Most AI projects stall somewhere between a promising demo and production. We take yours from messy data to a system your team actually uses, on time and within budget.
Tell us what's slowing you down
Each engagement starts with a single question: what decision are you trying to make faster or more accurately? Everything follows from the answer.
We train regression and classification models on your historical data, then package them as API endpoints your existing software can call. Demand forecasting, churn prediction, anomaly detection: the maths changes but the delivery pattern stays the same.
A model is only as good as the data feeding it. We design extraction, cleaning, and transformation pipelines that run on schedule, log every anomaly, and alert your team before bad data reaches the model. Most clients see a 40% reduction in data-related incidents within eight weeks.
From document classification to conversational agents, we fine-tune large language models on your domain vocabulary. A legal firm we worked with cut contract review time from four hours to twenty-five minutes per document.
Quality inspection on a manufacturing line, shelf audit in retail, damage assessment for insurance claims. We build image recognition pipelines that run on edge devices or in the cloud, depending on your latency and cost constraints.
Not sure where to start? We run a two-week diagnostic: interview stakeholders, catalogue your data assets, score each potential use case by impact and feasibility, then hand you a prioritised roadmap with cost estimates. No obligation to hire us for the build.
We follow the same five-phase framework on every engagement. It keeps scope tight and surprises rare.
A 45-minute video call where we listen. You describe the problem, the data you have (or think you have), and the outcome that would make the project worth it. We take notes and follow up within 48 hours with a written assessment.
We connect to a sample of your data, run basic statistical profiles, and determine whether the signal is strong enough to model. If it isn't, we say so. Around one in five prospects hear "this won't work yet" at this stage, along with specific advice on what data to collect before trying again.
In three to six weeks we deliver a working prototype: a model, an API, and a simple dashboard showing predictions against ground truth. You test it with real decisions and tell us where it fails.
We containerise the model, add monitoring, write integration tests, set up automated retraining schedules, and deploy to your cloud environment (AWS, Azure, or GCP). Your ops team gets full documentation and a runbook.
Models drift. Data changes. We offer monthly retainer plans that include model performance reviews, retraining runs, and a guaranteed four-hour response time for critical issues.
Big firms assign a partner to win the deal, then hand execution to juniors cycling through on two-week rotations. We work differently. The person on the discovery call is the person writing your model code.
Our team is small on purpose: nine engineers, each with at least seven years of applied machine learning experience. No project managers relaying messages between you and the people doing the work.
Pricing is fixed-fee per phase. You approve each phase before we start, and you own every line of code we write. If we fall behind schedule, the overage cost is ours.
A strategy audit runs between £8,000 and £15,000. A prototype sprint for a single model usually falls in the £20,000 to £45,000 range. Full production deployment, including pipeline engineering and monitoring, can reach £60,000 to £120,000 depending on complexity. We quote per phase, so you always know the number before committing.
Most data is. The feasibility check in phase two exists specifically to surface gaps early. Sometimes the fix is straightforward: merging two databases, filling nulls with a sensible default, or adding a data collection step to an existing workflow. Other times the honest answer is to wait six months while you accumulate better records. We will tell you which scenario applies.
Not at the start. We handle model development and deployment. What you do need is someone technical enough to own the system once it is live: a backend developer or DevOps engineer who can restart containers, read logs, and escalate when something looks wrong. If you plan to iterate on models in-house later, we can help you hire and onboard that team.
Logistics, financial services, healthcare diagnostics, e-commerce, and manufacturing. The underlying techniques transfer well across sectors; what changes is the domain knowledge. We spend the first week of every project learning your industry's vocabulary, regulations, and failure modes.
All work happens within your cloud tenant or on encrypted, access-controlled environments we provision in the UK. We sign a Data Processing Agreement before touching any personal data. Anonymisation and pseudonymisation are standard steps in our pipeline templates. If your use case involves sensitive categories under GDPR Article 9, we bring in a specialist data protection consultant for a joint review.
Describe the problem you are trying to solve. We will reply within one working day with an honest assessment of whether AI is the right tool for it.
Address
242 Bridget Croft, Long Gusikowskiford, Northern Ireland, NY61 6YS, United Kingdom
Phone
+44 300 560 9649
Email
[email protected]
