and two half-day training sessions. We remain available for 90 days of post-launch support at no extra charge.
Who we are
A small team that ships
We are seven engineers and data scientists based in Wales. Before founding Ai Clear Intents in 2020, we spent a combined 40 years building ML systems inside large organisations: financial services, healthcare, logistics. We got tired of watching good research die in Jupyter notebooks.
Our size is deliberate. Every project is led by a senior practitioner, not delegated to juniors after the sales call. You talk to the people who write the code.
We hold ISO 27001 certification and are registered with the ICO. All client data stays within UK or EEA infrastructure unless you explicitly request otherwise.
Questions we hear often
Frequently asked questions
It depends on the task. A classification model for structured tabular data can work well with a few thousand rows. Image recognition typically needs several hundred labelled examples per category. During the discovery phase, we assess your data volume and tell you honestly whether it is sufficient or whether we should augment it first.
Discovery starts at £2,500. A full prototype-to-production engagement for a single use case usually falls between £18,000 and £55,000, depending on data complexity and integration depth. We quote fixed prices after discovery, not open-ended day rates.
No. We integrate with what you already use. Our models expose a REST API or consume data from your existing pipelines. If you run SAP, Salesforce, or a custom ERP, we build connectors rather than asking you to migrate.
All work happens inside your infrastructure or on dedicated instances in UK data centres. We sign a DPA before any data transfer. Personal identifiers are pseudonymised during model training unless the use case explicitly requires them, and raw data is deleted from our systems within 30 days of project close.
Absolutely. Most clients begin with the discovery phase and a single prototype. If the results justify it, we expand scope. There is no obligation to commit to a full production build until you have seen the prototype perform on your own data.
Get in touch
Tell us what you are working on
Office
3 Rebecca Wood, Lower Romaguera-Waters, Wales, GA26 0ZU, United Kingdom
Phone
We respond to enquiries within one working day. If your request is urgent, phone is fastest.