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Graph of Plan

Enterprise AI/ML Teams

Enterprise AI/ML teams are specialized groups within large organizations (500+ employees) responsible for developing, deploying, and governing AI systems. They operate under compliance requirements, procurement processes, and organizational complexity.

Segment Profile

Size & Distribution

  • Market: ~15,000 enterprises globally with dedicated AI/ML teams
  • Team size: 5-50 people per AI team
  • Growth: 25% CAGR as enterprises accelerate AI adoption
  • Addressable: Enterprises deploying autonomous agents in production

Pain Points

Governance gaps are career risk. When an AI system makes a decision that affects customers or operations, someone is accountable. Enterprise AI teams need audit trails that satisfy legal, compliance, and executive scrutiny.

Tool sprawl creates blind spots. Large organizations run multiple AI initiatives across different teams. Without centralized visibility, governance becomes impossible.

Procurement is slow. Enterprise buying cycles are 3-12 months. Tools need security reviews, legal approval, and budget allocation across fiscal years.

Build vs. buy tension. AI teams can build internal tooling, but it diverts resources from core AI development. Commercial tools must offer substantial time savings.

Buying Behavior

  • Decision maker: VP of AI/ML, Head of Data Science, or CISO
  • Influencer: Individual contributors who evaluate technical fit
  • Procurement: Legal, Security, Procurement must all approve
  • Budget: £25k-£250k ACV range for infrastructure tools
  • Sales cycle: 3-6 months with POC/pilot phase
  • Reference required: Will call existing customers

Success Metrics

Enterprise AI teams measure tool value by:

  • Compliance audit pass rate
  • Time to investigate incidents
  • Developer productivity (reduced context-switching)
  • Risk mitigation (avoided incidents)

Why Our Products

Nomos Cloud

Primary fit. Nomos Cloud is designed for enterprise AI governance:

  • Tamper-evident audit trails satisfy compliance
  • Decision traces enable incident investigation
  • Centralized visibility across agent deployments
  • SOC 2, HIPAA, and custom compliance options

Key value props:

  • “Chain of truth” for every AI decision
  • Integration with existing AI frameworks
  • Enterprise SSO and RBAC

Murphy

Strong fit. Enterprise R&D teams managing complex delivery programs benefit from:

  • Portfolio-wide visibility across multiple streams
  • Critical chain analysis for dependent workstreams
  • Executive-facing confidence dashboards

SmartBoxes

Supporting fit. Enterprise AI teams building internal tools:

  • Rapid prototyping for AI-powered utilities
  • Sandboxed execution for experimental agents
  • Audit trail integration via Nomos

Acquisition Strategy

Channels

  1. Enterprise inbound: Compliance teams searching for “AI audit trails”
  2. AI framework partnerships: LangChain, AutoGPT, CrewAI integrations
  3. Conference presence: AI/ML enterprise conferences (QCon, MLOps World)
  4. Analyst relations: Gartner, Forrester coverage

Messaging

  • Lead with compliance and governance
  • Emphasize auditability and incident response
  • Show integration with existing AI stack
  • Reference enterprise logos and case studies
  • Provide security documentation upfront

Targeted By