We have a lot of data but it is messy. Where do we start?

Start with a data audit. We assess what you have, where it lives, and what condition it is in. Then we prioritise based on which data matters most for your business goals. You do not need to fix everything at once. Focus on the data that drives your highest-value decisions first.

Do we need a data warehouse or a data lake?

It depends on your use cases. A warehouse is best for structured business reporting and analytics. A lake suits unstructured data and AI workloads. Many organisations benefit from a lakehouse approach that combines both. We recommend what fits your needs, not what is trendiest.

How do we ensure GDPR compliance with our data infrastructure?

We design privacy into the architecture from the start. That includes data classification, access controls, retention policies, consent management, and the right to erasure workflows. Every system we build includes audit logging so you can demonstrate compliance.

What does a data and AI consultancy engagement typically cost?

Data strategy engagements start from ten thousand pounds. Pipeline builds and analytics platforms vary widely based on complexity but typically range from twenty to eighty thousand. We always scope carefully so you know the cost before committing.

How long before we see value from our data investment?

Quick wins are usually visible within four to six weeks. Automated dashboards replacing manual reports, data quality issues resolved, and new insights surfacing. Larger infrastructure builds take three to six months but we phase delivery so value arrives incrementally.

Can you work with our existing data team?

Absolutely. We often embed alongside in-house data engineers and analysts. We bring specialist expertise for the strategic and architectural work, while your team handles ongoing operations. Knowledge transfer is built into every engagement.

We have a lot of data but it is messy. Where do we start?

Start with a data audit. We assess what you have, where it lives, and what condition it is in. Then we prioritise based on which data matters most for your business goals. You do not need to fix everything at once. Focus on the data that drives your highest-value decisions first.

Do we need a data warehouse or a data lake?

It depends on your use cases. A warehouse is best for structured business reporting and analytics. A lake suits unstructured data and AI workloads. Many organisations benefit from a lakehouse approach that combines both. We recommend what fits your needs, not what is trendiest.

How do we ensure GDPR compliance with our data infrastructure?

We design privacy into the architecture from the start. That includes data classification, access controls, retention policies, consent management, and the right to erasure workflows. Every system we build includes audit logging so you can demonstrate compliance.

What does a data and AI consultancy engagement typically cost?

Data strategy engagements start from ten thousand pounds. Pipeline builds and analytics platforms vary widely based on complexity but typically range from twenty to eighty thousand. We always scope carefully so you know the cost before committing.

How long before we see value from our data investment?

Quick wins are usually visible within four to six weeks. Automated dashboards replacing manual reports, data quality issues resolved, and new insights surfacing. Larger infrastructure builds take three to six months but we phase delivery so value arrives incrementally.

Can you work with our existing data team?

Absolutely. We often embed alongside in-house data engineers and analysts. We bring specialist expertise for the strategic and architectural work, while your team handles ongoing operations. Knowledge transfer is built into every engagement.

Data & AI Consultancy

<p>AI is only as good as the data behind it. Most businesses sit on vast amounts of valuable data but lack the infrastructure, processes, and skills to use it. We help you build the data foundations that make AI possible and the analytical capabilities that drive better decisions.</p>

Discuss Your Project

Building Your Data Foundation

Every AI initiative starts with data. If your data is scattered, inconsistent, or inaccessible, no amount of clever algorithms will save you. We start where it matters most: getting your data house in order.

Our data strategy service audits your current data landscape. Where is your data? What format is it in? Who owns it? How fresh is it? We map every source, identify gaps, and design an architecture that brings it all together in a way that supports both analytics and AI.

This is not about building a data lake and hoping for the best. We design purpose-driven data architectures that serve your actual use cases. Whether you need real-time dashboards, batch analytics, or AI-ready feature stores, the architecture fits the need. A solid data foundation is also the prerequisite for a successful AI strategy.

Data Engineering and Pipelines

Clean, reliable data pipelines are the backbone of any data-driven organisation. We build automated pipelines that extract data from your source systems, transform it into usable formats, and load it into the right destination, whether that is a warehouse, a lake, or an AI training environment.

Our engineering team works with modern tools including dbt, Airflow, Spark, and cloud-native services from AWS, Azure, and GCP. We choose the right tools for your scale and complexity rather than defaulting to the most expensive option.

Every pipeline includes data quality checks, error handling, and monitoring. Bad data gets caught before it reaches your reports or models. When something breaks, alerts fire immediately and the system tells you exactly what went wrong and where.

Analytics and Business Intelligence

Data only creates value when it reaches the people who make decisions. We build analytics solutions that put the right information in front of the right people at the right time. No more waiting a week for a report that is already outdated.

We design dashboards and self-service analytics platforms using tools like Power BI, Looker, and Tableau. But the tool matters less than the design. Our dashboards are built around the decisions you need to make, not just the data you happen to have.

For organisations ready to move beyond descriptive analytics, we build predictive and prescriptive models. Demand forecasting, churn prediction, pricing optimisation, and anomaly detection. These models turn historical data into forward-looking intelligence that drives action across retail, finance, and beyond.

Making Your Data AI-Ready

If AI is on your roadmap, your data needs to be ready. That means more than just having enough of it. It means consistent labelling, appropriate granularity, proper versioning, and governance that ensures quality and compliance.

We prepare your data estate for AI by implementing feature stores, data catalogues, and lineage tracking. Your data scientists and engineers can find, trust, and use data without spending eighty percent of their time cleaning it.

We also help with the organisational side. Data ownership, access policies, quality standards, and stewardship roles. The companies that succeed with AI are the ones that treat data as a strategic asset, not an IT problem. We help you make that shift. For enterprise organisations managing complex data estates, we offer dedicated long-term engagements.

What You Get

Data Strategy & Architecture

A clear plan for how your data will be collected, stored, governed, and used. Aligned to your business goals and AI roadmap.

Automated Data Pipelines

Reliable ETL and ELT pipelines that keep your data fresh, clean, and accessible. Built with modern tooling and full monitoring.

Data Quality Framework

Automated checks and validation rules that catch errors at source. Bad data never makes it to your reports or models.

Self-Service Analytics

Dashboards and reporting tools that empower your team to find answers without waiting for a data request.

Predictive Analytics

Machine learning models that forecast demand, predict churn, spot anomalies, and surface opportunities from your data.

Data Governance & Compliance

Policies, roles, and tooling for GDPR compliance, data access control, and audit trails. Data you can trust.

How We Work

Discover

We audit your current processes, data, and AI readiness. No jargon — just a clear picture of where you stand.

Strategise

We build a tailored AI roadmap aligned with your business goals. Every recommendation has a clear ROI case.

Implement

We build, integrate, and deploy AI solutions. Hands-on, working alongside your team, not from an ivory tower.

Optimise

We measure, refine, and scale what works. AI is a journey, not a one-off project.

Frequently Asked Questions

We have a lot of data but it is messy. Where do we start?: Start with a data audit. We assess what you have, where it lives, and what condition it is in. Then we prioritise based on which data matters most for your business goals. You do not need to fix everything at once. Focus on the data that drives your highest-value decisions first.
Do we need a data warehouse or a data lake?: It depends on your use cases. A warehouse is best for structured business reporting and analytics. A lake suits unstructured data and AI workloads. Many organisations benefit from a lakehouse approach that combines both. We recommend what fits your needs, not what is trendiest.
How do we ensure GDPR compliance with our data infrastructure?: We design privacy into the architecture from the start. That includes data classification, access controls, retention policies, consent management, and the right to erasure workflows. Every system we build includes audit logging so you can demonstrate compliance.
What does a data and AI consultancy engagement typically cost?: Data strategy engagements start from ten thousand pounds. Pipeline builds and analytics platforms vary widely based on complexity but typically range from twenty to eighty thousand. We always scope carefully so you know the cost before committing.
How long before we see value from our data investment?: Quick wins are usually visible within four to six weeks. Automated dashboards replacing manual reports, data quality issues resolved, and new insights surfacing. Larger infrastructure builds take three to six months but we phase delivery so value arrives incrementally.
Can you work with our existing data team?: Absolutely. We often embed alongside in-house data engineers and analysts. We bring specialist expertise for the strategic and architectural work, while your team handles ongoing operations. Knowledge transfer is built into every engagement.

Need Help with Data & AI Consultancy?

Let's talk about how we can help your business. Free consultation, no strings attached.

Book a Free Consultation