Most AI projects stall because the model never connects to real operations. We ship production-grade systems, typically inside twelve weeks, that integrate with the tools your team already uses.
Talk to our engineers about your data
Models deployed to production
Median time from kick-off to launch
Client retention after first project
Industries served since 2021
Each engagement starts with your data, not a product demo. We pick the smallest model that solves the problem, because inference costs matter long after launch day.
We train gradient-boosted and time-series models on your historical records, then wrap them in a REST API your existing dashboards can query. Typical use: demand forecasting for retail and logistics clients who need daily SKU-level predictions refreshed every four hours.
Invoices, contracts, compliance forms: we fine-tune layout-aware transformer models (LayoutLMv3 or Donut, depending on volume) to extract fields with over 96% accuracy. The output lands in your ERP or document management system via webhook, no manual re-keying required.
When a task involves reading email, checking a database and writing a reply, a well-prompted LLM agent can handle it. We build these agents on open-weight models hosted in your own cloud tenancy so sensitive data never leaves your infrastructure.
Regulated sectors like finance and healthcare often cannot use public cloud endpoints. We containerise models with ONNX Runtime, set up GPU scheduling on your hardware and hand over a runbook your ops team can follow without us.
Data drift will degrade any model eventually. We instrument prediction endpoints with distribution checks and alert your team when accuracy drops below a threshold you set. Quarterly retraining cycles keep performance stable without surprise costs.
We avoid long discovery phases that burn budget before any code is written. Here is the sequence we follow for most projects.
We spend two days in your systems, profiling tables, checking label quality and estimating what accuracy is realistic. You get a written report, not a slide deck.
A working prototype on a sample of your data, delivered in three to four weeks. If the numbers do not justify going further, we stop and you pay only for the PoC.
The model is hardened, containerised and connected to your live data sources. We write integration tests and load tests before anything touches real traffic.
Documentation, a recorded walkthrough, and 90 days of included support. After that, you can run the system independently or keep us on a lightweight retainer.
Our clients tend to be companies with 50 to 500 employees that have enough data to benefit from machine learning but not enough headcount to build an internal AI team.
A logistics firm in the Midlands came to us after their spreadsheet-based demand model started costing them £40k a month in overstock. We replaced it with a LightGBM pipeline that reduced excess inventory by 31% in the first quarter.
A legal services company needed to extract clause types from thousands of legacy contracts during a merger. Manual review would have taken six paralegals eight weeks. Our document model processed the backlog in four days with 97.2% field-level accuracy.
These are not showcase demos. They are production systems still running today, maintained by the client's own staff with occasional input from us.
If your question is not here, the contact form below goes straight to an engineer, not a sales inbox.
It depends on the task. For tabular prediction (churn, demand, pricing) we usually want at least 5,000 labelled rows. Document extraction can work with as few as 200 annotated samples if the layout is consistent. During the data audit we will tell you honestly whether the volume is sufficient or whether we should collect more before spending on model training.
Both. For structured data we rely on scikit-learn and XGBoost. For language tasks we often start with open-weight models like Mistral or Llama and fine-tune them on your domain vocabulary. If a hosted API (OpenAI, Anthropic) genuinely fits better and your data governance allows it, we will recommend that route instead. We have no vendor lock-in incentive.
A data audit runs between £2,500 and £4,000. A full proof-of-concept to production cycle for a single model ranges from £18,000 to £55,000 depending on data complexity and integration depth. We quote fixed price after the audit so there are no open-ended invoices.
Yes. We have deployed on AWS, Azure and GCP. If you are on-premise only, we bring our own portable tooling and leave you with Docker images that run on any Linux host with an NVIDIA GPU.
We agree on a minimum accuracy threshold before the production build starts. If the PoC does not hit that number, we either iterate on feature engineering at no extra charge for one additional cycle, or we recommend stopping. You keep all code and data artefacts either way.
Describe what you are trying to automate or predict. We will reply within one working day with an honest assessment of whether AI is the right tool.
939 Market Place, Miller-on-Homenick, England, VR46 8BT, United Kingdom
We typically respond within one working day. If your enquiry involves sensitive data, let us know and we will set up an encrypted channel before you share anything.