Predict What's Next — Turn Data into Foresight with Production - Ready ML.

Demand forecasting, churn prediction, recommendations and risk models that integrate with your systems — built with Python, TensorFlow & scikit-learn

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NO OBLIGATION • TECHNICAL AUDIT INCLUDED

Example Data Pipeline

Problem

Decisions made on lagging reports, guesswork, or one-off analysis cost you revenue and introduce risk. Teams struggle with noisy signals, manual forecasting, and brittle models that fail in production. You need reliable, maintainable predictions that actually influence outcomes.

Solution

We design, train, and product ionize predictive models that give your business forward-looking signals — from accurate demand forecasts to customer churn alerts and personalized recommendations. Our models are explainable, monitored in production, and tailored to your business objectives so you can act earlier and smarter.

Results

Act earlier

Spot trends before they materialize and move faster than competitors.

Reduce waste

Better forecasting lowers inventory and operational costs.

Increase revenue

Targeted recommendations and churn mitigation improve retention and lifetime value.

Clients

★★★★★

Trusted by product teams, analytics leaders and ops heads across SMEs and scaleups

"Spot-on insights — reducing risk and boosting confidence."

“Empowers us with foresight and agility.”

“From gut-feel to data-driven decisions.”

“Tailored models, measurable outcomes.”

"Their churn model reduced attrition by 18% in six months — the model paid for itself within the quarter." — Head of growth, anonymized client.

Benefits

Problem framing & success metrics — models aligned to business KPIs so predictions drive decisions, not vanity metrics.
Data pipeline & feature engineering — robust features reduce noise and improve model stability.
Model selection & explainability — we balance accuracy with interpretability (SHAP, LIME) so stakeholders trust outputs.
Production deployment & monitoring — CI/CD for models, drift detection, and alerting to keep performance steady.
Custom inference layer & integration — REST APIs, batch jobs, or edge deployments to fit your stack.
Retraining strategy & MLOps — scheduled retrains and governance to avoid model decay.
Privacy & security best practices — PII handling, encryption, and compliance-aware pipelines.

Problem framing & success metrics — models aligned to business KPIs so predictions drive decisions, not vanity metrics.
Data pipeline & feature engineering — robust features reduce noise and improve model stability.
Model selection & explainability — we balance accuracy with interpretability (SHAP, LIME) so stakeholders trust outputs.
Production deployment & monitoring — CI/CD for models, drift detection, and alerting to keep performance steady.
Custom inference layer & integration — REST APIs, batch jobs, or edge deployments to fit your stack.
Retraining strategy & MLOps — scheduled retrains and governance to avoid model decay.
Privacy & security best practices — PII handling, encryption, and compliance-aware pipelines.

Mini Case Study

Case

High Churn and low average order value.

Work Delivered

Churn classifier + personalized product recommendation pipeline.

Outcome

Churn reduced 18%, recommendation-driven revenue up 12%, overall ROI positive within 3 months.

Architecture diagram, precision/recall chart, production dashboard screenshot.

All Case Studies

More Dashboards

Pricing

Pilot — £7,500 (one-off)

Scope: Single predictive use-case (e.g., churn or demand), data assessment, model prototype, pilot integration, 4 weeks support.

Best for proving value quickly.

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Team — £22,000 (project) — Most Popular

Scope: end-to-end pipeline, model, production deployment, monitoring, documentation, 2 months post-launch support.

Recommended for teams ready to operationalize ML.

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Enterprise — £4,000 / month (retainer)

Scope: ongoing MLOps, model lifecycle management, regular model improvements, dedicated data scientist.

Best for continuous learning systems and high-impact predictions.

Apply

Custom enterprise packages and one-off data migrations available — talk to us for a tailored quote.

Book free discovery call to secure priority delivery and access starter pricing.

LIMITED ONBOARDING SLOTS.

Guarantee of Work Assurance

SATISFACTION PROMISE.

We’ll deliver a working pilot that meets the mutually agreed metric target or provide an actionable remediation plan and credit toward next phase. All projects include a 30-day optimization window post-launch.

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NO OBLIGATIONS • NO TAILORED ROADMAP • FAST NEXT STEPS

How long until we see results?

Typical initial model + pilot is 6–12 weeks depending on data readiness and scope.

Do we need a data science team in-house?

No — we deliver production-ready solutions and provide handover, training, or ongoing MLOps support depending on your preference.

What about model accuracy and fairness?

We evaluate multiple metrics (precision, recall, AUC) and run bias audits and fairness checks where relevant.

How will this integrate with our systems?

We deliver APIs, scheduled batch jobs, or direct integration with your data warehouse / feature store.

How are models governed and monitored?

We implement drift detection, performance dashboards, and retrain triggers as part of MLOps.

How long until we see results?

Typical initial model + pilot is 6–12 weeks depending on data readiness and scope.

Do we need a data science team in-house?

No — we deliver production-ready solutions and provide handover, training, or ongoing MLOps support depending on your preference.

What about model accuracy and fairness?

We evaluate multiple metrics (precision, recall, AUC) and run bias audits and fairness checks where relevant.

How will this integrate with our systems?

We deliver APIs, scheduled batch jobs, or direct integration with your data warehouse / feature store.

How are models governed and monitored?

We implement drift detection, performance dashboards, and retrain triggers as part of MLOps.

How long until we see results?

Typical initial model + pilot is 6–12 weeks depending on data readiness and scope.

Do we need a data science team in-house?

No — we deliver production-ready solutions and provide handover, training, or ongoing MLOps support depending on your preference.

What about model accuracy and fairness?

We evaluate multiple metrics (precision, recall, AUC) and run bias audits and fairness checks where relevant.

How will this integrate with our systems?

We deliver APIs, scheduled batch jobs, or direct integration with your data warehouse / feature store.

How are models governed and monitored?

We implement drift detection, performance dashboards, and retrain triggers as part of MLOps.