Your data architecture
wasn't built for AI.
We fix that.
Accurate metrics. Clean data models. For humans and AI.
How we get you there
Three phases. Each one delivers standalone value.
Phase 1
Diagnose
2 weeks · $15K–$25K
A detailed report showing everything that’s broken and why — plus a proposed design to fix it.
- End-to-end lineage
- Metric conflicts cited from actual code
- Anti-patterns and inefficiencies
- Proposed redesign and implementation plan
Metadata only. No production data needed.
Phase 2
Build
6–10 weeks · $50K–$100K
Clean data models and semantic layer deployed directly to your stack. Fully validated against your existing logic.
- Validated data pipelines
- Semantic layer implementation
- Consolidated metric definitions
- Code you own — no lock-in
Deployed on your stack. You own everything.
Phase 3
Enable
Varies · $20K+
AI agents that answer business questions reliably — because the data foundation is now trustworthy.
- Natural language analytics agent
- Governed, reliable answers
- Proof of AI ROI
- Foundation for autonomous agents
A tangible AI ROI story for your leadership.
The problem
Your dashboards don't agree. Stakeholders don't trust the numbers. Your team spends more time fixing than building.
And now AI agents are making decisions on that data.
Fragmented definitions
Revenue means one thing in finance’s dashboard and another in marketing’s pipeline. Nobody tracks the copies. They drift silently.
Fire drills every month
End-of-month close becomes a reconciliation exercise. The board deck doesn’t match the dashboard. Nobody can explain why.
Agents amplify the chaos
AI agents inherit every inconsistency in your data and amplify it at machine speed. They fail silently — the numbers look right until they don’t.
No one owns the truth
Business logic is scattered across dbt, Looker, Tableau, and spreadsheets. There’s no central authority and no mechanism to keep them in sync.
Who's building this
George Bezerra, PhD
Founder & CEO
15 years building the data and AI infrastructure that enterprises run on. PhD in Computer Science, postdoc at MIT CSAIL researching extreme-scale database architectures.
Led a 60-person data organization as SVP of Data at The Knot Worldwide. Previously Sr. Director at TripAdvisor. Co-founder of InvestInData, an angel collective of 50+ senior data executives.
Built Pristino Labs after watching the same problem repeat at every company: business logic scatters across tools, drifts from the original intent, and nobody owns the canonical definition. AI agents will make this worse — unless the architecture is solved first.
Ready to talk?
30 minutes. No pitch. Just an honest look at where your data stands and what it would take to make it AI-ready.
Our process uses metadata only. No production data access required.