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REAL Growth Partnership treats AI as a concrete commercial tool, not a buzzword. We build the data foundations first, including customer data unification, then deploy AI where it moves the P&L: content production at scale, product intelligence, first-party data monetisation, and Lontra AI, our productised people-performance platform for large workforces.

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Diagram: one brief fanning out into a matrix of asset variants, with a single variant emphasised.VariantsOne brief

What gets in the way

Most of what makes AI useful happens before any model is chosen. Where the data sits, whether the definitions agree and how the work actually gets briefed decide the result long before the tool does.

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  • AI on data that does not join up

    Customer data sits in separate systems with no agreed definition between them, so anything built on top inherits the gaps. Personalisation and decisioning then produce confident answers from partial evidence.

  • Analysis rebuilt by hand

    Customer intelligence lives in spreadsheets that only their author can navigate, and every new question starts the work again. Working out what data exists and which attributes matter takes longer than the decision it is meant to support.

  • Content demand hiring cannot meet

    The same work now has to serve the customer who buys, the platform deciding who sees it and the assistants answering questions about it. Platforms reward volume and variety rather than one finished hero asset, and adding people to a studio raises the cost base faster than it raises output.

  • Tools bought without a workflow

    Licences are signed and demonstrations impress, then use settles on whoever remembers the tool exists. With no workflow, approval or reporting built around it, the way work gets briefed and signed off does not change.

Diagram: fragmented records converge into one view of the customer, which feeds an AI node, branching to product intelligence, first-party data and AI content production.Fragmented recordsOne view of the customerAIProduct intelligenceFirst-party dataAI content production

Data foundations first, then AI where it moves the P&L.

REAL Growth Partnership treats AI as a concrete commercial tool, not a buzzword.

  • Data strategy & product intelligence
    Connecting insight, marketing and operations.

  • Customer data unification (CDP)
    One view of the customer as the foundation for AI.

  • First-party data monetisation
    Turning first-party data into new revenue streams.

  • AI content production & efficiencies
    Content at scale, at a fraction of the cost base.

  • Lontra AI: HR performance platform
    Productised people-performance solution for large workforces.

Technology features

Custom AI-product build across Martech and HR landscape

Data analytics & science; Data products driving value decisions

KPI & target centricity, with transformation monitoring

Custom AI product build

Data analytics and science

Data products

KPI and transformation monitoring

Frequently asked questions

  • Because AI personalisation, decisioning and content tools are only as good as the customer view underneath them. REAL builds a single customer view, usually through a CDP, so that AI is applied to accurate, connected data rather than fragmented records.

  • Lontra AI is REAL's productised people-performance platform for large workforces. It applies AI to HR and performance data to give operators of large teams a clear view of workforce performance and the levers that improve it.

  • You do not need perfect data, but you do need the records behind a decision to be connected and governed. We start from the decisions you want to make and unify what those depend on. On a recent customer intelligence programme that meant auditing and documenting more than one hundred customer attributes before any modelling began.

  • Yes, because a person signs off every asset before it goes live. AI drafts, adapts and scales; brand and legal standards sit inside the workflow as a required step rather than a final check.

  • Rarely. We build on the platforms you already have and connect what is fragmented. New tooling is recommended only where the current stack genuinely cannot do the job, and we show you the reasoning before you commit to it.

  • All of it. On the content programme above, the capability was running in-house by month six. The same rule applies to data models and tooling: you get the documentation, the training and a team able to run the work without us.

A leading UK online retailer's content studio, rebuilt

A £6.76m studio cost base that could not scale to meet a structural shift: content now serves the customer, the platform algorithm and AI assistants, and Meta and Google reward volume and variety that no amount of hiring could deliver.

Read the full case study

  • £2.0–3.4m

    Annualised production efficiency, proven on live briefs

  • −30 to −70%

    Cost per asset, with 2x to 5x more assets produced

  • 3–4 mo

    To self-funding, with the capability owned in-house by month six