Tell us the bottleneck. We build a model, an API, or an automation pipeline around it. No generic chatbot wrappers. No dashboards full of graphs nobody reads.
Send us your problem
What we build
Every project starts with a question: what decision are you trying to make faster? We work backward from that answer into code.
We train supervised models on your labelled data. Think fraud scoring for a payments company, defect detection on a factory line, or triage routing for a support queue. You get a containerised model with a REST endpoint, not a Jupyter notebook.
Demand planning, resource scheduling, cash-flow projection. We combine gradient-boosted trees with recurrent architectures depending on how noisy your signal is. Output plugs into your existing ERP or spreadsheet workflow.
Extracting structured data from invoices, contracts, or medical records. We fine-tune layout-aware transformers so the system handles your specific document formats, not just generic PDFs. Accuracy targets above 95% on your validation set or we keep iterating.
Some data can't leave your building. We package models into Docker images that run on your hardware with no internet dependency. Includes monitoring scripts, log rotation, and a runbook your ops team can actually follow.
We connect large language models to your internal knowledge base using retrieval-augmented generation. Where a general model hallucinates, a grounded one cites your own documents. We handle prompt engineering, embedding pipelines, and guardrail layers.
How it works
You describe the problem. We ask about data availability, volume, and what "good enough" looks like for your use case. By the end of the call we can tell you whether machine learning is the right tool or whether a simpler rule-based approach would serve you better.
We review a sample of your data under NDA. Within five working days you receive a short document: what model architecture we recommend, expected accuracy range, infrastructure requirements, and a fixed-price quote. No obligation to proceed.
Training, evaluation, iteration. You see results on a held-out test set before anything goes live. We share a dashboard where you can inspect individual predictions and flag errors that feed back into the next training run.
The model ships as an API behind your firewall or on a managed cloud instance. We set up alerting for data drift and accuracy degradation. If performance drops below the agreed threshold, we retrain at no extra charge for the first twelve months.
Questions
It depends on the task. For binary classification (spam vs. not spam, defective vs. good) we can often get useful results with a few thousand labelled examples. More nuanced tasks like multi-class document categorisation may need tens of thousands. During the scoping call we give you a realistic minimum based on similar projects we have delivered.
Python is our primary language. We build on PyTorch for deep learning, scikit-learn for classical ML, and FastAPI for serving. Infrastructure is Terraform plus Docker. If your stack uses a different language for the consuming application, we expose a JSON API that anything can call.
Yes. We have delivered projects in healthcare and financial services where data governance is strict. We sign your DPA, work inside your VPN if required, and never copy production data to our own machines unless you explicitly permit it for training purposes.
Most engagements fall between £8,000 and £40,000 depending on complexity, data volume, and whether the deployment is cloud or on-premise. We quote fixed price after the data audit so there are no surprises. Ongoing monitoring and retraining is a separate monthly retainer, usually between £500 and £1,500.
Every project includes twelve months of drift monitoring and one free retraining cycle. After that you can continue on a retainer or take full ownership of the codebase. We document everything and run a handover session with your engineering team so you are never locked in.
Get in touch
Whether you have a clear specification or just a vague hunch that a process could be automated, write to us. The initial conversation costs nothing and we are honest about cases where AI is not the right fit.