Sentrial GTM brain

Sentrial helps AI engineering teams trace production agents, detect failures and test fixes before release.

category

AI agent observability and evaluation

market profile

  • text: Production AI agents can fail silently through tool loops, unsupported answers and quality regressions.
  • text: Teams need a feedback loop connecting production sessions with evaluations and release decisions.

personas

  • title: AI engineering lead
    pains:
    • Offline evaluations miss failures that occur in real user sessions
    • Repeated tool calls and hallucinations are difficult to diagnose across agent steps
  • title: Developer building production agents
    pains:
    • Must inspect prompts, tool calls and responses across traces
    • Needs confidence that a prompt or model change fixes a failure without regressions
  • title: Head of AI product
    pains:
    • Agent success rate and user experience degrade without obvious infrastructure errors
    • Latency and token costs are hard to tie to behavior

icp

  • text: Software teams operating customer-facing AI agents or multi-step LLM workflows in production.
  • text: Developer-led startups through larger engineering organizations using Python, TypeScript or OpenTelemetry instrumentation.

message pillars

  • text: Convert production failures into a learning loop.
  • text: Observe complete agent sessions.
  • text: Find behavioral failures before they accumulate.
  • text: Evaluate candidate changes before shipping.

objections

  • objection: We already have infrastructure monitoring.
    response: Show how agent-level sessions connect prompts and tool calls to behavioral failures and test cases.
  • objection: Instrumentation will slow down our team.
    response: Start with the SDK or OpenTelemetry and instrument one agent to evaluate setup effort.
  • objection: We already run offline evals.
    response: Use real failed sessions as additional cases and compare a candidate change against the baseline.

competitors

  • name: LangSmith
    why: Agent tracing, monitoring and evaluations for development teams.
  • name: Langfuse
    why: Open-source LLM tracing and evaluation alternative.
  • name: Braintrust
    why: Evaluation and observability platform for AI applications.
  • name: Datadog
    why: Existing observability platform offers agent monitoring alongside infrastructure telemetry.

keywords

  • AI agent observability
  • LLM production monitoring
  • agent tracing
  • AI agent evaluation
  • LLM regression testing
  • agent hallucination detection

voice

  • Developer-oriented
  • Concise and iterative
  • Evidence-led

signals

  • text: A team has launched an agent into production and is investigating tool loops or incorrect answers.
  • text: Engineering is adding evaluation gates or comparing model and prompt changes before releases.

stack

  • Python
  • TypeScript
  • OpenTelemetry
  • OpenAI SDK

pricing

model: flat
starting price: $10/month

motion

product_led

channels

  • developer documentation
  • LinkedIn
  • search

confidence

high

Founders

  • name: Neel Sharma
    title: Founder/CEO
    role: CEO
    bio: Neel is the founder of Sentrial - production monitoring for AI products. He studied Computer Science at UC Berkeley and worked on agentic optimization at Sense. He saw firsthand that traditional evaluation frameworks break down once agents hit production. That insight led to the creation of Sentrial, a platform built to monitor, evaluate, and improve agent performance in real-world, production environments. contact: neel@sentrial.com

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