CTGT GTM brain

category

market profile

• text: Financial and other regulated enterprises need evidence that AI outputs follow organizational policies and can be audited.
• text: Enterprise AI safety vendors use differing approaches, including model guardrails, monitoring and intervention inside models.

personas

• title: Head of enterprise AI
  pains:

  • High-stakes deployments stall when outputs are unreliable
  • Cannot scale pilots without demonstrating policy conformance
• title: Financial services compliance or model risk leader
  pains:

  • Client communications and research must satisfy evolving regulations
  • Needs an audit trail tying generated output to policy
• title: ML platform engineering leader
  pains:

  • Prompt and RAG controls require ongoing maintenance
  • Retuning every model for each policy change consumes engineering capacity

icp

• text: Large financial institutions using generative AI for client communications, research summaries or internal assistants.
• text: Enterprises in high-trust domains with complex internal policy and on-premises or VPC deployment requirements.

message pillars

• text: Enforce organizational policy at output time.
• text: Translate business documents into enforceable rules.
• text: Provide traceable policy decisions for auditors.

objections

• objection: We already use prompts and guardrails.
  response: Compare the policy engine with existing controls using representative internal policies and a bounded test set.
• objection: Compliance needs proof before production.
  response: Review audit trails and evaluate deployment security and benchmark methodology with model risk stakeholders.

competitors

• name: AWS Bedrock Guardrails
  why: Offers guardrails for enterprise generative AI and is compared on CTGT's product page.
• name: Microsoft Azure AI Content Safety
  why: Cloud AI safety controls compete for policy enforcement budgets.
• name: Fiddler AI
  why: Offers real-time AI guardrails and observability for enterprise deployments.

influencers

• name: Cyril Gorlla
  url: https://www.rippling.com/blog/founder-spotlight-cyril-gorlla
  why: CTGT co-founder interviewed about enterprise AI reliability.

keywords

• AI policy enforcement
• enterprise AI governance
• LLM guardrails
• mechanistic interpretability
• financial services AI compliance
• AI model audit trails

voice

• research-led
• risk-focused
• technically confident

signals

• text: A bank is moving generative AI from pilots into regulated client-facing workflows.
• text: An enterprise is formalizing behavioral controls and auditable policy enforcement for AI agents.

stack

• Azure
• Amazon Bedrock

hiring

• Machine Learning Engineer: LLM Interpretability & Systems

pricing

motion

channels

• research content
• events
• press
• LinkedIn

confidence

Founders

• name: Cyril Gorlla
  title: Founder/CEO
  role: CEO
  bio: Cyril left his research at Stanford/UCSD to found CTGT. His work on efficient and interpretable AI was presented at AI conference ICLR while he held the Endowed Chair's Fellowship at the University of California San Diego. He is a Nordson Leadership Scholar and Ivory Bridges Fellow.