How it works

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.

The problem

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. Every query starts from scratch. And no individual connector can tell you what’s missing — because it only sees its own data.

Individual connectors
CRM
AI sees records
Calls
AI sees transcripts
Email
AI sees threads
Slack
AI sees messages
Calendar
AI sees events
Fragments. Siloed data in, partial truths for AI, incomplete answers out.
vs
Customer Context Graph
CRMCallsEmailSlackCalendar
Customer Context Graph
Entities resolved · Relationships mapped · Activities linked
Your AI agents
Complete picture. One connection. Your AI has full context from every system.
What the context graph does

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 ordered chronologically. The full story of every customer, every deal, every relationship. Not scattered across five tools — one coherent 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 a question about a deal and get an answer built from CRM + calls + email + Slack + calendar. Every source contributing. Every answer citing its sources. One question, full picture.

How you connect

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.

The comparison

Individual connectors vs. Context Graph

Individual connectorsContext Graph
Cross-system questionsAI pieces it together ad hocRelationships already resolved
Entity resolutionYou build and maintain itHandled for you
SetupConfigure and maintain each connectorConnect once, unified automatically
MaintenanceFix each connector when APIs changeManaged for you
Gap detectionNot possible (each connector sees only its own data)First-class feature
Token efficiency5–15 retrieval calls, 50–100K tokens per queryPre-computed graph, ~2K tokens
ConsistencyNon-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

Usage-based pricing. Add your whole team without per-seat costs.

Enterprise security

SOC 2 compliant. Your data is encrypted in transit and at rest.

Technical details

The context layer is the foundation. What you build on it is up to you.