New platform solves the missing data layer for AI agents, delivering investment-research-grade account intelligence while reducing LLM token consumption
ATC today launched the first AI-native intelligence platform purpose-built to provide the missing data infrastructure for the agentic GTM era. The platform introduces Conviction Intelligence, a new category of data infrastructure that replaces shallow, fragmented behavioral signals with deep, investment-research-grade analysis. While AI agent adoption among B2B sellers hit 54% in 2025 (per Salesforce’s State of Sales report), account intelligence maturity sits near 12%. ATC’s Conviction Intelligence solves this data layer crisis by triangulating curated sources to deliver account-specific directives that tell revenue teams and autonomous agents exactly who to target, why now, who owns the problem, and what to say.
By feeding this highly compressed, verified analysis directly to Large Language Models (LLMs) via the Model Context Protocol (MCP), ATC minimizes the need for expansive, generic context windows. This targeted grounding dramatically cuts down LLM token consumption while enabling customers to significantly improve the impact of their go to market outcomes. In early deployments, ATC customers achieved a 10X prospect reply rate against an industry baseline near 1%.
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The Intelligence Layer Agentic GTM Has Been Missing
Ninety-five percent of tech GTM teams now use automated outreach, causing message volume to skyrocket while response quality flatlines. AI agents have inherited decade-old behavioral data from legacy sequence tools, producing incorrect answers faster at a greater scale. ATC was engineered from the ground up to serve as the structural data layer beneath the execution stack, answering the fundamental question agents and reps must resolve before acting: is this account worth our time?
ATC’s founders spent two decades leading Pacific Crest Securities, delivering institutional technology research for investors like Fidelity and J.P. Morgan. This financial-grade discipline now powers the ATC infrastructure. The platform compresses output into hyper-specific account directives, allowing AI agents to act on verified analysis instead of pattern-matched guesses.
Early Field Results
Customers are seeing reply rates scale past industry baselines as outreach shifts from high-volume sequencing to high-conviction targeting.
Jim Beasley, a regional sales manager for a leading data-infrastructure company, achieved an 11.4% reply rate using the platform. “Running Claude on ATC means we go after a fraction of the accounts we used to and see better results from every prompt,” said Beasley. “The difference is knowing more, earlier, so every conversation we start is worth having.”
As MCP emerges as the standard protocol connecting AI agents to external tools, ATC’s native MCP server plugs directly into Claude and ChatGPT. This ensures agent actions are driven by verified, account-specific directives rather than generic context windows.
“Claude and other LLMs are central to modern GTM, but signal vendors trained sellers to confuse activity with outcomes,” said Scott Sandbo, CEO at ATC. “AI agents inherited that same shallow data layer. ATC provides the deep intelligence infrastructure required for true agentic grounding.”
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