Foundations & Infrastructure
Get core systems in order: clear ownership, sane integrations, and less technical debt before you add more tools.
Pain points
- Fragmented systems make every new AI or RevTech rollout a custom integration project.
- Nobody owns data standards, so workflows break when someone changes a field or connector.
- The team knows architecture matters but doesn't have bandwidth or patterns to design it well.
Value props
- Governable architecture so downstream workflows don't break every quarter.
- Data and integration design that can support AI and automation when you're ready for it.
- Consolidation and standards that cut the rebuild cycle.
Use cases
- Modernizing CRM and supporting systems before a bigger GTM automation push.
- Defining who owns what data and how it moves between systems.
- Getting the stack ready before you spend on AI 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 projects stall when nobody trusts the system of record or the data flowing into it.