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Redisq Technologies AI runtime control plane
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Where teams start first

Use cases that make Redisq click immediately.

These are the first workflows where a trace, a finding, and a report create clear value fast.

Support chatbot

Customer conversations with sensitive data

Capture customer inputs, detect PII, and hand reviewers a session-level report when trust is on the line.

  • Common events: user.input, llm.request, llm.response
  • Main risk: personal data leakage
  • Primary buyer: support leadership and security
AI copilot

Internal assistants used by employees

Monitor prompts and responses when internal teams ask questions that may contain customer or company-sensitive information.

  • Common events: user.input, retrieval.query, llm.response
  • Main risk: confidential data exposure
  • Primary buyer: AI platform and IT
Document workflow

Uploads, summarization, and review flows

Track how AI handles contracts, claims, forms, and uploaded text so teams can review outputs with context.

  • Common events: user.input, system.message, llm.response
  • Main risk: hidden sensitive clauses or fields
  • Primary buyer: operations and legal ops
Agent with tools

Multi-step workflows touching systems

Reconstruct what happened when an agent calls external APIs, tools, retrieval systems, or internal databases.

  • Common events: tool.call, tool.result, policy.note
  • Main risk: unsafe actions or hidden dependencies
  • Primary buyer: engineering and platform
Governance review

Security and compliance sign-off

Give non-engineering stakeholders a clean narrative for how one AI workflow behaved without dumping raw logs on them.

  • Common output: session report and finding evidence
  • Main risk: approval delays caused by ambiguity
  • Primary buyer: security, compliance, governance
Design partner path

How we recommend getting started

Pick one narrow workflow, send one realistic session, then use the resulting trace and report to decide where Redisq should sit in production.

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