Executive Summary

Enterprises building RAG pipelines are stuck between two bad options: send raw documents to third‑party LLMs and expose PII, or aggressively redact and degrade retrieval performance; security, legal and procurement teams at mid-market and large companies face this daily. Buyers are increasingly blocking AI integrations over data‑sharing risk, creating stalled projects and brittle workarounds. You could build a transparent proxy that intercepts outbound data, automatically pseudonymizes detected PII while preserving semantic structure for embeddings, and forwards only transformed text to LLMs and vector providers. Ship it as an HTTP/middleware proxy with SDKs, on‑prem and cloud options, configurable detection models, and an evaluation suite that measures embedding drift and retrieval effectiveness. The market is attractive and tangible: roughly 2M addressable businesses at a $3K ACV implies a $6.0B service opportunity, and our market score (88/100) and revenue potential (80/100) reflect accelerating enterprise RAG adoption and stricter procurement standards. Vector search is now mission‑critical for knowledge apps, so solutions that demonstrably reduce exposure without harming semantics are in demand. You can differentiate by delivering provable, low‑latency semantic preservation (benchmark embedding drift <5%), audit trails, and turnkey integrations with major vector stores and LLMs—capabilities that generic DLP or redaction tools struggle to match. Be upfront about the challenges: high‑precision PII detection across languages and formats, edge cases that affect downstream retrieval, and the need for third‑party audits and strong enterprise sales to build trust.

Market Opportunity

Proxy that pseudonymizes PII to preserve RAG embeddings targets a $6.0B = 2M businesses × $3K ACV total addressable market with medium saturation and a year-over-year growth rate of 25% YoY — based on combined growth estimates for AI governance, data-protection tooling, and enterprise LLM adoption (Gartner/MarketsandMarkets synthesis).

Key trends driving demand: Enterprise adoption of RAG architectures is accelerating, meaning more business data is sent to LLMs — creating demand for privacy-preserving middleware.; Regulation and procurement scrutiny around data sharing with third-party AI providers is increasing, pushing companies to adopt tools that demonstrably limit exposure.; Vector search and embedding quality are business-critical for retrieval; solutions that protect PII without harming semantics unlock product value for knowledge-heavy apps..

Key competitors include Microsoft Presidio (open source), Amazon Comprehend PII, Gretel.ai.

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Proxy that pseudonymizes PII to preserve RAG embeddings

Executive Summary

Market Validation

Market Opportunity

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