"We spent fourteen months building an internal ML pipeline. Foresight rebuilt it in six weeks, and the output accuracy jumped from 71% to 89%."

— Director of Operations, UK logistics firm (2025 engagement)

That result came from a team of four, not forty. The difference was not resources. It was knowing which problem to model first, which data to discard, and where a simple regression outperforms a neural network.

Most AI projects fail because they begin with technology. Ours begin with a question: what decision are you trying to make faster or more accurately? Everything follows from the answer.

How we think about Artificial Intelligence

01 The problem outranks the model We refuse to recommend deep learning when a decision tree solves the business question. Choosing the simplest adequate model keeps costs low, interpretability high, and deployment timelines short. A retail client needed demand forecasting across 1,200 SKUs; a gradient-boosted ensemble delivered 94% accuracy in three weeks.

02 Data quality before data volume Feeding terabytes of noisy records into a model produces confident wrong answers. We spend the first sprint auditing, cleaning, and reshaping your existing data. One insurance client discovered that 30% of their claims records contained duplicate entries that skewed every prior analysis they had run.

03 Your team runs it, not ours We build systems your staff can operate after handover. Every deployment includes documentation written for non-engineers, recorded walkthroughs, and a 90-day support window. Dependency on external consultants is a failure state, not a revenue model.

04 Explain or don't ship If a model cannot explain its recommendation in terms a department head understands, it stays in staging. Regulators, board members, and end users all need transparency. We use SHAP values, partial dependence plots, and plain-language summaries for every production model.

05 Measure the business outcome, not the F1 score Accuracy metrics matter in the lab. In your business, what matters is whether the model reduced churn, shortened lead time, or caught fraud earlier. We define success criteria in commercial terms before writing a single line of code.

6 weeks average deployment Clients across logistics, insurance, retail, legal 89% average model accuracy on production data Manchester-based, UK-wide delivery

Capability map

We work in five areas. Each maps to a specific business decision type, not a technology category.

Capability	What it solves	Typical timeline	Output you receive
Prediction modelling	Demand forecasting, churn risk, pricing sensitivity	4–8 weeks	Deployed model, API endpoint, monitoring dashboard
Document intelligence	Contract extraction, invoice parsing, compliance scanning	3–6 weeks	Extraction pipeline, accuracy report, integration guide
Decision automation	Approval routing, risk scoring, triage classification	5–10 weeks	Rule engine with ML layer, audit trail, operator manual
Data audit and readiness	Assessing whether your data can support the AI use case you want	2–3 weeks	Written report with gap analysis, remediation plan, cost estimate
Custom research prototyping	Novel problems without off-the-shelf solutions	6–14 weeks	Proof-of-concept model, feasibility assessment, scaling roadmap

Data scientist working on machine learning code at a modern desk

Whiteboard session mapping an AI system architecture

Are you a good fit?

You have a recurring business decision that currently relies on spreadsheets, gut feeling, or manual review by a team of three or more people.
You hold at least six months of historical data related to that decision, even if it is messy or spread across multiple systems.
You have a named internal sponsor who can allocate two hours per week to the project for its duration.
You want a working system, not a slide deck. Proof-of-concept is fine as a starting point, but the goal is production.
Your budget can accommodate a minimum engagement of £12,000. Most projects fall between £15,000 and £60,000 depending on complexity.

"They told us upfront that our data wasn't ready and helped us fix it before starting the model. Saved us from wasting the budget." — Head of Analytics, mid-size insurer

A note on the AI market right now

There is too much noise. Vendors promise autonomous everything. Startups raise rounds on demos that break the moment real data touches them. Consultancies sell "AI strategy" documents that gather dust in shared drives.

We have watched this cycle before with big data, with blockchain, with cloud migration. The pattern is always the same: inflated expectations, followed by disillusionment, followed by the quiet work of people who actually build things that run in production.

We sit in that last group. Our engagements are not glamorous. They involve cleaning date formats, arguing about feature importance, and testing edge cases at two in the morning. The result is a model that works on Monday, and still works the following January.

If you want a partner who will be honest about what AI can and cannot do for your specific situation, we should talk. If you want someone to tell you that large language models will replace your entire workforce by next quarter, we are not the right firm.

Readiness signals

These are the patterns we see in organisations that get the most value from AI projects.

Operational bottleneck

A process that takes your team hours each week and follows repeatable logic. Invoice matching, lead scoring, inventory reorder decisions. These are the first candidates.

Data already collected

You do not need a data lake. You need records. CRM exports, ERP logs, transaction histories. If it exists in a spreadsheet or database, we can likely use it.

Clear success metric

Reduce processing time by 40%. Catch 15% more fraudulent claims. Forecast demand within 5% accuracy. Concrete targets let us design the right model and prove its value.

Internal champion

Someone inside your organisation who understands the problem, has authority to provide data access, and can rally colleagues during user acceptance testing.

Start a conversation

Tell us what decision you want to improve. We will reply within two working days with an honest assessment of whether AI is the right tool.

Thank you. We will be in touch within two working days.

Or reach us directly: [email protected] · +44 161 486 6304

48 Oak Road, Manchester M1 5AN, Greater Manchester, United Kingdom

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