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KYVERAPPLIED INTELLIGENCE

Every session leaves a trail

When an AI-driven task goes wrong, how do you find out what happened?

You replay the session. Kyver routes AI calls through a pass-through shim that records the prompt, the context retrieved, each tool call, each policy decision and the final output, tied to the user and session. When something goes wrong you can see every step, find the cause and stop it from recurring.

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Session replay: quote assistantsess_41c9e0

  1. REQUEST11:20:04

    user u_2087 (sales): quote 40 units of HX-220 for account 30412

    session opened

  2. RETRIEVE11:20:05ROOT CAUSE

    pricebook search returned rev. C (superseded) ahead of rev. D

    no effective-date filter

    See the fix

  3. MODEL11:20:06

    hosted model drafts quote from rev. C, unit price $41.80

    used retrieved context

  4. TOOL11:20:07

    erp.quotes.create, draft Q-55190

    allowed

  5. POLICY11:20:07

    discount under rep limit

    passed

  6. OUTPUT11:20:08

    quote sent to customer at last year's price

    reported by rep

Post-mortem, closed with a change

  • fixRetrieval filter excludes superseded pricebooks
  • testquote_uses_current_pricebook added to the regression suite
  • checkTrace search: 2 other sessions used rev. C, both corrected
A sales rep asks the quote assistant for a quote. The retrieval step returns a superseded pricebook ahead of the current one, the model drafts the quote from it, the quote passes the tool and policy checks, and it goes to the customer at last year's price. The retrieval step is marked as the root cause. The fix adds a filter for superseded pricebooks, a regression test, and a search for other affected sessions.

How Kyver handles AI traceability

  • A pass-through shim on every call

    A thin layer in front of each model and tool call. Applications call AI the same way they always did, and the shim records each request and response.

  • Traces grouped by session

    Calls are linked into sessions, so you can follow one task from request to result across models, tools and approvals.

  • Search and replay

    Find every session that touched a given customer, vendor or policy, and step through each one.

  • Post-mortems that change something

    Each incident ends in a fix: a new test, a tighter policy or a better prompt. The trace data then shows whether the fix worked.

When, not if

AI systems fail differently from ordinary software. The same input can produce different output, and the cause of a bad result may be a document retrieved three steps earlier.

Something will go wrong eventually. Without a record of each step, the post-mortem is guesswork and the same failure comes back.

Built in alongside traceability

Questions about AI traceability

What is AI observability?

Recording what AI systems do (prompts, retrieved context, tool calls and outputs) so you can monitor quality and investigate failures. It is the AI equivalent of application logging and tracing.

Does tracing store sensitive data?

A trace can contain anything the AI saw. We set redaction rules for regulated fields, keep traces in your environment, and apply the retention limits you approve.

Does the shim slow responses down?

Very little. Recording happens alongside the call, and the overhead is small next to the time a model takes to respond.

Start with the assessment

Fixed price, agreed up front. Implement the plan with us within 6 months and we refund the fee.

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