What a recent client told us

"We asked AI Plus Trust to reduce our invoice processing time. Within six weeks, their model cut it from eleven minutes per invoice to under forty seconds. Our finance team now focuses on exceptions, not data entry."
— Procurement director, Cardiff-based logistics firm (2025 engagement)

How we bring Artificial Intelligence into your business

Every engagement follows a story. Here is the typical arc, from first conversation to measurable results and long-term support.

Week 0

Discovery call and data audit

We start with a 45-minute call. No slides, no jargon. You describe the problem; we ask about your data, your team and your constraints. After the call we review a sample of your data (anonymised if needed) and write a one-page feasibility note within three working days.

If the project is not a good fit for AI, we say so. About one in four enquiries ends here, which saves everyone time and money.

Weeks 1–2

Scope and commercial agreement

We produce a detailed scope document: what the model will do, what data feeds it, how accuracy will be measured, and what infrastructure is required. The commercial proposal is fixed-price for the build phase, with optional ongoing support billed monthly.

Team reviewing AI project scope in a modern office
Weeks 3–5

Data preparation and baseline model

Cleaning, labelling and structuring your data usually takes longer than training the model itself. We handle the pipeline engineering so your team doesn't need to learn new tools. By the end of week five we have a baseline model running against a held-out test set, and we share the first accuracy metrics with you.

"They found duplicates in our CRM we'd missed for years. The data prep alone was worth the engagement." — Operations lead, Bristol SaaS company
Weeks 6–8

Iteration, testing and stakeholder review

We run two or three iteration cycles. Each one improves precision, recall or latency based on what matters most to your use case. Stakeholders get access to a live demo environment where they can test predictions against real scenarios and flag edge cases.

This is where trust gets built. People who were sceptical about AI start to see it handle the messy cases they thought only a human could manage.

Week 9

Deployment and integration

We deploy to your existing cloud infrastructure or provision a managed environment. API endpoints are documented, authentication is configured, and monitoring dashboards go live on day one. Rollback procedures are tested before the model touches production traffic.

Months 3–12

Monitoring, retraining and expansion

Models drift. Data changes. We monitor prediction quality weekly and retrain on fresh data quarterly or when accuracy drops below an agreed threshold. Many clients expand into a second use case once they see the first one working — document classification leads to automated routing, demand forecasting leads to dynamic pricing.

"Twelve months in, the churn-prediction model still outperforms the rule-based system it replaced. Retraining keeps it sharp." — Head of data, Swansea insurance firm
42projects delivered since 2021
9 weeksmedian time to production
91%of clients renew support

Capability map

Predictive analytics

Demand forecasting, churn prediction, lead scoring. We build regression and classification models on your historical data using gradient-boosted trees or neural networks depending on dataset size and interpretability requirements.

Natural language processing

Document classification, sentiment analysis, entity extraction, summarisation. We fine-tune large language models on your domain vocabulary so the output is specific to your industry rather than generic.

Computer vision

Defect detection on production lines, document digitisation, object counting in aerial imagery. We train convolutional networks and deploy them on edge devices or cloud endpoints depending on latency needs.

Data engineering

Pipeline design, ETL automation, data quality monitoring. Many AI projects fail because the data isn't ready. We fix that first, then build the model on a solid foundation.

AI strategy consulting

Not every problem needs a neural network. We help leadership teams identify which processes benefit from AI, estimate ROI and build an internal roadmap that avoids vendor lock-in.

MLOps and support

Continuous integration for models, automated retraining pipelines, drift detection alerts. We keep your AI systems healthy after launch so your team can focus on using the outputs.

How we think about AI differently

Most AI consultancies lead with the technology. We lead with the decision. What decision does your team make repeatedly that could be faster, more consistent or better informed? That question shapes everything we build.

A model that is 96% accurate but sits unused because nobody trusts it has zero value. So we spend as much time on explainability, change management and user interface design as we do on training algorithms. The people who use the system need to understand why it recommends what it recommends.

We also refuse to build AI that replaces judgment where judgment matters. Our systems augment human decision-making. They flag anomalies, rank options and surface patterns. The human still decides.

Data scientist reviewing model performance metrics on screen
Technology and nature blending in a Welsh landscape

Based in Wales, working across the UK

Our office is at 462 Julio Vale, Long Stracke-Corwin, Wales, LY5 4CJ. We work with clients from Swansea to Edinburgh, mostly remotely, with on-site workshops when the project benefits from face-to-face collaboration. Call us on +44 70 2467 9027 or email [email protected].

Start a conversation

Is your project a good fit?

You have historical data

At least six months of structured records — transactions, tickets, sensor readings, documents. The more data, the better the model, but we've built useful classifiers on as few as 2,000 labelled examples.

Strong fit for predictive and NLP work

You have a repeatable decision

A process someone performs dozens or hundreds of times a week following roughly the same logic. Approvals, categorisations, prioritisations — these are ideal candidates for AI augmentation.

Strong fit for automation and classification

You need a strategy first

You know AI could help but aren't sure where to start. We run a two-day workshop that maps your operations, identifies high-value opportunities and produces a prioritised roadmap with cost estimates.

Start with a strategy engagement

You already have a model that underperforms

Maybe an internal team built something that works in testing but fails in production, or accuracy has degraded over time. We audit existing models, diagnose the root cause and either fix or rebuild them.

Rescue and optimisation engagement

Tell us about your challenge

We respond within one working day. No automated replies.

Thank you. We'll be in touch within one working day.

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Last updated: January 2026.

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Last updated: January 2026.

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