Does this sound familiar?
Why it happens
Most GTM tech stacks grow one purchase at a time. A tool solves one team's problem, gets connected to the CRM with a quick integration, and a year later nobody knows which system holds the truth. The CRM ends up configured around old processes and workarounds, and data quality suffers because nobody owns the definitions.
AI makes all of this more visible, because it amplifies whatever data and processes you feed it. The order matters: first the process and the definitions, then a CRM and tools set up to support them, and then automation and AI on top.
What I build and fix
CRM and tech stack
- CRM architecture, setup and migrations (HubSpot, Salesforce, Pipedrive, Attio and others)
- Tech stack audit and tool selection
- Integrations between your product backend, CRM, billing and ticketing
- Workflow automation across tools
Data quality & governance
- Cleanup, deduplication and enrichment
- Field definitions, data standards and ownership
- GDPR and data-compliance workflows
AI & GTM automation
- Enrichment and lead scoring with GTM AI tools (Clay, Apollo and others)
- AI-assisted outbound and research
- Choosing AI tools that actually pay off
Call & contact centre operations
- Dialler, VoIP and IVR setup (Five9, VCC Live, CloudTalk, Aircall, Talkdesk and others)
- Routing, number porting, call compliance and recording
- Call performance reporting
What you walk away with
A CRM and tech stack set up around your processes: clean data with clear ownership, integrations that hold, and automation and AI built on a foundation that's ready for them. All documented, with your team trained to run it.
Related reading
- You Can't Enrich Your Way Out of Bad Operations
- AI Readiness Checklist for GTM Operations (free Excel template)