Most AI projects stall in the proof-of-concept stage. Ours reach production in four to eight weeks, trained on your data, running on your infrastructure, solving the specific problem you described on the first call.
Send us your toughest problem
Each engagement starts with a question you need answered. We pick the technique that answers it fastest and cheapest.
Demand forecasting, churn scoring, maintenance scheduling. We train gradient-boosted trees or lightweight neural nets on your historical records, validate against a holdout set, and hand over a containerised API you can call from any system.
Invoices, contracts, regulatory filings: we extract structured data from messy PDFs using fine-tuned layout-aware transformers. One insurance client cut their claims processing queue from nine days to under two hours.
Quality inspection on production lines, aerial survey analysis, retail shelf monitoring. We label, train, and deploy object-detection or segmentation models that run on edge devices or cloud GPUs depending on your latency budget.
Sentiment classifiers for customer reviews, topic-routing for support tickets, entity extraction from medical notes. We fine-tune open-source language models so your data stays on your servers.
Inherited a model nobody understands? We reverse-engineer the pipeline, document it, benchmark it, and either fix it or replace it. About a third of our engagements start this way.
Five stages, each with a clear deliverable. You can stop after any stage and still have something useful.
A 90-minute call where we translate your business question into a machine-learning task. We write a one-page brief that defines the input data, the target variable, and the success metric. If the problem is not a good fit for AI, we say so.
We connect to your data source, run quality checks, and produce a short report: row counts, missing values, class balance, potential leakage. No data leaves your environment unless you explicitly allow it.
We train a baseline model and share results in a notebook you can re-run. You see precision, recall, and a confusion matrix on real records. If the numbers are not useful, we adjust the framing or recommend stopping.
The winning model gets wrapped in a REST API, containerised, tested with integration tests, and deployed to your cloud account. We write monitoring alerts that fire when input distributions drift.
Documentation, a recorded walkthrough, and a three-month support window. Retraining scripts are included so your team can refresh the model without calling us back.
Names changed where clients asked, but the numbers are exact.
A gradient-boosted demand model reduced over-ordering of packaging materials by 22% across 14 warehouses. The model retrains weekly on fresh sales data and has been live since March 2023.
We built a contract-clause extractor that reads lease agreements and populates a structured database. Paralegals who previously spent four hours per lease now spend forty minutes reviewing the model's output.
Drone imagery fed into a segmentation model identifies early blight in potato fields. The co-op treated 310 hectares selectively in 2024 instead of blanket-spraying, cutting fungicide costs by roughly £38,000.
A sentiment classifier processes 4,000 product reviews per day and routes negative ones to the customer-care team within minutes. Response time to unhappy buyers dropped from 19 hours to under 3.
Straight answers, no jargon where we can avoid it.
Most engagements land between £15,000 and £60,000 depending on data complexity and deployment requirements. We quote a fixed price after the problem-framing call, not an hourly rate.
Yes. About a quarter of our clients are in mainland Europe or North America. Meetings happen over video, and we access data through secure tunnels. Time-zone overlap of at least four hours is the only hard requirement.
It always is. The data-review stage exists precisely for this. We document gaps, propose imputation strategies or alternative features, and set realistic expectations before any model training begins.
Everything we produce during an engagement belongs to you: code, trained weights, documentation. We retain no licence to your data or models after the project ends.
AWS, Azure, and GCP. If you run on-premise Kubernetes, we can deploy there too. We match whatever your ops team already manages so there is no new infrastructure to learn.
We work inside your VPC or private network. Model training happens on your hardware. For healthcare and financial-services clients we follow the relevant compliance frameworks (NHS DSPT, FCA guidelines) and can sign a DPA before the first data access.
Describe the problem in plain English. We will reply within one working day with an honest assessment of whether AI is the right tool.
Address:
9 Beatty View, Wilderman Green, England, YI7 1QF, United Kingdom
Phone:
+44 334 660 2266
Email:
[email protected]
