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.
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.
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.
Connect your systems
CRM (Salesforce, HubSpot), call transcripts (Gong), email (Gmail, Outlook), calendar, Slack. No custom engineering required. No field mapping. Takes minutes.
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.
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
Cross-system questions
Individual connectors
AI pieces it together ad hoc
Context graph
Relationships already resolved
Entity resolution
Individual connectors
You build and maintain it
Context graph
Handled for you
Setup
Individual connectors
Configure and maintain each connector
Context graph
Connect once, unified automatically
Maintenance
Individual connectors
Fix each connector when APIs change
Context graph
Managed for you
Gap detection
Individual connectors
Not possible (each connector sees only its own data)
Context graph
First-class feature
Token efficiency
Individual connectors
5–15 retrieval calls, 50–100K tokens per query
Context graph
Pre-computed graph, ~2K tokens
Consistency
Individual connectors
Non-deterministic (different results each time)
Context graph
Same answer every time
| 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 |
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.