Identity resolution
sarah.kim@acme.com, Sarah Kim on a Gong call, and @sarah.kim in Slack become one person — automatically. Across every source, every deal, every account. Resolved once, not re-guessed on every query.
From fragmented data to full customer context.
Clearskies connects every system that touches your customer, resolves identities across them, and gives any AI the complete picture through a single connection.
Connecting more tools
doesn’t solve the problem.
When you connect Claude or ChatGPT to Salesforce, Gong, email, and Slack separately, your AI gets four disconnected views. It has to figure out — every single time — that sarah.kim@acme.com in email, Sarah Kim on a Gong call, and @sarah.kim in Slack are the same person, in the same deal.
That burns tokens, takes time, and gets it wrong. And no connector can tell you what’s missing; each one only sees its own data.
A context graph does the hard work
before AI asks a question.
Timeline construction
Every interaction in order. The full story of every customer, deal, and relationship in one timeline.
Gap detection
Not just what’s there — what’s missing. A champion who went dark. A deal with no email activity in 14 days. A follow-up that never happened. The context graph surfaces absence, not just presence.
Cross-system synthesis
Ask about a deal and get one answer built from CRM, calls, email, Slack, and calendar, with every source cited.
Three steps to full context.
01
Connect your systems
CRM (Salesforce, HubSpot), call transcripts (Gong), email (Gmail, Outlook), calendar, Slack. No custom engineering required. No field mapping. Takes minutes.
02
The context graph builds automatically
Clearskies ingests your data, resolves entities, maps relationships, and links activities across every system. One coherent customer graph, continuously updating.
03
Connect to your AI
Add Clearskies as an MCP server in Claude, connect it to ChatGPT, or use our API. Your team’s AI now has full customer context, ready to query.
Individual connectors vs. context graph
Plan
Individual connectors
Context graph
Cross-system questions
AI pieces it together ad hoc
Relationships already resolved
Entity resolution
You build and maintain it
Handled for you
Setup
Configure and maintain each connector
Connect once, unified automatically
Maintenance
Fix each connector when APIs change
Managed for you
Gap detection
Not possible (each connector sees only its own data)
First-class feature
Token efficiency
5–15 retrieval calls, 50–100K tokens per query
Pre-computed graph, ~2K tokens
Consistency
Non-deterministic (different results each time)
Same answer every time
What makes it different
Model-agnostic
Claude, ChatGPT, Gemini, or your own tools. One context layer, any AI.
Your data stays yours
No vendor lock-in. No extraction fees. Reads from and writes to your data warehouse.
Source transparency
Every answer cites which calls, emails, CRM fields, and Slack threads were used. Every answer flags what’s missing.
Zero user license fees
Flat plans sized to your team, not per seat. Add everyone and every AI client at no extra cost.
Enterprise security
SOC 2 compliant. Your data is encrypted in transit and at rest.
Technical details
MCP (Model Context Protocol)
Clearskies delivers context to AI through MCP, Anthropic’s native protocol for connecting AI to external data. One connection replaces five separate connectors. The graph is pre-computed — AI doesn’t rebuild context on every query.
Supported integrations
CRM, calls, email, calendar, Slack, and support — every system that touches your customer. Status (live, beta, roadmap), what each one reads and writes back, and setup specifics live on the integrations directory. See every supported integration
Data architecture
Clearskies maintains a continuously-updated customer graph. Identity resolution runs at ingest, not at query time. Timelines are pre-built. Gaps are detected proactively. The graph writes back to your data warehouse if you have one.
The context layer is the foundation. What you build on it is up to you.
