Most AI projects stall between prototype and production. We skip the slide decks and go straight to working code. If your team has a data problem and needs a system that runs in the real world, that is exactly what we do.
Talk to an engineer about your dataEvery project we take on starts with a concrete question: what decision are you trying to make faster, or what task is eating hours of skilled human time? Here are the patterns we see most often.
Invoices, contracts, lab reports. Your team copies data from PDFs into spreadsheets by hand. We build extraction pipelines that read, classify, and structure those documents automatically. One logistics client reduced their processing time from 12 minutes per invoice to under 8 seconds.
→ Document intelligenceDemand planning, staffing schedules, inventory reorders. When the spreadsheet model breaks, someone guesses. We train predictive models on your historical data and deploy them as internal tools your planners actually use day to day.
→ Predictive modellingA defect spotted at the end of the production line costs ten times more than one caught early. We connect computer vision systems to existing cameras on your floor. The model flags anomalies in real time and sends alerts before the batch is ruined.
→ Visual inspectionSupport tickets pile up. Response times slip. We build retrieval-augmented generation systems that draft accurate replies using your own knowledge base, so agents review and send rather than write from scratch. Average handling time drops by around 40%.
→ Conversational AI
We follow a fixed sequence. No phase starts until the previous one has a clear deliverable you can review.
We look at what you actually have: databases, CSVs, API feeds, scanned paper. We assess volume, quality, and gaps. You get a written report with a feasibility score and a list of what needs fixing before any model training begins.
Typical duration: 1–2 weeksA narrow-scope model trained on a representative sample of your data. We test it against a holdout set and share the numbers with you: precision, recall, latency, edge cases. If the results don't justify continuing, we tell you and you stop paying.
Typical duration: 3–5 weeksThe model gets wrapped in an API, connected to your existing systems, and deployed on infrastructure you control. We write monitoring hooks so you can see throughput, error rates, and drift over time. Documentation ships with the code.
Typical duration: 4–8 weeksData changes. Business rules shift. We offer quarterly model reviews and retraining cycles. You can also run retraining yourself using the pipeline we hand over.
Ongoing, billed monthlyAggregate figures across 38 completed projects since 2021. Individual results depend on data quality and scope.
We are generalists in method but specific in application. Each project requires domain immersion, so we spend the first week learning your vocabulary and constraints before writing a line of model code.
A proof of concept usually runs between £8,000 and £18,000 depending on data complexity. Full production builds range from £25,000 to £80,000. We quote fixed prices after the data audit, not hourly rates that balloon.
No. We hand over documented, containerised systems that your developers or DevOps staff can maintain. If you have data scientists, great, we collaborate with them. If not, we design the system so a general-purpose engineer can operate it.
That is what the proof-of-concept phase is for. We define success criteria with you before training starts. If the model does not meet those thresholds, we share the analysis, explain why, and you decide whether to iterate or stop. You only pay for work completed.
Yes. We routinely work inside client VPNs and on-premise clusters. For regulated industries like healthcare and finance, we never require data to leave your environment. We can also operate within air-gapped setups if needed.
The data audit takes one to two weeks. After that, a proof of concept is typically ready in three to five weeks. So within about seven weeks of starting, you have a working model you can evaluate against real inputs.
AWS, Azure, and GCP. We also deploy to bare-metal servers and edge devices when the use case requires low latency or offline operation. The deployment target is decided during the production build phase based on your existing infrastructure.
Describe your problem in a few sentences. We will reply within one working day with an honest assessment of whether AI is the right tool for it. No sales pitch, no obligation.
