NUUN AI
SOLUTION PROGRAM

From AI pilot to AI practice.

Strategy, governance, platform, first-wave builds, and enablement — shipped as one program.

Quick answer
NUUN AI's practice-setup solution packages AI strategy, governance, platform selection, first-wave builds, and team enablement as one program — so enterprise AI graduates from the pilot and into production. Aligned to NIST AI RMF and ISO/IEC 42001. Typical length: 9–18 months.

WHAT'S INCLUDED

  • AI strategy and opportunity mapping. Prioritized use cases with ROI and risk assessments.
  • Governance and policy framework. Acceptable-use, risk-management, and model-validation policies; AI committee structure and escalation paths.
  • Platform selection. Foundation model evaluation, vector store selection, orchestration framework, and evaluation tooling.
  • Reference architecture. Security, observability, and cost controls scoped into the platform from day one.
  • First-wave builds. 2–4 production-grade AI applications built, evaluated, and monitored.
  • Evaluation harness. Offline and online evaluation, hallucination monitoring, and drift detection.
  • Team enablement. Training for builders, reviewers, and leaders; center-of-excellence operating model.
  • Roadmap and run-book. 18-month roadmap with run-book for the internal team to continue delivery.

WHEN THIS SOLUTION FITS

  • Enterprises with pilot-stage AI work that hasn't reached production.
  • Organizations with fragmented AI efforts across business units, needing a unified practice.
  • Regulated industries (financial services, healthcare, public affairs, energy) requiring governance-first AI approaches.
  • Post-board-mandate AI strategy requiring a full practice stand-up.

WHEN IT DOES NOT FIT

  • Single-use-case AI builds — use our AI & Digital Transformation practice for project work.
  • Pure training or workshops — we offer those separately but this solution is execution-led.
  • Pre-strategy exploration — you need a shorter strategy sprint first; reach out and we'll scope one.

HOW THE PROGRAM RUNS

  1. Strategy and opportunity (months 1–3). Use-case mapping, prioritization, and AI strategy document.
  2. Governance and policy (months 2–4). Acceptable-use, risk-management, model-validation policies; AI committee structure.
  3. Platform (months 3–6). Foundation-model selection, orchestration, vector store, evaluation framework, and reference architecture.
  4. First wave (months 4–12). 2–4 production builds with evaluation and monitoring.
  5. Enablement (months 9–15). Internal training, CoE operating model, run-books.
  6. Handover (months 15–18). Transition to internal team with a defined roadmap and sustained-performance protocols.

WHAT YOU'LL GET

  • An AI strategy document — prioritized opportunities with ROI and risk.
  • A governance and policy package — acceptable-use, risk, model-validation, and committee structure.
  • A platform and reference architecture — deployed, secured, and observable.
  • Production AI applications — 2–4 shipped, evaluated, and monitored builds.
  • An enablement package — training, run-books, and CoE operating model.
  • A sustained-performance monitoring protocol — post-handover check-ins and drift-detection cadence.

SELECTED WORK

  • Financial services client — Enterprise AI practice stand-up → [X] production use cases shipped; governance passed internal audit. Read case →
  • Healthcare client — Clinical-support AI + governance → pilot graduated to production with MLR-approved workflows. Read case →
  • Consumer client — Customer-service AI + RAG platform → deflection rate up [X]%, satisfaction up [X] points. Read case →

RELATED READING

SOURCES & FURTHER READING

FAQ.

How do you handle AI risk and responsible-AI requirements?
NIST AI RMF and ISO/IEC 42001 are our baseline frameworks. For regulated industries we overlay sector-specific requirements (FDA for clinical, FINRA for financial services, HIPAA for health data). Risk is built into the platform, not layered on.
Can you support our existing AI team rather than replace them?
Yes — this is the typical engagement model. Your team has the domain knowledge; our team has the enterprise-AI discipline. Co-delivery accelerates practice maturity without displacing institutional expertise.
What about generative AI specifically versus traditional ML?
Most practice stand-ups include both. Generative AI is where the current interest concentrates; traditional ML (predictive, recommendation, anomaly detection) often has higher ROI. The strategy phase weighs both honestly.
How do you measure AI-practice success?
Against named outcomes — production use cases shipped, cost savings realized, revenue generated, or risk reduced. Not against model-quality benchmarks alone. Models that never graduate from the pilot don't deliver value.
Do we need a data platform in place before you can stand up an AI practice?
No. The program assesses the current data stack in the first phase and sequences platform work alongside the AI roadmap. If your data platform is immature, we stage the first-wave AI builds on lower-risk use cases while the data layer matures — so the practice ships value in the first year regardless.

Book An Ai-practice Consult

Bring the strategy need or the stalled pilot. We'll bring the production-graduation method.

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