Proven AI Creations — Intelligent Software Built Through Real Milestones

PHASE 01 — DIAGNOSIS

Understanding What Already Exists

Before a single model is trained, we audit your current systems, data flows, and decision bottlenecks. Most organisations have more usable data than they realise — and more broken assumptions than they admit. We map both.

"They found three redundant data sources our own IT team had overlooked for two years." — Operations Director, logistics firm, Belfast

PHASE 02 — FRAMING

Defining the Right Problem

AI fails when it solves the wrong question. We run structured workshops to isolate the business outcome you actually need — not the one that sounds most impressive in a board meeting. This phase typically takes five to eight working days and produces a decision brief, not a slide deck.

PHASE 03 — PROTOTYPING

Building the Smallest Useful Thing

We prototype with real data on constrained timelines. The goal is a working proof-of-concept that stakeholders can interrogate — not a demo that only works under ideal conditions. If the prototype fails, we document why and pivot before budget is spent on scale.

Fourteen of our last twenty prototypes survived to production. The six that didn't saved their sponsors an estimated £380,000 in misdirected development.

PHASE 04 — ENGINEERING

Production-Grade AI, Not Science Projects

Once a prototype proves its worth, we engineer it for reality: monitoring, fallback logic, retraining schedules, data drift detection, and integration with your existing stack. Our engineering phase includes adversarial testing — we deliberately try to break what we've built.

PHASE 05 — HANDOVER & STEWARDSHIP

Your Team Runs It. We Stay Close.

We transfer knowledge aggressively. Documentation, pair sessions with your developers, runbooks for edge cases. After handover, we offer stewardship retainers — not to create dependency, but to catch model decay and shifting data patterns before they become incidents.

"Six months after handover, their stewardship team caught a drift issue that would have corrupted our forecasting for Q4." — Head of Analytics, retail group

Capability Matrix

CAPABILITY	TYPICAL USE	DELIVERY WINDOW	TEAM SIZE
Predictive Modelling	Demand forecasting, churn prevention, risk scoring	6–10 weeks	2–3 specialists
Natural Language Processing	Document classification, sentiment analysis, extraction pipelines	4–8 weeks	2 specialists
Computer Vision	Quality inspection, asset monitoring, document digitisation	8–14 weeks	3–4 specialists
Recommendation Engines	Product suggestions, content personalisation, next-best-action	5–9 weeks	2 specialists
AI Strategy & Audit	Readiness assessment, vendor evaluation, roadmap design	2–4 weeks	1–2 advisors
MLOps & Infrastructure	Pipeline automation, model monitoring, retraining orchestration	4–12 weeks	2–3 engineers

Working Principles We Don't Compromise

Data Honesty First

If your data can't support the model you want, we'll say so before you spend a penny on engineering. We've turned away work when the data wasn't ready.

Explainability Is Non-Negotiable

Every model we deploy comes with plain-language explanations of what it does, why it makes the decisions it makes, and where it might be wrong.

No Vendor Lock-In

We build on open standards wherever possible. If you decide to part ways, your models, your data, and your documentation come with you.

Measured Outcomes, Not Vanity Metrics

We agree on success criteria before work begins. If we can't define how to measure impact, we redesign the project scope until we can.

A Moment from the Field

In late 2023, a mid-sized pharmaceutical distributor approached us with a forecasting problem that had resisted two previous vendor attempts. Their stock-out rate was running above 11%. After a seven-week engagement — diagnosis, framing, and a tightly scoped predictive model — stock-outs dropped to under 3.5% within the first quarter of deployment. The model now runs autonomously with quarterly stewardship reviews.

What Clients Actually Said

HEAD OF DIGITAL — MANUFACTURING FIRM, DERRY

"Other vendors showed us flashy demos. Proven AI showed us a spreadsheet of risks and a plan for each one. That's why we signed."

Outcome: Predictive maintenance model reduced unplanned downtime by 29% in year one.

CTO — FINTECH STARTUP, LONDON

"Their honesty during the prototype phase saved us from building something our data couldn't support. We pivoted to a simpler model that actually worked."

Outcome: Fraud detection accuracy improved from 74% to 91% with a leaner architecture.

Questions We Get Asked

How do you price engagements?

We price by phase. Diagnosis and framing are fixed-fee. Prototyping and engineering are scoped after framing, with a ceiling agreed before work begins. Stewardship retainers are monthly. We don't do time-and-materials billing without a cap.

What if our data isn't clean or well-organised?

That's common and expected. Part of our diagnosis phase includes a data quality assessment. We'll recommend remediation steps and can assist with data engineering if needed, but we won't pretend dirty data will produce reliable models.

Do you work with organisations outside Northern Ireland?

Yes. We're based in Northern Ireland but work with clients across the UK and Ireland. Most collaboration happens remotely with periodic on-site workshops for diagnosis and framing phases.

Can you integrate with our existing technology stack?

We design for integration from the start. During diagnosis, we map your current infrastructure and design deployment paths that work with what you already have — cloud, on-premise, or hybrid.

What happens if a prototype fails?

We document the failure, explain why it happened, and recommend next steps — which might be a redesigned approach, a different data strategy, or an honest recommendation to pause. You only pay for the work completed, not for a promised outcome.

Start a Conversation

Tell us what you're trying to solve. We'll tell you honestly whether we can help.

Direct contact:

Phone: +44 55 8080 2002

Email: [email protected]

Office:

86 The Crescent
Upton Pouros-Schmeler Court
Northern Ireland
DY8 8MW
United Kingdom

AI Software Services Shaped by Real Milestones, Not Marketing Promises