Axiomat GTM brain

category

market profile

• text: A plausible AI-generated SQL query may execute successfully but still misinterpret business semantics.
• text: Semantic layers and AI analytics products are adjacent alternatives; formal verification makes a distinct correctness claim.

personas

• title: Head of data or analytics engineering
  pains:

  • Different definitions of revenue or customer counts produce confident but inconsistent answers.
  • Analysts must manually check generated SQL before stakeholders can trust it.
• title: AI product engineering leader
  pains:

  • A customer-facing data agent cannot be released safely when incorrect answers go undetected.
  • Business logic changes are difficult to enforce in generated queries.

icp

• text: Data-rich organizations with a defined schema and business rules whose employees or customers ask questions of structured data.
• text: Teams developing data-querying agents with high costs of incorrect financial or operational answers.

message pillars

• text: Tie answers to the actual rules of the business.
• text: Use existing data and documentation.
• text: Make the cost of wrong answers tangible.

objections

• objection: Our semantic layer already defines metrics.
  response: Compare coverage and verification guarantees on a representative set of business questions, including cases where the query must be rejected.
• objection: Can the system handle our business definitions without moving sensitive data?
  response: Review its logical model and data access in a scoped demo; the site says data stays in place.

competitors

• name: Omni
  website: https://omni.co/articles/text-to-sql-vs-semantic-layer
  why: Governed semantic analytics can provide an alternative path to consistent AI answers.
• name: AtScale
  website: https://www.atscale.com/blog/semantic-layer-text-to-sql-accuracy/
  why: A semantic layer grounds AI data queries in governed metrics.
• name: ThoughtSpot
  why: Spotter offers an AI analytics alternative for questions about business data.

keywords

• AI SQL correctness
• text to SQL hallucination
• verified data queries
• AI analytics semantic layer
• AI agent data accuracy
• formal verification SQL

voice

• Direct and confident
• Business-outcome focused
• Technical but accessible

signals

• text: An organization delays an AI analytics launch because generated queries need human review.
• text: A company is standardizing definitions for revenue, customers or other high-stakes metrics.

pricing

motion

channels

• LinkedIn

confidence

Founders

• name: Ethan Kharitonov
  title: Founder/CEO
  role: CEO
  bio: Ethan is a founder of Axiomat (YC F26). Previously, he was an Applied Scientist at AWS researching formal methods and AI verification - the only employee in Amazon’s history to move from a software role into applied science within eight months of joining. He has published research in formal methods and left his PhD at Georgia Tech to build Axiomat. He holds a B.S. in Mathematics and Computer Science from the University of Toronto, where he also led research in functional analysis and topology.
• name: Jack Naimer
  title: Founder/COO
  role: COO
  bio: Jack is a founder of Axiomat (YC F26). Previously he was a Master's Research Scholar in AI at EPFL, where he led pretraining of a large-scale DNA foundation model for drug discovery and cellular engineering. Before that he built the first DNA reasoning model in Bo Wang's lab, and he led the development of a machine learning model for the Peter Munk Cardiac Centre. He holds a B.A.Sc. in Engineering Science, majoring in Machine Intelligence, from the University of Toronto.
• name: Elad Perlman
  title: Founder/CTO
  role: CTO
  bio: Elad is a founder of Axiomat (YC F26). Previously, he worked as a full-stack engineer at Survalent, developing real-time control software for utilities. He holds a B.S. in Mathematics and Computer Science from the University of Toronto, where he graduated with high distinction.