AI Incident Evidence Review

Investigate AI failures
and prove the fix worked.

Bring one problematic workflow or risky release. TalosRed uses the traces and logs you already generate to reconstruct what happened, identify affected sessions, and return a defensible before-and-after report.

01

Import traces or logs

02

Map fields

03

Run rubrics

04

Review findings

05

Export evidence

No production routing change required for the first evaluation
Review an AI Incident
See how it works

//The Problem

AI teams already have telemetry.
They still lack defensible evidence.

Telemetry shows activity, not verdicts

Tracing and logs can prove a workflow ran, but they do not tell release owners whether the behavior was safe, stable, or policy-aligned.

Release reviews become judgment calls

Model updates, prompt edits, and tool changes create regressions faster than manual QA can keep up. Teams need a repeatable way to decide ship, fix, or hold.

Incidents and audits turn into evidence hunts

When customers, security, or procurement ask what happened, screenshots and raw spans are not enough. Teams need findings, examples, and a report they can reuse.

Core question

"Can we make a defensible ship, fix, or investigate decision from the telemetry we already have?"

TalosRed starts here

//How It Works

Telemetry-native review.
Not another dashboard.

TalosRed works from OpenTelemetry/OpenInference traces, prompt-response logs, and curated datasets. Map the fields, run domain rubrics, review flagged behavior, and export a repeatable evidence package.

Ingest reviewable telemetry

Start with traces, logs, or datasets you already have. No live proxy cutover is required for the first review.

Apply release and risk rubrics

Evaluate behavior against safety, quality, compliance, and workflow-specific criteria tied to the decision in front of your team.

Export evidence your team can reuse

Share trace-linked findings, risk patterns, and a versioned report that can support the next release review, incident review, or diligence request.

Evaluation report
Trace-linked case findingsReviewed
Risk category breakdownScored
High-severity examplesFlagged
Retest baseline for future changesSaved
Operational resultDefensible decision

//Why Teams Buy

Observability shows activity.
TalosRed packages evidence.

TalosRed acts as the evidence layer on top of the telemetry you already collect, helping teams find risky AI behavior, support release decisions, and create a reusable baseline for the next review.

Not replacing observability.

Your tracing stack is the substrate. TalosRed is the evidence layer on top: it turns traces, logs, and curated datasets into findings, evidence reports, and retestable baselines for AI release assurance.

Review one AI failure
Observability stack
TalosRed
Capture spans, logs, and request flow
Turn behavior into reviewable findings
Answer what happened
Answer whether it is safe to ship or escalate
Useful for live debugging
Useful for release review and incident review
Store telemetry history
Export evidence and retest baselines
Broad observability substrate
Decision layer for AI risk and quality

What your team gets back

Workflow and session reconstruction
Suspected failure origin
Affected-session estimate
Missing-evidence assessment
Before/after remediation comparison
Trace-linked findings
HTML/JSON evidence report
Reusable regression baseline

Investigate an incident

Reconstruct what happened, find the affected sessions, and identify the evidence needed to act.

Prove remediation

Compare before and after behavior so teams can show that a fix worked, not just that it shipped.

Review a risky release

Turn a prompt, model, or workflow change into a repeatable ship, hold, or fix decision.

Prepare for diligence

Export structured evidence for customer, security, procurement, or audit questions.

Private design partner program

Bring one risky workflow.
Leave with evidence.

We work with teams already shipping AI. Share OpenTelemetry/OpenInference traces, prompt-response logs, or a curated dataset, and TalosRed turns them into findings, an evidence report, and a repeatable review baseline.

Review one AI failure

Best fit

  • AI product and platform teams
  • Teams with a recent failure or blocked release
  • Enterprise-facing or regulated AI products
  • Teams already instrumenting OTel/OpenInference
  • Buyers facing security or procurement review

Data handling

  • OpenTelemetry/OpenInference traces accepted
  • Prompt-response logs or datasets accepted
  • No proxy deployment required for first review
  • Anonymized exports are accepted
  • Founder-assisted scoping available before upload

Start with the evidence layer

TalosRed starts with telemetry-native evidence for release assurance, incident review, and audit readiness. From there, teams can expand into recurring regression checks, policy monitoring, and broader control workflows.