We build AI software that actually gets used

Most AI projects die in a proof-of-concept folder. Ours run in production, processing live data, saving hours every week for teams of 20 to 2,000. If your spreadsheet has outgrown itself, we should talk.

Show me what my data can do
Engineers reviewing AI model outputs in a bright modern office
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Models in production
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Data rows processed daily
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Average uptime %
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Years delivering AI software
What we actually solve

Four problems we hear every week

"Our data team built a model, but nobody trusts the output."

Trust comes from transparency. We instrument every prediction with confidence scores, feature-importance breakdowns and plain-language explanations your operations manager can read without a statistics degree.

"We tried an off-the-shelf tool and it doesn't fit our workflow."

Generic platforms force you to reshape your process around their UI. We design AI software that slots into the tools your staff already open every morning: your ERP, your CRM, your scheduling board.

"The pilot worked great on historic data, then flopped on live data."

Concept drift is real. Our deployment pipeline includes automated retraining triggers, data-quality gates and performance monitors that page your team before accuracy drops below the threshold you set.

"We can't justify the cost without a clear payback timeline."

Before writing a line of code, we map the financial impact: hours saved, error rates reduced, revenue recovered. If the numbers don't work on paper, we tell you, and we don't charge for that conversation.

From question to production

Five steps, no mystery

1

Data audit

We review what you collect, where the gaps are, and whether the question you want answered is the right one. Takes about a week.

2

Feasibility score

A short document with a probability estimate, expected accuracy range and cost bracket. No jargon, no 80-page deck.

3

Prototype

A working model on a small data slice, testable by your team within three to four weeks. Real inputs, real outputs.

4

Integration

We connect the model to your systems via API, batch job or embedded widget. We handle auth, rate limiting and error handling.

5

Monitor and retrain

Ongoing performance dashboards, automated retraining and a quarterly review call with our lead engineer. You own the model and the code.

Proof, not promises

Recent outcomes from our AI software

Automated warehouse sorting line
Logistics — parcel sorting

Mis-sort rate cut by 74 %

A regional courier was losing parcels to manual sorting errors. We trained a computer-vision classifier on their label format and integrated it with the conveyor PLC in under six weeks.

74 % fewer mis-sorts
Analyst reviewing anomaly detection dashboard
Fintech — transaction monitoring

False-positive alerts down 58 %

Their compliance team spent half the day reviewing flagged transactions that turned out to be legitimate. Our gradient-boosted model reduced noise without increasing missed fraud, saving roughly 120 analyst-hours per month.

120 hours saved monthly
Hydroponic greenhouse with sensor equipment
Agriculture — yield prediction

Harvest forecast accuracy: 91 %

A vertical-farming startup needed reliable yield forecasts to manage supply contracts. Combining sensor telemetry with weather-API data, our time-series model hit 91 % accuracy at the 14-day horizon.

91 % forecast accuracy
Common questions

Things people ask before signing

Most engagements land between £15,000 and £80,000 depending on data complexity, integration depth and whether you need ongoing retraining. The initial data audit and feasibility score cost a flat £2,500, which we credit against the project fee if you proceed.
No. We handle model development, deployment and monitoring. What we do need is someone who understands your business process well enough to tell us what a good prediction looks like. That person is usually an operations lead or a product manager.
Python (scikit-learn, PyTorch, XGBoost) for modelling, FastAPI or gRPC for serving, Docker and Kubernetes for orchestration. We deploy to AWS, Azure or GCP based on what you already use. Infrastructure-as-code is Terraform. Everything is version-controlled and reproducible.
You do. We transfer the repository, the model artefacts and all documentation at project close. If you later want to bring maintenance in-house, we run a handover session with your engineers at no extra cost.
The feasibility report arrives within two weeks. A testable prototype follows in three to four weeks after that. Full production deployment typically happens eight to twelve weeks from kickoff, depending on integration scope.
Almost all real-world data is messy. We build cleaning and imputation pipelines as part of every project. If the data truly cannot support the question you want answered, we flag that in the feasibility stage so you don't waste budget.
Get in touch

Let's figure out if AI software makes sense for your problem

Describe what you're trying to predict, automate or understand. We'll reply within one working day with an honest assessment.

32 Rodriguez Side, Berniering, England, EU49 7IY, United Kingdom

+44 76 6163 3507

[email protected]