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ChatSee.ai

Category: AI Agents

ChatSee.ai provides a failure intelligence platform for enterprise AI agents, helping organizations identify, analyze, and prevent recurring behavioral failures in production AI systems. ChatSee.ai was founded in 2025. The company is led by Sekhar Sarukkai. Based in San Francisco, California, USA. Team size: 11-50. Total funding raised: $6.5M. Latest round: Seed. Key investors include True Ventures, First Rays Venture Partners, Seven Hills Ventures.

Founded
2025
Headquarters
San Francisco, California, USA
Team size
11-50
Total funding
$6.5M

Value proposition

ChatSee provides a failure intelligence layer for autonomous AI systems — capturing the context behind behavioral failures, preserving remediation knowledge, and feeding it back so agents learn and improve over time. It addresses the "confidence gap" enterprises face when moving AI agents from testing to production.

Products and solutions

ChatSee Guardian Agent platform: Behavioral Reliability (tone drift, persona deviation, consistency scoring), Agent Performance Management (goal alignment, task completion tracking), Collaborative Governance (circuit breakers, policy enforcement, audit logging), Discovery & Observability (emergent behavior detection, cross-agent benchmarking), Failure Memory™ (persistent repository of failure incidents with remediation knowledge).

Unique value

Unlike traditional AI observability tools that track what agents do (uptime, latency), ChatSee focuses on behavioral correctness — detecting semantic drift, policy violations, and silent failures that traditional monitoring misses. Its Failure Memory™ architecture ensures enterprises never solve the same AI error twice.

Target customer

Enterprise engineering teams, SREs, AI governance teams, CISOs, and product managers deploying AI agents in production across industries.

Industries served

E-commerce, Financial Services, Enterprise Software, Technology

Technology advantage

Proprietary behavioral taxonomy built on 10,000+ grounded examples of enterprise agent failures classified into 157+ canonical failure modes (now 167 per website). Failure Memory™ architecture for persistent organizational learning. Runtime Guardian Agent architecture that can be deployed on-premise or as SaaS. Gartner-recognized in Market Guide for Guardian Agents.

How they differentiate

ChatSee differentiates by focusing on behavioral correctness rather than system uptime or model quality. Its Failure Memory™ creates a persistent organizational knowledge base of failures and remediations. The platform uses a proprietary taxonomy of 167 canonical failure modes derived from real enterprise agent failures, enabling pattern recognition and automated remediation that generic observability tools cannot provide.

Main competitors

Voker (agent performance platform), Respan (proactive observability for agents), Arize AI (ML/AI observability), Langfuse (LLM observability), Monte Carlo (AI data observability)

Key partnerships

Gartner (included in Market Guide for Guardian Agents), TAG-infosphere (independent research report by Dr. Edward Amoroso)

Major milestones

Founded 2025, Raised $6.5M seed round led by True Ventures (June 2026), Included in Gartner Market Guide for Guardian Agents (2026), Published independent research report by TAG-infosphere (2026)

Market positioning

ChatSee positions itself as a new category — "failure intelligence" — distinct from traditional AI observability. It targets the behavioral control plane layer between infrastructure monitoring (Datadog) and model evaluation (Arize), focused specifically on runtime behavioral assurance for autonomous AI agents in production.

Geographic focus

United States (San Francisco Bay Area)

About Sekhar Sarukkai

PhD in Computer Science from Indiana University; started career at NASA Ames Research Center; Co-founder & CTO of Confluent Software (acquired by Oblix/Oracle); Co-founder & CTO of Securent (acquired by Cisco); Co-founder of Skyhigh Networks (acquired by McAfee, where he became Fellow & Chief Scientist); Lecturer at UC Berkeley School of Information.

Official website: