Data & AI Expertise

Data science that ships, scales, and pays for itself

Prioritise high‑impact AI use cases with clear commercial upside
Design, build, and deploy production data science solutions reliably
Operationalise models with monitoring, governance, and continuous improvement

Discuss your AI roadmap

When our data science team is the right partner

Best suited to organisations that see AI as a strategic capability and need experienced partners to accelerate safely.

You have AI pilots but few production wins

You’ve run PoCs or experiments, but they haven’t translated into stable, widely adopted production systems.

You need a focused AI roadmap, not a feature tour

You want a small number of high‑impact use cases prioritised by business value, feasibility, and time‑to‑impact.

You lack in‑house senior data science capacity

Your team is stretched or skewed junior, and you need experienced practitioners to set patterns and lead delivery.

You must satisfy risk, compliance, and governance

You operate in a regulated or risk‑sensitive environment and need explainable models, controls, and auditability.

You want AI embedded in products and workflows

You care less about dashboards and more about AI directly influencing decisions in your apps and operations.

You aim to scale a portfolio of AI initiatives

You’re planning multiple data science projects and need shared platforms, standards, and delivery practices.

Example engagements

Where our data scientists create fast, visible impact

B2B SaaS / Subscription

Revenue lift through intelligent cross‑sell and upsell

Designed and deployed propensity models that score accounts for cross‑sell and expansion, integrated with CRM and sales playbooks. Delivered prioritised lead lists, next‑best‑offer recommendations, and measurable uplift in pipeline and conversion.

Retail & CPG

Forecasting and inventory optimisation at scale

Built demand forecasting models combining historical sales, promotions, and external signals. Operationalised forecasts into replenishment rules and dashboards, reducing stockouts, excess inventory, and manual planning effort.

Telecom / Fintech

Customer churn prediction and retention actions

Developed churn propensity models and interpretable drivers, then embedded scores into customer success and marketing workflows. Enabled targeted retention campaigns, improved save rates, and reduced acquisition‑replacement costs.

Logistics / Manufacturing

Risk scoring and anomaly detection for operations

Implemented anomaly detection on sensor, transaction, and process data to flag quality issues and operational risks early. Integrated alerts into existing tools, reducing downtime, rework, and compliance incidents.

Financial Services

From PoCs to governed ML platform

Assessed scattered AI pilots, selected high‑value candidates, and designed a standardised path to production. Introduced model lifecycle management, monitoring, and governance controls to satisfy risk and compliance while accelerating delivery.

How we work

A data science approach built for production, not just prototypes

We combine strategic guidance with hands‑on delivery so your AI investments move quickly from idea to stable, auditable systems in production.

Discover value and feasibility

Align with business and technology leaders on goals, constraints, and existing data assets. Identify and size candidate use cases, assess feasibility, and shortlist the opportunities with the strongest value‑to‑effort ratio.

Design solution and operating model

Shape the end‑to‑end solution: data sources, features, model classes, architecture, and integration points. Define success metrics, governance requirements, and how models will be owned, monitored, and improved over time.

Build, validate, and explain

Engineer data pipelines, develop and compare models, and run rigorous validation with clear baselines. Produce interpretable insights and documentation so stakeholders understand performance, limitations, and risk controls.

Deploy, integrate, and automate

Productionise models via APIs, batch jobs, or in‑app features. Integrate with CRMs, ERPs, and operational tools. Implement CI/CD for data and models, automated retraining where appropriate, and robust monitoring for drift and performance.

Enable teams and scale portfolio

Train product, engineering, and business teams on how to use, trust, and iterate on data science outputs. Establish standards, templates, and reference implementations to make the next AI initiative faster and less risky.

Business Outcomes

Clear AI roadmap tied to revenue, cost, and risk outcomes
Fewer stalled PoCs; more models reliably running in production
Stronger data and ML foundations that your teams can own
Improved decision‑making with transparent, explainable models
Reduced delivery risk through proven patterns and governance

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AI Adoption in 2025: A Framework for Enterprise Success

A practical roadmap for executives launching enterprise-scale AI initiatives—covering governance, architecture, success metrics, and change management.

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AI Adoption in 2025:
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