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The problem

AI capabilities for revenue teams are advancing fast. Models reason well, write well, and can take action in the tools your team already uses. Yet most revenue teams are still far from running AI across their day-to-day sales motion, and the gap between what AI can do and what teams have actually shipped keeps widening. The reason is not the models. It is the context underneath them. Three things keep breaking: Connectors break. Wiring Claude, ChatGPT, Cursor, or Grok Bot to Salesforce, Gong, Gmail, and Slack one source at a time works in a demo. In production, every custom MCP server, script, and API path is one more thing your best operator has to keep alive, and each one sees only its own data. Models change. A new model takes the lead every few months. When the joins, identities, and history live inside prompts and one-off integrations, every model change means rebuilding context from scratch. Models hallucinate. When a model queries individual connectors, the picture comes back in fragments. It has to work out, on every query, that the email address, the voice on the call, and the Slack handle are the same person in the same deal. Where the fragments leave gaps, it fills them with what sounds right rather than what is in the data.

Buy vs. build

Teams have had two ways to respond. You can buy a sales AI product. It arrives with its own context, built for the average company rather than yours, and its own interface, which is one more place your team has to go. When the feature does not match your process, you wait on a roadmap. Or you can build the context layer yourself: connectors, identity resolution, timelines, and a way to serve all of it to AI. That means competing for engineering resources, and then maintaining something that breaks every time an API or a model changes.

The path that should exist

There should be a third option: a context layer you connect once that stays complete, current, and queryable, and that any AI tool can work from. The AI clients your team uses will keep changing. The context underneath them should not. That is what Clearskies is. Connect your CRM, calls, email, calendar, and Slack. The Context Graph builds and maintains itself. Claude, ChatGPT, Cursor, Grok Bot, your workflows, and your own code all work from the same customer record, with every answer cited back to its sources.

What we believe

  • AI fails at context, not reasoning. The limiting factor for AI on revenue teams is whether the model has the full customer picture: CRM, calls, email, calendar, Slack, and the business context that makes sense of them.
  • Connected is not unified. Giving AI access to five sources is not the same as giving it one resolved picture. Identity resolution, timelines, and gap detection have to happen before the question is asked.
  • Context should outlive the model. The joins, the history, and the business rules belong in a layer you own, so your answers stay the same when your model changes.
  • Your business context belongs in the layer. Sales process, team structure, territories, and operating rules should travel with every query, rather than being rebuilt in each prompt or skill.
  • Operators should ship outcomes, not maintain connectors. RevOps and GTM Engineers understand the workflows better than any vendor. Their time should go to the briefs, workflows, and analyses, not to keeping integrations alive.
  • Pricing should encourage rollout. Per-seat fees slow adoption and penalize the teams that use AI most. Plans should be sized to the team, not to a seat count.
  • Every answer should show its work. If AI is going to inform a forecast or a QBR, each answer needs to cite the calls, emails, CRM fields, and Slack threads it came from, and flag what is missing.

Next steps