For developers building GTM agents, Clearskies is a context layer that unifies CRM, calls, email, calendar, and Slack into a single context graph, resolving identities and mapping relationships so agents run revenue analysis with full business context.
Presence & Market Position
Tracked · below panel floor — earned mentions are accruing toward the next scored window.
Measures earned, engagement-weighted share of voice across the GTM voices panel. Methodology →
Clearskies is positioning itself as infrastructure rather than a standalone application, a context layer that unifies CRM, call, email, calendar, and Slack data into a single account-and-deal graph that any AI tool, from Claude to ChatGPT to an internal build, can draw on instead of reconstructing customer history each time. It argues that plain API connectors provide access without context, and that continuously maintained context is what makes AI outputs trustworthy. Its footprint in GTM conversation is still early, with the visible signal so far a practitioner demo, a customer walking through connecting the CRM and calls together and running briefs, research, and coaching workflows off the unified picture rather than fragments.
Turso
Supermemory
Zep
Mem0
VascoStratus
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Business Profile
Clearskies is built by the team behind Scratchpad, a revenue workflow platform, and carries that operator lineage into a context-layer product aimed at engineers and revenue teams building AI workflows rather than end users clicking through a UI. The company doesn't publish funding or headcount, and its public customer evidence is early, with a testimonial from Stratus describing how the company stitched CRM, call, and communication data together into one build. Pricing is usage-based, tied to workflow volume rather than per-seat licensing, positioning it as infrastructure teams build on top of rather than a seat-priced application.
Agent Readiness
Measures how easily your agents can build on it — API, MCP, CLI, SDK, docs depth. Methodology →
Clearskies ships as an API and an MCP server, positioned so any AI tool, in-house build or established assistant, can read a maintained context graph instead of pulling raw CRM, call, and calendar data separately. Its documentation frames the product for teams building agent workflows directly, offering to get a working integration running in hours rather than months, though the build-out beyond the current API and MCP surfaces isn't detailed on the public record yet.
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