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Enterpret is an AI-native platform that centralizes customer feedback from 50+ sources—Twitter, Salesforce, Zendesk, Slack, Intercom, community forums, surveys, and Gong calls—into a unified context graph. The company builds adaptive taxonomies and an agent platform that enables teams to query, monitor, and act on customer intelligence across tools. Customers include Canva, Notion, WisprFlow, ElevenLabs, OpenRouter, and Western Union. Backed by Kleiner Perkins, Peak XV Partners, and Unusual Ventures.
You will be the first product manager for the Integration Ecosystem, owning both the user experience of setting up and managing integrations and the system behavior underneath. Every record in Enterpret's context graph arrives through an integration, making this surface critical to downstream accuracy, completeness, and freshness.
Key responsibilities include: managing a portfolio of 50+ integrations prioritized by revenue impact and error frequency; defining and instrumenting system-behavior metrics (completeness, latency, error rates); owning the end-to-end integration setup and management experience; defining per-integration error taxonomies and proactive notification strategies; and building agent-first setup flows where an AI agent inspects the customer's source, proposes configuration, and the customer reviews.
You will think from first principles about how enterprise customer context should reach Enterpret and build a cohesive ingestion layer. Success milestones: in 3 months, own the problem inventory with complete error taxonomy for the highest-usage integration; in 6 months, achieve self-serve setup for most sources with proactive notifications; in 12 months, every integration has a health standard with owned processes for API deprecations and platform changes.
Required: direct product ownership of integrations, connectors, data ingestion, or API products on technical B2B surfaces; fluency in platform APIs (auth flows, webhooks, rate limits, pagination, deprecation cycles); experience defining and instrumenting reliability metrics; comfort operating AI-first.
