Data & AI Strategy
Translate leadership priorities into a 12-18 month roadmap with clear capability milestones.
- Use-case portfolio prioritization
- Operating model and team design
- Business KPI definition
Data & AI Engineering + Consulting · New Zealand
Turn Data and AI into execution, not slideware.
We partner with delivery-focused teams to design strategy, engineer production-grade platforms, and integrate AI into real business workflows.
Finance Retail Education Public Sector
Trusted execution rhythm
Built for enterprise teams that need engineering depth, controlled risk, and visible business outcomes.
Architecture + Delivery AI Enablement + Integration Governance-ready by design Finance · Retail · Education · Public Sector
Services
From strategy to engineering and production operations.
Delivery scope designed for enterprise and public-sector constraints, not generic startup assumptions.
AI Product Engineering
Design and ship production-grade AI products with robust evaluation and observability.
- RAG and agent architecture
- LLM safety and quality controls
- Prompt and retrieval optimization
Data Platform & Governance
Build cloud data foundations that scale across analytics, BI, and machine learning.
- Lakehouse and warehouse architecture
- Data quality and lineage
- Access and policy controls
MLOps & AI Operations
Move from pilot to reliable runtime with deployment, monitoring, and rollback standards.
- CI/CD for models and pipelines
- Model drift monitoring
- Incident and change management
Decision Intelligence
Turn fragmented data into decision workflows with measurable business outcomes.
- Executive metrics framework
- Experimentation and uplift tracking
- Process-level automation
AI Enablement & Integration
Enable teams to embed AI into real workflows and integrate models into existing enterprise systems.
- Workflow and API integration patterns
- Copilot enablement for business teams
- Platform handover and operating playbooks
Engagement formats
Discovery sprint, architecture track, and engineering delivery stream in one program.
Representative outcomes
Case patterns with integration-led delivery.
During development stage, these are anonymized summaries based on common enterprise scenarios. Client-specific names and metrics can be added later.
Enterprise Bank
Challenge: Inconsistent risk signals across channels slowed response time for high-value events.
Integration approach: Integrated streaming events, scoring services, and analyst workflow tooling into one operating loop.
Delivered: Designed a real-time risk intelligence pipeline with event scoring and analyst triage workflows.
National Retail Group
Challenge: Forecast variance and stock imbalance caused avoidable markdown and inventory pressure.
Integration approach: Connected demand models to planning workflows and integrated outputs into existing merchandising systems.
Delivered: Delivered multi-horizon demand forecasting and decision dashboards integrated into planning cycles.
Public Sector Service Team
Challenge: Policy and service data lived in silos, reducing visibility for planning and intervention.
Integration approach: Integrated shared data contracts with cross-team reporting and human-in-the-loop review paths.
Delivered: Built a unified analytics model and integrated natural-language workflows into daily operations for cross-team decisions.
Education Provider
Challenge: Learning and operational systems were disconnected, limiting intervention speed for support teams.
Integration approach: Integrated student event streams, advisor tooling, and early-alert workflow triggers.
Delivered: Integrated student analytics with advisor workflows and early-alert AI recommendations for faster interventions.
Delivery model
Discover, design, build, and scale with governance.
Discover
Assess current architecture, data contracts, and business constraints.
Design
Define target-state architecture, operating model, and risk controls.
Build
Implement prioritized value slices with quality gates and observability.
Scale
Operationalize standards and handoff playbooks for long-term autonomy.
Insights
Implementation notes for data and AI leaders.
AI Governance in Regulated Industries: An Execution Blueprint
A practical blueprint for turning governance requirements into delivery workflows for finance and public sector teams.
2026-02-28 · DataNAI
RAG Architecture Trade-offs for Enterprise Delivery
How to make practical retrieval-augmented generation decisions across quality, latency, and governance constraints.
2026-02-27 · DataNAI
Data Quality Controls for Production AI Systems
A control baseline for data quality that supports reliable AI outcomes across model and workflow lifecycles.
2026-02-26 · DataNAI
Decision point
If you already have a high-priority use case, we can map feasibility, integration scope, and first release plan in one call.
FAQ
Common questions before project kickoff.
What services does DataNAI provide?
We provide Data and AI strategy, data platform engineering, GenAI application delivery, MLOps setup, and delivery enablement.
Where is DataNAI based?
We are based in Auckland, New Zealand, and work with teams across ANZ and global markets.
Do you support regulated environments?
Yes. We design operating patterns for governance-heavy environments, including finance and public-sector contexts.
Can we start with a small engagement?
Yes. Most engagements begin with a focused discovery sprint and a first production-aligned value slice.
Do you work with internal engineering teams?
Yes. Our default mode is co-delivery with your internal data, platform, and product teams.
Next step
Bring one priority use case. We will map feasibility, architecture, and delivery path.
Project inquiry response target: within 24 hours (business days).