Transmute de-identifies matched sensitive information before it reaches AI. Its transformation engine refines what flows through your tools, while its data pipeline captures activity that currently goes uncollected—creating raw material for mining, analytics, and insights.
De-identification · Transformation engine · Data pipeline & analytics
Your infrastructure. Your capture policies. Your data.
Useful context in. Sensitive details out. Your call before release.
The diagnosis is useful. The patient’s identity isn’t.
Example: summarize a clinical note without sending the patient’s name or medical-record number.
Healthcare filter packs match structured identifiers. IQ’s local machine-learning models look for named entities in free text, adding context-aware detection for HIPAA-sensitive workflows.
The last mile is yours: inspect, edit, approve.
Clearance holds the transformed request before it reaches the AI provider. Review redactions and attachments, correct what needs attention, then approve the exact payload—or export it without sending it to a model.
Require human approval for 100% of requests routed through an enabled, mandatory Clearance gate.
100% refers to approval coverage, not detection accuracy. IQ requires Max/Enterprise and configured models; Clearance requires a supported tier and enabled policy. Supported formats and model availability determine scan coverage. Transmute supports your HIPAA/PCI program; it does not certify compliance.
The value is in what you can do with the data. Leave matched sensitive identities out, refine the content moving through AI, and capture what currently goes uncollected for your own pipeline and analysis.
01 · De-identification
Leave the identities out.
Give AI the context it needs without the sensitive values your filters match. Replace names, identifiers, and secrets with placeholders; restore the mapped values in responses. Add human review before release where required.
02 · Transformation engine
Refine what flows through.
Define how data changes as it moves through your AI workflow. Apply configured filters to supported text and attachments, preserve useful context, and inspect the transformed payload before it leaves.
03 · Data pipeline & analytics
Mine what went uncaptured.
Capture AI activity that currently goes uncollected. Retain supported records in your own storage, bring sanitized events together, and build a foundation for mining, workflow analysis, and business insights.
Capture, audit, and federation capabilities depend on your edition and configuration. Broader mining and intelligence analytics are the direction we’re building toward.
The App
Make AI useful. Keep sensitive data under control.
Start with the app on your machine. Transform matched secrets into placeholders before they reach a model, then restore them in the response. Audit and dashboard capabilities depend on your plan.
One install, every tool
One local gateway. Plugins auto-install for the LLM CLIs your team already uses, Claude Code, Codex, Cursor, DeepSeek, Ollama.
Transparent redaction
Secrets and PII your packs match become placeholders outbound, then rehydrate inbound. The provider never sees the real value; the developer never notices.
Belt and suspenders
The plugin catches obvious leaks at the agent-loop boundary; the gateway scrubs the wire. If one layer misses, the other catches.
Desktop coverage
Claude Desktop, Cursor, VS Code Copilot, Zed, Continue, routed through the gateway with backup, restore, and health checks per app.
Redacted tokens. Sanitized payloads. Matched PHI never sent in the clear.
What your developer saw
AKIA2X9...REAL_KEY Sarah Johnson eyJhbGci...full_token ...full response
Real values. Full responses. Nothing hidden from the developer.
Platform Capabilities
Take the gloves off your AI agents.
You gave your agent a job, then tied its hands. Let it work. Transmute’s in your corner. You can retire from babysitting.
A pipeline shaped around your work
Adapt capture and transformation to your data and workflows through source-delivered extension points.
Know when the pipeline needs attention
See the health of connected services and recover from failures with managed retries and circuit breakers.
Bring the activity together
Federate sanitized events into your own S3 storage. Explore recorded activity across hosts and teams while raw originals stay on their source machines.
Make transformations accountable
Keep a tamper-evident record of transformations. Review what happened and export evidence for your compliance program.
Process sensitive content on your infrastructure
Run configured detection models in your environment. Add contextual analysis without sending that detection work to a vendor-hosted service.
Preserve useful context
Use supported entity detection and linkage to replace sensitive values while retaining the roles and relationships a model needs.