Hyperengage creates a unified, structured memory graph for each customer by aggregating product usage data, CRM activity, and all interaction logs from tools like HubSpot, Salesforce, Intercom, and Slack. This graph enables AI agents to read, reason, and act on comprehensive customer context, automating tasks such as briefing CSMs, answering queries, and triggering playbooks, thereby improving post‑sales efficiency and renewal rates.
Funding
Funding not disclosed
Founders
Product
Problem
Customer success teams must gather data from multiple disconnected tools—CRMs, product analytics, support tickets, and communication platforms—to assess account health and prepare for interactions. This fragmented workflow leads to lost context, inefficient manual data aggregation, and missed renewal or expansion opportunities.
Solution
Hyperengage consolidates product usage, CRM activity, and all customer interactions into a single, structured Customer Memory Graph. The platform automatically ingests and reconciles data from sources such as HubSpot, Salesforce, Segment, Intercom, Slack, and Stripe without custom ETL scripts. The unified graph provides a living view of each account’s health trajectory, usage depth, and risk signals, which AI agents and human users can read, reason over, and act upon. Built-in agents—Sidekick for briefing, Copilot for natural‑language Q&A, and Signals Engine for automated playbooks—leverage the graph to generate call briefs, answer queries, and trigger workflow automation. Teams can also connect custom LLM agents via the MCP API, enabling bespoke automation while maintaining a single source of truth.
Target Audience
Primary customers are customer success, renewal, and revenue operations teams in SaaS companies that need a consolidated view of account health and automated post‑sales workflows.
Features
- Automatic, bidirectional ingestion and reconciliation of data from 8+ SaaS tools (HubSpot, Salesforce, Segment, Intercom, Zendesk, Slack, Stripe, etc.) with no manual mapping or ETL code
- Unified Customer Memory Graph that models accounts as structured entities with health scores, usage events, interaction history, and risk signals
- AI-native agents (Sidekick, Copilot, Signals Engine) that read the graph to generate call briefs, answer queries, and fire playbook actions in real time
- MCP API allowing any LLM or custom agent (Claude, Codex, ChatGPT, or proprietary models) to read/write the graph using scoped tokens
- Unlimited human and agent readers per account, with per‑account pricing based on the number of tracked customers
- Role‑based access control, SSO/SAML, SCIM provisioning, and SOC 2/GDPR compliance features for secure enterprise use
- Activity log and audit trail for all reads and writes, supporting transparency and governance