RevOps Architecture & Data/Integrations
Foundations & Infrastructure
Build scalable, AI-ready systems with enterprise-grade architecture, governance, and consolidation to cut technical debt.
Pain points
- Technical debt from fragmented systems makes it hard to integrate new AI or RevTech tools.
- Data governance and system ownership are unclear, leading to inconsistent outputs and workflow breakage.
- Scaling requires architecture decisions, but internal teams lack capacity or patterns.
Value props
- Establishes enterprise-grade, governable architecture so downstream workflows remain stable.
- Makes systems AI-ready by ensuring data and integration design support agentic workflows and revenue intelligence.
- Reduces ongoing rebuilding by consolidating and standardizing the foundations.
Use cases
- Modernizing the CRM and supporting systems before rolling out larger GTM automation.
- Creating governance frameworks for data movement and system-of-record clarity.
- Preparing a company's stack for AI investment and orchestration.
Killer questions
- What specific governance and ownership models do you implement (data standards, controls, operational runbooks)?
- How do you design integrations so downstream workflows don't break during tool changes?
- What's your approach to reducing shelfware while improving scalability?
Why now:
- AI initiatives fail without reliable systems of record and governed data flows.