The technology behind our agents
One memory per customer. Every agent, every time.
Every Hyperengage agent reads the Customer Memory Graph — a structured, living record of every customer in your business. It fuses CRM data, product behavior, support history, and conversations into one model, so your agents act on the whole account, not a slice of it.
Customer Memory Graph· live
Unified customer context
Signals powering every workflow
234,891 events↑
Sources
Readers
7 sources · 1 graph · 4 readerslatency 84ms
HubSpot
CRM
·live
Salesforce
CRM
·live
Segment
Product
·syncing
Intercom
Support
·live
Zendesk
Support
·live
Slack
Comms
·live
Stripe
Billing
·connected
Insight
Event
Decision
Email
Meeting
Thread
Action
Account
Sidekick
reads to brief CSMs
Copilot
reads to answer questions
Signals Engine
reads to fire playbooks
MCP
apiClaude · ChatGPT · more
Customer Memory Graph: CRM, product events, support, email, and billing sources flow into a unified memory graph that AI agents read.
Customer Memory Graph· live
7 Sources
HubSpot
CRM
Salesforce
CRM
Segment
Product
Intercom
Support
Zendesk
Support
Slack
Comms
Stripe
Billing
Account
234,891 events↑
Unified customer context
Insight
Event
Decision
Email
Meeting
Thread
Action
4 Readers
Sidekick
reads to brief CSMs
Copilot
reads to answer questions
Signals Engine
reads to fire playbooks
MCP
apiClaude · ChatGPT · more
7 sources · 1 graph · 4 readerslatency 84ms
The problem
Context lives in 8 places. Agents start from zero every time.
CSMs stitch context manually before every call. Agents re-read old threads to understand an account. Dashboards show what happened, not what it means. The result: post-sales runs on tribal knowledge and copy-paste.
Before
CRM
Product
Support
Slack
Calls
Billing
Notes
CSM
Stitches context manually before every call. Agents re-read old threads to understand an account.
Tribal knowledge
What feeds it
Every signal, reconciled
Hyperengage ingests CRM, product events, support tickets, billing data, and conversation history. It doesn't pile them in a data lake. It reconciles them into one semantic model with one definition of active user, churn risk, and expansion signal.
CRM
Salesforce, HubSpot — accounts, deals, contacts, ownership.
Product events
Segment, internal telemetry — feature usage, depth, recency.
Support
Zendesk, Intercom — tickets, sentiment, time to resolution.
Comms
Email, Slack, calls — every interaction, reconciled to one timeline.
Billing
Stripe, Chargebee — contract value, renewal date, payment health.
What lives in it
Each account becomes a living object
A Customer Memory Graph node holds:
01
Health trajectory
Not just a score — the direction.
02
Usage depth
Which features, how often, by whom.
03
Interaction history
Every email, ticket, call, reconciled to one timeline.
04
Risk signals
The patterns that precede churn.
05
Expansion moments
The patterns that precede growth.
How agents read it
Four readers. One graph.
Sidekick briefs CSMs. Copilot answers questions. Signals Engine fires playbooks. MCP exposes the graph to Claude, ChatGPT, and other MCP clients. Same source of truth. Four surfaces.
Reader
Sidekick
Reads the graph to brief CSMs before every call.
Reader
Copilot
Reads the graph to answer hard questions in seconds.
Reader
Signals Engine
Reads the graph to fire playbooks when conditions are met.
Reader
MCP (Agent API)
Exposes the graph to Claude, ChatGPT, and other MCP clients. Live state, not stale CRM exports.
Memory graph vs. CRM
A CRM records what happened. A Customer Memory Graph knows what it means.
CRM | Customer Memory Graph | |
|---|---|---|
| What it stores | Forms, fields, manual entries | Reconciled signals across every tool |
| What it understands | Whatever the CSM typed in | Behavior, sentiment, trajectory |
| Who reads it | Humans | Humans + AI agents |
| When it updates | When someone remembers to | Continuously |
Put agents to work on the full picture.
Start free and connect your stack yourself, or let our forward-deployed engineers get your first agent live in 2 weeks.