Executive Summary

Many mid-market and enterprise teams deploying RAG for knowledge, support, and sales struggle to keep PII out of embeddings and third-party LLM calls while preserving retrieval fidelity; privacy officers and data engineers face regulatory (GDPR/CCPA) and vendor-policy constraints that can block RAG projects. Without a reliable way to mask identifiers, organizations either accept compliance risk or degrade search quality with over-aggressive redaction. Build a low-latency proxy that deterministically pseudonymizes PII before it reaches embeddings or LLM APIs, preserving consistent tokens so vector similarity and retrieval-augmented generation remain accurate. The product would include configurable policy rules, reversible keystore under customer control, SDKs for major vector DBs and RAG platforms, audit logging, and VPC/on‑prem deployment options for compliance. This targets a $6.0B addressable market (≈200,000 mid-market and enterprise firms × $30k ACV) at a moment of rapid LLM adoption and tighter vendor/data regulations — market score 90/100 and revenue potential 88/100 indicate strong willingness to pay. Standardized vector stores and embedding APIs lower integration friction, making a proxy pattern commercially feasible now. You can differentiate by delivering highly accurate, deterministic pseudonymization tuned for RAG (minimizing false negatives), enterprise security (SOC2, customer key control), and seamless integrations that avoid costly re-indexing, but expect engineering challenges around NER accuracy, latency at scale, and key‑recovery workflows. If you can mitigate those technical risks and demonstrate ROI in pilots, this is a practical, defensible product with medium competition and attractive economics.

Market Opportunity

Consistent pseudonymization proxy to keep RAG useful while hiding PII targets a $6.0B = 200,000 mid-market & enterprise firms × $30,000 ACV for organization-wide privacy+RAG tooling total addressable market with medium saturation and a year-over-year growth rate of 25% YoY (estimated growth for AI privacy, MLOps security and data-protection tooling as reported in analyst notes from Gartner and Forrester).

Key trends driving demand: Enterprise LLM adoption — more teams are deploying RAG for knowledge, support, and sales, creating demand for privacy controls that don't break retrieval.; Regulatory pressure and vendor data policies — GDPR/CCPA and cloud vendor policies increasingly restrict sending raw PII to third-party models, creating urgency for masking/proxy solutions.; Vector DB and RAG platform standardization — standardized vector stores and embedding APIs reduce integration friction and make a proxy integration pattern feasible.; Developer-first security tooling — companies prefer light, API-first security primitives that integrate into CI/CD and runtime pipelines rather than heavy governance projects.; Shift from redaction to pseudonymization — customers realize redaction damages model performance, aligning buyers to solutions that preserve semantics..

Key competitors include Privitar, BigID, Redact.ai.

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Consistent pseudonymization proxy to keep RAG useful while hiding PII

Executive Summary

Market Validation

Market Opportunity

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