Put policy in the request path
Review what your application sends before it reaches a model. Include prompts, retrieved context and tool arguments in the assessment.
← All productsGive AI context.
02 / AI Data Services
Give AI context.
Keep identifiers back.
A PII proxy between your application and its language model. Apply your data policy before the model call, then control how identifiers return to the user.
Book a demoFor AI product teams, ML engineers and security leads
EXPLORE THE WORKFLOW
Start with a real task
A support request needs an answer. The model does not need the customer’s email to explain how to change a plan.
Support application
- message
- Help Alex Morgan change their plan.
- reply_to
- alex@example.com
Selected personal fields
- person
- Alex Morgan
- alex@example.com
Identify what should be protected and what context the task needs.
Interactive illustration · Fictional data · No live requests
Useful AI assistance with less direct disclosure.
PII proxy · OpenAI-compatible · Your modelsWHY THIS LAYER
Useful context. Controlled disclosure.
Retain the task’s meaning
Evaluate answer quality with protected fields. A customer-support explanation and a calculation may need different treatment.
Fit your model stack
Explore the OpenAI-compatible interface and provider options. Confirm the specific features your application uses during the pilot.
TECHNICAL EVALUATION
What reaches the model—and what comes back?
Define the evidence for your pilot before making a production decision.
Input coverage
Include prompts, retrieved passages and tool arguments in your test set.
Measure missed identifiers and false positives for your actual languages and formats.
Answer quality
Compare protected and unprotected runs on the same synthetic tasks.
Score useful answers, incorrect substitutions and tasks that require original values.
Response path
Establish which recipient may receive restored identifiers.
Exercise streaming, tool calls, retries and a protection-service outage if your workflow uses them.
FROM INTEREST TO INTEGRATION
Evaluate the answer, too.
Define success for one workflow before expanding the scope.
Plan it with an architect- 01
Choose representative prompts
Use synthetic examples of normal requests, edge cases and unsupported inputs. Define which entities need protection.
- 02
Evaluate both sides
Measure detection misses and answer quality. Check streaming and tool use where your workflow depends on them.
- 03
Agree on failure behavior
Define what should happen if protection cannot run. Include logs, retries and authorized response handling in the review.
BEFORE YOU INTEGRATE
The questions
worth asking.
START WITH ONE WORKFLOW
Review your AI workflow
Walk through a synthetic prompt, define protected fields and agree on the checks for a pilot.
Book a 30-minute demoOpens the booking form on provyn.cloudsales@provyn.cloud