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Executive Summary

Enterprise developers building AI agent systems face a thorny identity resolution problem: when an agent in Salesforce needs to hand off to an agent in Zendesk or Stripe, it must match the same customer across systems without leaking PII or creating a centralized honeypot of mapping data. Today they either build bespoke hashing schemes that break when data formats vary, maintain risky centralized identity graphs, or avoid cross-system agents entirely. This blocks high-value use cases like "automatically refund this customer and update their support ticket" that require coordinating multiple SaaS tools. The MVP is a privacy-preserving MCP server that performs private set intersection: two agents submit encrypted identifiers (email, phone, customer ID) and receive only mutual matches without exposing unmatched records or raw PII to either side or the server. Start with a hosted service supporting email and phone matching for the top 10 SaaS platforms, priced at $500/month per production environment with volume tiers at $2K-$5K for enterprises running 50+ agents. Target initial customers among the 140K enterprises already spending on agent orchestration infrastructure, aiming for $30K ACV by bundling the MCP server with integration adapters and compliance documentation. This market is attractive now because MCP protocol adoption gives you instant distribution across Anthropic, LangChain, and LlamaIndex ecosystems, while enterprise AI compliance teams are actively blocking agent projects that cannot demonstrate data minimization. The $4.2B TAM assumes these 140K companies will allocate roughly $30K annually to agent infrastructure including your identity layer, orchestration, and access management. Timing favors a privacy-first solution as GDPR fines and US state privacy laws make centralized identity graphs a board-level risk. You stand out by being the only turnkey private matching solution purpose-built for AI agents rather than generic PIR systems that require cryptography expertise to deploy. The status quo is homegrown hashing (fragile) or AWS Clean Rooms (expensive, slow, not agent-native). Your first validation move is building the core PSI protocol for email matching and getting three design partners from AI-forward enterprises to test it in staging with real CRM and support ticket data. The GTM path runs through AI engineering communities, MCP marketplace placement, and direct outreach to companies posting AI agent jobs. The biggest risk is that enterprises solve this by standardizing on a single SaaS suite or that MCP servers remain a science project rather than production infrastructure, so validate actual procurement interest and multi-tool agent prevalence in your first ten customer conversations.

Executive Summary

Why Now

AI agent frameworks (Anthropic MCP, LangChain, AutoGPT) reached production maturity in 2023-2024, shifting developer focus from proof-of-concept to enterprise deployment where privacy and compliance become mandatory. Regulatory scrutiny on AI data handling (EU AI Act, state privacy laws) makes plaintext identity sharing a dealbreaker for enterprise buyers. MCP protocol adoption is accelerating (Anthropic released spec in late 2023, ecosystem growing rapidly in 2024), creating a narrow window to define the privacy primitive before fragmented solutions lock in. Private set intersection libraries (Google PSI, Microsoft APSI) matured enough for production use in 2022-2023, making implementation feasible without PhD-level cryptography expertise.

Pain Points

AI agent developers building cross-service workflows cannot securely match user identities (email, phone, account ID) without exposing PII to intermediary services or agent platforms.

MCP protocol today provides action primitives (file access, API calls) but lacks a standardized privacy-preserving matching primitive, forcing each developer to build custom cryptographic solutions or accept data exposure risk.

Enterprise customers evaluating AI agent platforms reject solutions that require sharing customer PII with agent orchestration layers, blocking adoption in healthcare, finance, and regulated industries.

Private matching protocol for AI agents needing secure identity resolution

Executive Summary

Private matching protocol for AI agents needing secure identity resolution

Executive Summary

Executive Summary

Why Now

Pain Points

MVP Preview

Market Validation

Market Opportunity

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Validation Questions

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