ModelTrust
Investor prospect overview
Thesis Traction Moat Request the deck →
● Private discussions • NDA available • Investor materials on request

Trust, policy, and control for AI agents—built for real-world systems.

ModelTrust helps organizations safely deploy AI-driven automation across tools, APIs, and sensitive workflows with governance, approvals, and auditability—so “agentic” systems don’t become a security incident.

Policy enforcement
Audit trails
Permissioned tool access
Enterprise integrations
8
Design partners / pilot conversations
$1.2M
Qualified pipeline (next 12 months)
18%
MoM pipeline growth (last 90 days)
$2.5M
Target raise (seed) • rolling close
70/20/10
Use of funds: product / GTM / compliance
18 mo
Planned runway post-close

Investment thesis

AI agents are moving from generating recommendations to executing actions inside real tools and systems. Enterprises need a trust layer that enforces permissions, validates intent, and produces audit-ready evidence before agents can perform sensitive operations in production.

  • Why now: rapid adoption of tool-using AI is outpacing governance, security, and compliance readiness.
  • Buyer pain: preventing unintended actions, data leakage, and “over-permissioned” automation across APIs and SaaS tools.
  • Outcome: faster automation rollout with policy controls, approvals, and measurable risk reduction.

Moat & defensibility

ModelTrust is designed as the control plane between agents and execution, combining policy enforcement, verifiable audit trails, and broad interoperability—creating high switching costs as deployments expand.

  • Policy boundary: a central enforcement point for tool access, approvals, and least-privilege execution.
  • Evidence-grade audit: tamper-resistant logs and decision context designed for compliance and incident response.
  • Interoperability: connectors and standards-based integration into existing stacks (identity, apps, APIs).

Business model

Pricing scales with customer footprint and criticality, with a clear expansion path from pilots to enterprise-wide rollout.

  • SaaS subscriptions: tiered by tenant/workspace, number of integrations, and policy/audit features.
  • Enterprise licensing: annual agreements with premium support, security reviews, and rollout assistance.
  • Partner channel: systems integrators and security vendors resell and implement within larger programs.

Roadmap

A focused path from validated pilots to repeatable deployments, with compliance readiness and distribution built in.

  • 0–3 months: finalize MVP scope with design partners; ship hardened reference architecture and core policies.
  • 3–6 months: production pilots; expand top integrations; launch compliance and security documentation pack.
  • 6–12 months: repeatable onboarding; partner-led deployments; broaden policy library and enterprise controls.
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