Build AI agents that actually understand your revenue data.
Your agents don't carry a quota.
Designed for RevOps teams engineered revenue:
Prove what your AI agents closed.
Close the loop between agent and seller action → revenue. Every action is traced and joined back to the deal it moved, so you can show what your AI actually drove.
Cut AI token spend.
Serve up the right, pre-enriched context instead of raw data on every call. Agents use up to 70% fewer tokens — and produce sharper, grounded output.
Context for every agent.
Build a central context layer, delivered to every seller and agent in a single MCP call. One endpoint, any model, no lock-in.
Build your own sales agent.
Ship a fully-configured and customized AI assistant today — white-labeled, trained on your data, proactive on at-risk deals, and observable in your Loop console.
Build a private AI model, trained on your revenue data.
Serve pre-enriched context once and reuse it across every agent. Teams cut token use up to 70%.
Revenue teams shipping AI that earns its seat.
Context engineering is the practice of building a structured, judgment-enriched data layer that AI agents can use to make revenue-relevant decisions. Instead of dumping raw CRM data or transcripts into an agent's prompt, context engineering pre-processes your GTM data through ML models trained on real deal outcomes, then delivers pre-materialized context anywhere your agents run via MCP or API.
Context engineering is the practice of building a structured, judgment-enriched data layer that AI agents can use to make revenue-relevant decisions. Instead of dumping raw CRM data or transcripts into an agent's prompt, context engineering pre-processes your GTM data through ML models trained on real deal outcomes, then delivers pre-materialized context anywhere your agents run via MCP or API.
Context engineering is the practice of building a structured, judgment-enriched data layer that AI agents can use to make revenue-relevant decisions. Instead of dumping raw CRM data or transcripts into an agent's prompt, context engineering pre-processes your GTM data through ML models trained on real deal outcomes, then delivers pre-materialized context anywhere your agents run via MCP or API.
Context engineering is the practice of building a structured, judgment-enriched data layer that AI agents can use to make revenue-relevant decisions. Instead of dumping raw CRM data or transcripts into an agent's prompt, context engineering pre-processes your GTM data through ML models trained on real deal outcomes, then delivers pre-materialized context anywhere your agents run via MCP or API.
Context engineering is the practice of building a structured, judgment-enriched data layer that AI agents can use to make revenue-relevant decisions. Instead of dumping raw CRM data or transcripts into an agent's prompt, context engineering pre-processes your GTM data through ML models trained on real deal outcomes, then delivers pre-materialized context anywhere your agents run via MCP or API.
Context engineering is the practice of building a structured, judgment-enriched data layer that AI agents can use to make revenue-relevant decisions. Instead of dumping raw CRM data or transcripts into an agent's prompt, context engineering pre-processes your GTM data through ML models trained on real deal outcomes, then delivers pre-materialized context anywhere your agents run via MCP or API.
Put your revenue data to work — for every agent and seller.
without building it themselves.