Ben here, cofounder of ATC. LLMs have become very good at writing plausible account plans, meeting briefs, and outreach. The hard part is giving them enough account-specific context to know where to focus. We built ATC as a read-only MCP server for Claude and ChatGPT. It gives the model structured tools for investigating account fit, technology stacks, active initiatives, developer activity, buying centers, stakeholders, and recent signals. Instead of asking a generic model to research an account from scratch, you can ask questions like: “Rank these 100 Snowflake accounts by which ones I should call this week, and explain why.” “Is Wells Fargo a fit for Snowflake? Show me the strongest evidence, the strongest counterargument, and what would weaken the thesis.” ATC supplies the account intelligence. Claude or ChatGPT performs the final synthesis and writing. What is available today: Remote MCP server with OAuth Claude and ChatGPT support Account research, ranking, meeting preparation, and account plans Read-only access 50 free credits each month Our team came from technology-ecosystem research for institutional investors. We built ATC because we saw that better models were not enough: the quality of an AI workflow depends heavily on the quality and structure of the context it receives. We would particularly value feedback on: Whether the evidence is sufficient to audit an account recommendation Which account-research tools belong in the MCP surface How much connector setup is acceptable before users see value Thanks for taking a look.

ATC: Tech GTM Intelligence in Claude

Give Claude/ChatGPT evidence-backed account intelligence

Give Claude/ChatGPT evidence-backed account intelligence