Contify · 2022 - 2025
Led the vision and launch of a grounded AI insights platform combining enterprise search, a knowledge graph, and multi-model orchestration.
Product point of view
Enterprise AI becomes useful when search relevance, organizational context, and source evidence work as one product system.
01 / Context
Enterprise users needed to answer complex market and competitive questions across fragmented information sources. The product had to improve search relevance, preserve source evidence, and fit the workflows customers already used.
02 / Decisions
A decision register connecting product direction to the evidence and constraints behind it.
Led the vision and launch of a grounded RAG assistant spanning enterprise search, knowledge graphs, multi-model orchestration, and source evidence.
Used telemetry, customer research, and competitive teardowns to prioritize search relevance, workflow, and platform investments.
Expanded the integration surface with 20+ connectors so the product could operate across customers' knowledge environments.
Grew the PM team from one to seven and introduced continuous discovery and product operating rhythms around a shared platform roadmap.
Tradeoffs held in view
The tension stayed visible. The choice made it actionable.
Choice
Combined enterprise search, a knowledge graph, and multi-model orchestration so users could inspect the evidence behind an answer.
Choice
Balanced relevance and workflow improvements with a 20+ integration program that brought enterprise context into the platform.
03 / System
A reconstruction of the product path from fragmented enterprise sources to relevant, evidence-backed answers.
Sanitized reconstruction
04 / Outcomes
Next system / 01