NUUN AI
PILLARAI & DIGITAL TRANSFORMATION

AI that graduates from the pilot.

Evaluated. Governed. Accountable to a business KPI — not a demo.

Book a consultSee sub-services
Outcomes
Business KPI
Every AI build is measured against a named business outcome — not model accuracy alone.
Governance
NIST AI RMF
Evaluation harness, governance policy, and incident runbook shipped with every engagement.
Practice
NUUN AI
Dedicated AI engineers, ML researchers, and applied-AI strategists inside the parent brand.
Practice proof

Numbers the practice will defend in writing.

Governance framework · every engagement
NIST AI RMF
Claude · GPT · Gemini · open-weight
Model-agnostic
Evaluation harness built before the model
Eval-first
Re-evaluation cadence · retire when underperforming
Quarterly

Quick answer
NUUN Digital's AI & Digital Transformation practice — anchored by NUUN AI — delivers AI strategy and roadmaps, generative AI and Retrieval-Augmented Generation (RAG) builds, machine learning models, workflow automation, AI-powered content systems, and the AI Initiatives Benchmarking Lab. Every engagement ships with an evaluation harness, a governance policy, and a business KPI — not a demo.
Sub-services

6 discrete engagements inside the AI & Digital Transformation pillar.

001
AI & Digital Transformation

AI Strategy & Roadmap

Where AI earns its keep — and where it doesn't.

002
AI & Digital Transformation

Generative AI Build

Custom assistants, RAG systems, and agent workflows with guardrails.

003
AI & Digital Transformation

Machine Learning & Predictive Models

ML models scoped against a business outcome, not a paper.

004
AI & Digital Transformation

Workflow & Process Automation

Automate the ops tax so humans do higher-order work.

005
AI & Digital Transformation

AI-Powered Content & Marketing Systems

Content ops powered by AI with human guardrails and brand safety.

006
AI & Digital Transformation

AI Initiatives Benchmarking

Benchmark your AI program against peers — think-tank style.

How we work.

A six-step process from discovery to measured outcome.

  1. 01

    Discover

    Interviews, audits, and a written problem statement.

  2. 02

    Design

    Approach options with trade-offs and pricing.

  3. 03

    Plan

    Phase-by-phase plan with a single accountable owner.

  4. 04

    Build

    Execution in weekly sprints, stakeholder demos every 2 weeks.

  5. 05

    Measure

    Against the KPI we set in week one. No vanity metrics.

  6. 06

    Compound

    Quarterly review, roadmap refresh, next bet.

From pilot purgatory to production

The gap between a demo that wows the exec team and a system that runs reliably in production is where most AI budgets die. We close that gap as a discipline, not a project — with evaluation harnesses, guardrails, change management, and a fallback plan the day the model misbehaves.

We publish the evaluation harness, the failure modes, and the governance policy. If it can't be audited, it's not ready for production.

Comparison — what kind of AI do you actually need?

| Your question | Right tool | Why | |---|---|---| | "Answer customer questions from our documentation" | RAG system on Claude / GPT-4 | Retrieval grounds the model in your source of truth | | "Automate a multi-step business process with judgment" | Agentic workflow + human-in-loop | Agents make calls; humans approve on escalation | | "Predict which customers will churn / buy / lapse" | Classical ML (XGBoost, logistic regression) | LLMs are the wrong tool; tabular data wins | | "Summarize and analyze unstructured text at scale" | Generative AI with evaluation harness | LLMs are strong here; evals keep them honest | | "Image or document classification" | Computer vision / fine-tuned models | Purpose-built models outperform general LLMs on narrow tasks | | "Generate content at scale with brand consistency" | LLM + style guide + human review | Production-grade requires a brand and fact-check layer |

Industries we know

AI patterns matched to real workflows across CPG, Financial Services, Health & Wellness, Healthcare & Pharma, Lottery & Gaming, Retail & E-commerce, Travel & Hospitality, Public Affairs, Energy, Real Estate, Education, and Tech & SaaS.

Browse industry pages →

Flagship research

Related reading

Sources & further reading

NUUN AI Practice — AI engineering, evaluation, and governance. Generative AI and RAG implementation, ML and predictive modelling, AI governance mapped to NIST AI RMF and ISO/IEC 42001.

Proof.

Recent wins across NUUN Digital engagements.

Retail & E-commerce

A loyalty app worth keeping on the home screen.

NativeiOS + Android rebuild · Swift + Kotlin
Financial Services & Fintech

A bank AI assistant with zero material findings.

ZeroMaterial audit findings at rollout

FAQ.

What is a RAG system and when do I need one?
Retrieval-Augmented Generation (RAG) combines a generative model with a retrieval layer over your own documents. You need one when the answer the model gives must be grounded in specific source material — policy, product documentation, case law, internal knowledge bases — and the model can't hallucinate its way through. RAG systems ship with the source citations in the output so reviewers can verify.
Build vs buy — how do I decide?
Buy for commoditized capability (general-purpose copilots, transcription, image captioning). Build for competitive differentiation — anywhere your data, workflow, or regulatory environment is unique enough that off-the-shelf tools dilute the advantage. We run a build-vs-buy matrix in the first two weeks of every engagement.
Which foundation models do you use?
Model-agnostic by design. We build on Claude (Anthropic), GPT / o-series (OpenAI), Gemini (Google), and open-weight models (Llama, Mistral, DeepSeek) depending on the task's reasoning depth, latency, cost, and data sensitivity. Every build includes a model-switching layer so clients aren't locked to one provider.
How do you handle AI governance and risk?
Every engagement includes an AI governance package — data handling policy, model-use policy, acceptable-use guidelines, a risk assessment mapped to NIST AI RMF, and an incident response runbook. For regulated industries (financial services, healthcare, public sector), we extend to include model-risk management, bias audits, and explainability documentation.
What is "AI Share of Model" and why should we track it?
Share of Model is the share of AI answer-engine responses (ChatGPT, Gemini, Perplexity, Claude, Copilot) that cite or mention your brand when asked industry-relevant questions. It's the AI-era equivalent of share-of-voice. We track ours weekly across 30 priority prompts and run the same programme for clients.
Can you evaluate an AI initiative we started in-house?
Yes. Our AI Initiatives Benchmarking Lab runs third-party audits of in-flight AI projects — evaluating use case fit, model choice, evaluation rigor, governance maturity, and go-to-production readiness. The output is an honest "kill / fix / ship" recommendation per initiative.
What does "NUUN AI" refer to?
NUUN AI is the dedicated AI practice within NUUN Digital — a focused team of AI engineers, ML researchers, and applied-AI strategists that operates as a distinct capability under the NUUN brand. It's how we deliver AI-specific work at the depth clients need.

Ready to talk AI & Digital Transformation?

Bring the target and the deadline — we'll scope an approach in 5 business days.

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