Predictive analytics
We train regression and classification models on your historical data to forecast demand, detect churn risk, or flag anomalies before they cost money. Most projects reach production in eight to twelve weeks.
Artificial Intelligence that solves the problems you actually have
We are a small team of engineers in England. We build machine learning models, natural language pipelines, and computer vision systems for companies that need AI to do a specific job, not impress a boardroom.
Send us your toughest problem
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Models deployed since 2021
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Average prediction accuracy
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Hours of manual work saved weekly
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Repeat clients in the last 12 months
What we build
Every project starts with a question: what decision are you trying to make faster, or what task should a human stop doing by hand?
Document intelligence
Invoices, contracts, medical reports: we build extraction pipelines that pull structured data from messy documents. Our models handle handwriting, poor scans, and multi-language PDFs with confidence scores attached to every field.
Computer vision
Quality inspection on a production line, vehicle damage assessment for insurers, shelf compliance checks in retail. We fine-tune detection and segmentation models on your images and deploy them on-premise or in the cloud, depending on latency requirements.
Custom LLM integration
We connect large language models to your internal knowledge base so staff can query policies, product specs, or customer records in plain English. Retrieval-augmented generation keeps answers grounded in your actual data rather than hallucinated facts.
Data pipeline engineering
A model is only as useful as the data feeding it. We design ETL workflows, feature stores, and monitoring dashboards that keep your AI systems reliable after the initial excitement fades.
Case study
Reducing claim processing time for a Midlands insurer
A regional insurance firm was spending an average of 22 minutes per motor claim on manual data entry. Their adjusters copied information from photographs, repair estimates, and police reports into three separate systems.
We built a document extraction model trained on 14,000 labelled claim files. The pipeline reads incoming emails, classifies attachments, extracts key fields (policy number, damage type, estimated cost), and pre-fills the claims management system.
Six months after launch, the average handling time dropped to seven minutes. The model runs on a single GPU instance hosted in the UK, and the firm processes roughly 600 claims per week through it.
68%
Reduction in handling time
97.3%
Field extraction accuracy
How a project runs
Problem definition (week 1)
We sit down with the people who will use the system. Not the board, the actual users. We map the decision or task, identify what data exists, and agree on a measurable success metric before writing any code.
Data audit and feasibility (weeks 2–3)
We profile your data for volume, quality, and bias. If the signal is too weak or the labels too noisy, we tell you early. About one in five enquiries ends here because the data cannot support the goal, and we would rather say so than charge for a model that will disappoint.
Prototype model (weeks 4–7)
We train a baseline, iterate on features and architecture, and share results in a notebook your team can inspect. No black boxes.
Integration and deployment (weeks 8–11)
The model gets packaged into an API or embedded into your existing software. We write monitoring hooks so you can see accuracy, latency, and drift on a live dashboard from day one.
Handover and support
We document everything, train your team to retrain the model when new data arrives, and offer a six-month support window. After that, you own it fully. No lock-in.
Questions we hear often
How much data do we need to get started?
It depends on the task. A tabular classification problem might work well with 5,000 rows. A custom object detection model usually needs at least 1,000 annotated images per class. During the feasibility phase, we run power analyses to estimate whether your current dataset is sufficient or whether we need to augment it.
Where does the model run?
We deploy to UK-based cloud infrastructure by default (AWS London or Azure UK South). If your compliance requirements demand on-premise hosting, we package models as Docker containers that run on your own hardware. Edge deployment on NVIDIA Jetson devices is also something we have done for manufacturing clients.
What does a typical project cost?
A focused proof-of-concept with a single model and API endpoint typically falls between £18,000 and £35,000. Larger engagements involving multiple models, retraining pipelines, and integration with legacy systems range from £50,000 to £120,000. We quote fixed-price after the feasibility phase so there are no surprises.
Do you work with startups or only established firms?
Both. About a third of our clients are venture-backed startups building AI into their core product. The rest are mid-size companies (50 to 500 employees) adding intelligence to existing operations. The common thread is that they have a clear problem and data to work with.
Can you help if we already have an in-house data team?
Yes. We often embed alongside internal teams for specific sprints: model architecture review, MLOps setup, or tackling a particular accuracy plateau. We transfer knowledge as we go so your team can maintain the system independently afterwards.
Talk to us
Describe the problem. We will tell you honestly whether AI is the right tool for it.
49 Towne Lane, West Crooksworth, England, CP13 1UP, United Kingdom