Retail demand forecasting
A 200-store fashion retailer reduced overstock by 23 percent in six months after we replaced their spreadsheet-based planning with a gradient-boosted time-series model fed by POS, weather, and event data.
Artificial Intelligence that fits your business, not the other way around
We build AI systems your team can actually use. Predictive models, language processing, document automation: each one shaped around the workflows you already have.
Talk to our engineers
What we build
Each engagement starts with your data and your goals. We do not sell off-the-shelf dashboards dressed up as AI.
Predictive analytics
We train regression and classification models on your historical records to forecast demand, churn, pricing shifts, or equipment failures. Most clients see measurable accuracy within the first month of deployment.
Computer vision
Object detection for quality control on manufacturing lines, automated document scanning for insurance claims, and aerial image analysis for agriculture. We handle labelling, training, and edge deployment.
Natural language processing
Chatbots that resolve tier-one support tickets, sentiment analysis pipelines for social listening, and entity extraction from legal contracts. We fine-tune large language models on your domain vocabulary so responses stay relevant.
Workflow automation
Connecting your AI models to the tools your team uses daily: Salesforce, SAP, internal ERPs, spreadsheets. We write the integration layer, the monitoring alerts, and the fallback logic for when a model returns low-confidence scores.
AI strategy consulting
Not every problem needs a neural network. We audit your data estate, score use-cases by ROI, and map a twelve-month roadmap so your investment goes where it matters most.
Model retraining and monitoring
Models drift. We set up automated retraining pipelines with data validation gates, performance dashboards, and alerting so accuracy stays high long after launch day.
Why most AI projects stall, and how we avoid that
Research from Gartner puts the failure rate of enterprise AI projects around 85 percent. The usual culprits: unclear objectives, poor data quality, and models that never leave a Jupyter notebook.
We counter each one with a structured engagement model.
- A two-day discovery sprint defines the business question before anyone writes code.
- Data profiling happens in week one so we surface gaps early, not after three months of development.
- Every model ships with an API, documentation, and a monitoring dashboard your ops team can read without us.
- We train two internal champions on your side to own the system after handover.
The result: production-grade AI that runs without our constant involvement. You pay for outcomes, not for ongoing dependency.
How an engagement unfolds
Four phases, clear deliverables at each gate.
01
Scope and data audit
We meet your stakeholders, review available datasets, and agree on a single measurable KPI the model must improve. Duration: one to two weeks.
02
Prototype
A working proof-of-concept trained on a sample of your data. You see real predictions, real accuracy metrics, and a candid assessment of feasibility. Duration: three to five weeks.
03
Production build
We harden the model, build the integration layer, run stress tests, and deploy behind your firewall or on your preferred cloud. Duration: four to eight weeks depending on complexity.
04
Handover and support
Documentation, champion training, and a ninety-day support window. After that, optional retainer for retraining and monitoring.
Outcomes from recent projects
Anonymised summaries. Full case studies available on request.
Insurance claims triage
An NLP classifier now routes incoming claims to the right handler within seconds. Average handling time dropped from 14 minutes to 3. The insurer processed 40 percent more claims per day without adding headcount.
Manufacturing defect detection
A convolutional neural network inspects aluminium castings on a conveyor belt at 120 parts per minute. False-positive rate sits below 0.4 percent, saving the client roughly £18,000 per month in scrap costs.
Questions we hear often
Do we need a large dataset to get started?
Not necessarily. Some techniques work well with a few thousand records. During the discovery sprint we assess volume and quality. If your data is too sparse, we can suggest augmentation strategies or alternative modelling approaches that need less training data.
Where does the model run?
Wherever you want. We deploy to AWS, Azure, GCP, or on-premise servers. For edge use-cases like factory inspection cameras, we package models into lightweight containers that run on local hardware with no internet dependency.
How do you handle sensitive data?
All work happens inside your infrastructure or a dedicated, encrypted environment. We sign NDAs and DPAs before accessing any data. For healthcare or financial services clients, we follow sector-specific compliance frameworks including ISO 27001 controls.
What does a typical project cost?
Prototype engagements start at around £12,000. Full production builds range from £30,000 to £120,000 depending on integration complexity and the number of models. We provide a fixed-price quote after the discovery sprint so there are no surprises.
Can you work alongside our internal data team?
Yes. About half our projects are collaborative. We join your Slack or Teams, attend standups, and commit to your repo. Knowledge transfer is built into every sprint, not tacked on at the end.
Let's discuss your project
Describe what you are trying to achieve. We will reply within one working day with an honest assessment of whether AI is the right tool, and if so, a rough scope and timeline.
Phone: +44 7494 479899
Email: [email protected]
Address: 738 Chapel Street, Old D'Amore-Tremblay, Wales, AJ9 9GP, United Kingdom