A simple path from data chaos to governed AI in production.

Three stages designed for speed, safety, and measurable outcomes.

1

2-Week Data + AI Diagnostic

A fast, structured assessment to identify what's broken and how to fix it.

What we do in 10 business days:

Data landscape map — systems, ownership, integrations, pain points

Quality + reliability assessment — where pipelines break, why reporting conflicts

Security + compliance baseline — access, PHI/PII, retention, audit

AI readiness assessment — what's feasible now vs later

Use-case selection workshop — pick 1–2 with measurable ROI

Deliverables

Architecture + governance blueprint (Microsoft-first)

Prioritized backlog (30/60/90 days)

Foundation Sprint plan (scope, timeline, team, cost ranges)

"First use case" definition with success metrics

Best for:

Organizations that want clarity and speed without committing to a huge program.

2

Foundation Sprint (4–8 weeks)

Build the minimum production-ready foundation with one working use case.

What gets built:

Data ingestion + transformation pipelines

Canonical models for reporting + AI features

Governance controls — RBAC, data zones, lineage, policy

Monitoring + alerting + cost visibility

1 production use case (analytics or AI)

Output:

A foundation you can run—plus a working use case in production.

3

Managed Data + AI Ops (ongoing)

Operate, improve, and expand your platform continuously.

What we run and improve:

Reliability — incident response, pipeline health, SLA-based operations

Security + governance — access reviews, audits, policy enforcement

Performance + cost — FinOps + scaling

Continuous delivery of new use cases (monthly roadmap)

Output:

A durable system that keeps getting better, not a one-time project.

Ready to get started?

Book a 2-Week Diagnostic to get clarity on what to fix first.

Book a Diagnostic