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LLM Trace Privacy Proxy
Build a developer-first proxy or SDK that sits between an app and its LLM/logging stack to detect, redact, hash, or drop sensitive data before traces are stored. The strongest value is preventing compliance problems at ingestion time rather than relying on retention cleanup after the fact.
Why this matters
You are a small team moving fast toward launch, and your AI product finally reaches real user traffic. That is when your logs stop looking like test data and start containing names, account details, support histories, and sometimes secrets. You still need traces to debug model behavior, but every extra field stored in production feels like liability. General logging tools help you keep data, not decide what should never be captured in the first place. Retention rules reduce exposure later, yet they do not solve the core problem: sensitive content was already stored. You want a drop-in layer that preserves observability while stripping risk before it enters your systems.
- · Built for Seed-to-Series A startups and small engineering teams shipping AI features to enterprise or EU customers without dedicated privacy engineers..
- · Most likely monetization: SaaS subscription.
The Pain · Narrative
You are a small team moving fast toward launch, and your AI product finally reaches real user traffic. That is when your logs stop looking like test data and start containing names, account details, support histories, and sometimes secrets. You still need traces to debug model behavior, but every extra field stored in production feels like liability. General logging tools help you keep data, not decide what should never be captured in the first place. Retention rules reduce exposure later, yet they do not solve the core problem: sensitive content was already stored. You want a drop-in layer that preserves observability while stripping risk before it enters your systems.
Score Breakdown
Market Signal
Go-to-Market
Founding engineers and platform leads at AI startups selling into Europe or enterprise accounts within the next 6 months.
~30K-80K likely early adopters globally
cold outbound
$199/month
10 paying startups routing at least 25% of production LLM traffic through the proxy within 30 days
MVP Scope · 1–2 weeks
- Build an OpenAI-compatible proxy that forwards requests and responses
- Add basic regex and pattern-based detection for emails, phones, IDs, and API keys
- Implement three actions per rule: redact, hash, or block
- Create a simple dashboard showing flagged fields and volumes
- Ship a lightweight Node.js and Python integration guide
- Add retention controls by route, tenant, and environment
- Integrate with one popular tracing platform via webhook or export
- Create audit logs for every redaction and rule match
- Add allowlists so teams can preserve approved fields for debugging
- Run pilot onboarding with 3 design partners and tune detection thresholds
Differentiation
Why This Might Fail
Self-rebuttal — the most important trust signal
- 1Buyers may conclude that simple middleware plus existing observability settings cover enough of the problem, reducing need for a standalone product.
- 2If the proxy degrades latency or breaks debugging workflows, developers will remove it despite the compliance value.
- 3Large LLM gateways and observability vendors may quickly add comparable redaction features as bundled functionality.
Evidence Summary
How AI synthesized this insight — no verbatim quotes
The discussion repeatedly centered on the tendency for prompts and traces to capture personal data once real users arrive. Multiple commenters emphasized filtering at the source rather than cleaning data later, and several mentioned retention and log configuration as partial but insufficient safeguards. The strongest commercial signal is that this issue appears close to launch and can threaten enterprise onboarding, making prevention software easier to justify.
Action Plan
Validate this opportunity before writing code
Recommended Next Step
Build
Strong demand signals detected. Real pain, real willingness to pay — start building an MVP.
Landing Page Copy Kit
Ready-to-paste copy based on real Reddit community language — no editing required
Headline
LLM Trace Privacy Proxy
Sub-headline
Build a developer-first proxy or SDK that sits between an app and its LLM/logging stack to detect, redact, hash, or drop sensitive data before traces are stored. The strongest value is preventing compliance problems at ingestion time rather than relying on retention cleanup after the fact.
Who It's For
For Seed-to-Series A startups and small engineering teams shipping AI features to enterprise or EU customers without dedicated privacy engineers.
Feature List
✓ LLM API proxy with PII and secrets detection ✓ Configurable redaction, hashing, and block rules before storage ✓ Trace-level retention controls and audit logs ✓ SDKs for popular frameworks and observability tools
Where to Validate
Share your landing page in r/r/webdev — that's exactly where these pain points were discovered.
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