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Context is everything

What makes AI useful on a revenue team? The same things that make a new hire useful: the full story of each customer, access to the right tools, feedback to improve, and clear boundaries. AI that only sees CRM data misses the actual conversation. It does not know what was said on the call, what follow-ups were promised, or what concerns came up over email. AI that sees each of those sources separately has a different problem: it has to reassemble the customer on every query, and it has no way to notice what is missing. That is why Clearskies connects CRM, calls, email, calendar, Slack, support, and engineering tools into one Context Graph, and why the graph is built before AI asks a question rather than at query time. How this shapes the product:
  • Deep integrations with all major GTM systems
  • The Context Graph as the foundation every other capability sits on
  • Identity resolution, timelines, and gap detection handled in the graph, not in the prompt

Connected is not unified

Adding a connector gives AI access to a source. It does not give AI the customer. When Claude, ChatGPT, Cursor, or Grok Bot is wired to Salesforce, Gong, email, and Slack separately, it gets four disconnected views. It has to work out, every time, that sarah.kim@acme.com in email, Sarah Kim on a Gong call, and @sarah.kim in Slack are one person in one deal. That burns tokens, takes time, and gets it wrong often enough to matter. No connector can tell you what is missing, because each one only sees its own data. That is why Clearskies resolves identities, maps relationships, and builds timelines once, across every source, and serves the result through a single connection. How this shapes the product:
  • One MCP connection in place of a separate connector per source
  • A pre-computed graph, so the AI does not re-retrieve and re-join your data on every query
  • Gap detection as a first-class feature, such as a deal with no email activity in 14 days
  • Every answer cites which calls, emails, CRM fields, and Slack threads it used

Context should outlive the model

A new model and harness takes the lead every few months. The AI clients your team uses will keep changing too. If your joins, history, and business rules live inside prompts and one-off integrations, every change means starting over. That is why Clearskies is model-agnostic and client-agnostic. The context layer stays constant; the AI on top of it is whatever is best today. How this shapes the product:
  • Works with Claude, ChatGPT, Cursor, Grok Bot, and your own tools on the same graph
  • One Clearskies plugin and one MCP server, so the same context follows you across clients
  • Consistent answers across tools, users, and prompts, because every workflow starts from the same context
  • The API and MCP server expose the same graph, so what you build in one place carries to another

Your business context, not the average company’s

Generic sales AI is built for the average company. Your team has specific deal stages, specific qualification criteria, and specific rules about who owns what. A pre-built “deal scoring” feature might use MEDDPICC when you use BANT, or flag deals at 30 days when you care about 60. That is why Clearskies carries your business context into every AI workflow: sales process, team structure, product lines, territories, and operating rules. It lives in the graph, so it is available to every client and every query instead of being re-explained in each prompt. How this shapes the product:
  • Business context modeled alongside customer data
  • Use cases and examples for common workflows that you adapt to your process, not locked features
  • Agents and workflows built in the Clearskies app that read the same context as your AI clients

Operators should ship outcomes, not maintain connectors

RevOps and GTM Engineers understand the sales motion better than any vendor. They know which signals matter and what a good brief looks like. The thing that slows them down is infrastructure: keeping custom MCP servers, scripts, and API paths alive, and re-doing that work when something upstream changes. That is why Clearskies manages the connectors, the graph, and the writes back to your systems, so operator time goes to the briefs, workflows, and analyses the team actually feels. How this shapes the product:
  • Self-service connections for supported sources, with no field mapping and no custom engineering
  • Managed maintenance when source APIs change
  • Workflows with triggers, agents, and actions that you publish without involving engineering

Pricing should encourage rollout

Per-seat pricing creates the wrong incentive. You want everyone on the team, and every AI client, working from the same context, but you are stuck calculating per-seat costs and negotiating with procurement. Rollout gets delayed and adoption suffers. That is why Clearskies has no per-seat fees. Every new workspace starts with credits to prove it on your own data. When Clearskies becomes part of your operating rhythm, you move to a flat monthly plan sized to your go-to-market team. How this shapes the product:
  • No per-seat fees; plans are sized to your go-to-market team, not a seat count
  • Credits to start, pay-as-you-go to explore, flat plans to scale
  • Unlimited Context plans remove the meter on context calls, subject to fair use

Iteration beats perfection

The first version of a brief, a workflow, or an analysis will miss edge cases. If you ship it to five people, collect feedback, and improve, you will have something valuable in weeks rather than months. For AI to move from an impressive demo to a dependable tool, it has to learn what “good” looks like for your team, and that only happens through real usage. That is why Clearskies is built for fast iteration on top of a stable context layer. How this shapes the product:
  • Test runs for workflows that are always dry, so nothing sends or writes until you publish
  • Use cases for the patterns teams feel weekly, such as pre-call briefs and QBR prep
  • Easy to update agents and workflows as you learn what works

What this means for you

These principles translate to a few concrete promises:
  • Complete context. Every AI tool your team uses works from the full customer picture, including what is missing.
  • Consistent answers. Claude, ChatGPT, Cursor, Grok Bot, and your internal agents start from the same context, so answers do not drift by tool, user, or prompt, and sources are cited.
  • Any AI, any time. Change models or clients without rebuilding your context.
  • Your data stays yours. No lock-in, no extraction fees, and you choose which data sources are connected.
  • Priced for rollout. No per-seat fees, with plans sized to your team.

Next steps

  • Quick start: Connect your data and put your graph to work in about 10 minutes
  • What you can build: Analyses, workflows, and agents on the Context Graph