ChatSee.ai, a company developing a failure intelligence platform for autonomous AI systems, announced a $6.5 million funding round led by True Ventures. The round also included participation from First Rays Venture Partners, Seven Hill Ventures, and industry veterans.
The company plans to use the new capital to expand its engineering team and accelerate enterprise deployments.
As enterprises increasingly deploy AI agents built on platforms from OpenAI, Google Gemini, Anthropic, Microsoft 365 Copilot, Salesforce Agentforce, Snowflake, and Databricks, ChatSee aims to address a growing challenge: AI systems that perform well during testing but experience recurring behavioral failures in production environments.
Unlike traditional software issues, many AI failures depend on context, intent, policy interpretation, and business outcomes, making them difficult to detect using static rules and conventional monitoring tools. ChatSee’s platform focuses on preserving failure intelligence and creating a shared organizational memory that captures what failed, why it failed, how it was resolved, and whether similar problems reoccurred.
The company says this approach enables enterprises to continuously improve AI behavior and operational outcomes while reducing recurring issues such as missed escalation triggers, incorrect policy decisions, tool misuse, unintended disclosures, workflow drift, and failures in long-running processes.
ChatSee was co-founded by serial entrepreneur Sekhar Sarukkai, whose previous companies include Skyhigh Networks, Securent, and Confluent Software, and Sanjay Agrawal, Ph.D., whose work has focused on large-scale distributed systems and enterprise AI infrastructure.
Industry analysts have increasingly focused on the need for technologies that monitor and govern AI systems during runtime. ChatSee noted that it was recently included in Gartner’s Market Guide for Guardian Agents in the business alignment and outcome optimization category.
According to the company, its failure intelligence layer complements observability platforms by focusing on behavioral correctness rather than simply tracking what AI agents are doing.
ChatSee’s platform creates what it describes as a shared failure memory, allowing enterprises to move beyond investigating isolated incidents and instead build continuously improving AI systems operating at production scale.
KEY QUOTES:
“Many of the most significant AI risks emerge at runtime as agents operate autonomously. Because these systems are probabilistic and adaptive, static testing alone is insufficient. This is driving the need for continuous runtime assurance across enterprise workflows, with platforms like ChatSee helping organizations observe and improve AI behavior over time.”
Dr. Eduard Amoroso, CEO, TAG-infosphere And Former CISO, AT&T
“When we started analyzing agent failures, we realized the problems seem chaotic but actually fall into repeatable patterns. That’s where observability falls short, it shows what happened, but not whether the behavior was actually correct. We’re discovering that these failures fall into repeatable patterns that can be classified, remediated, and continuously fed back into both human and AI workflows so systems learn and improve over time. This shifts AI operations from humans merely supervising agents to humans and agents collaboratively improving outcomes, turning reactive oversight into continuous, governed AI operations at scale.”
Sekhar Sarukkai, Co-Founder, ChatSee
“AI agents are quickly becoming operational infrastructure inside enterprises. But companies still lack tools to understand when those agents behave incorrectly in production and how to correct these failures at scale. ChatSee is addressing this critical gap in the emerging AI stack.”
Puneet Agarwal, Partner, True Ventures
