What is historical ticket simulation?

Historical ticket simulation runs your previously resolved support tickets through TR-AI's current answer-generation pipeline in a completely read-only environment. The AI generates proposed responses, which are then evaluated against the original human resolutions — without sending anything to customers or modifying any tickets.

Does simulation send messages to customers?

No. Simulation is entirely non-destructive. No customer-visible replies, ticket status changes, or external actions of any kind occur during a simulation run. This is enforced at the system level, not just by configuration.

Can I test with existing Zendesk tickets?

Yes. The Simulation Lab connects to your authorised Zendesk integration and retrieves historical resolved or closed tickets for testing. Only read access is used — the integration never writes to Zendesk during simulation.

How are AI responses evaluated?

Each response is scored across ten dimensions: factual accuracy, relevance, completeness, groundedness, citation correctness, troubleshooting usefulness, policy compliance, hallucination risk, escalation appropriateness, and professionalism. A composite quality score is calculated from these dimensions, with criterion-level explanations stored for each ticket.

Can I compare models or prompts?

Yes. You can run multiple simulations against the same ticket set using different AI models, system prompts, retrieval settings, or confidence thresholds. The results dashboard shows side-by-side metric differences and highlights cases where a newer configuration performs worse.

Can simulation predict real ticket resolution rates?

Simulation estimates automation suitability — the proportion of tickets where the AI produced a quality-passing response. This is not the same as a verified real-world resolution rate, which requires customer confirmation or independent validation. Results are clearly labelled as estimated automation eligibility.

What happens when historical documentation is unavailable?

If the exact historical context for a ticket cannot be reconstructed reliably, the ticket is flagged with a temporal fidelity warning in the results. The simulation explains the limitation rather than silently producing a potentially misleading evaluation.

Is customer data protected?

Yes. All simulation data is strictly tenant-isolated — no data is shared between accounts. Historical ticket content is never published on public pages. PII minimisation is applied during processing, and all credentials are encrypted at rest.

AI Simulation Lab

Know How Your AI Will Perform Before It Answers a Customer.

Replay historical support tickets in a safe testing environment. Compare AI responses with previous support outcomes, measure response quality, and discover automation opportunities before deployment.

Run Your First Simulation Request a Demo

How It Works

Connect Your Helpdesk

Securely authorise read-only access to your historical ticket data. No customer-visible changes are ever made during simulation.

Select Tickets

Choose products, categories, date ranges, and sample sizes. Filter by priority, language, or issue type to focus on the tickets that matter most.

Run the Simulation

TR-AI generates proposed responses without contacting customers or modifying any tickets. The original resolution is never shown to the AI before it answers.

Review the Results

Inspect answer quality, source coverage, escalation behaviour, and estimated automation suitability — side-by-side with the original human response.

Improve and Retest

Update your knowledge base and AI settings, then compare results across runs to measure progress before enabling live automation.

What the Simulation Lab Includes

Every tool you need to evaluate AI readiness before enabling live automation.

Safe Historical Ticket Replay

Reconstruct the information available at ticket creation time. Future resolutions are never leaked to the AI generation context.

Side-by-Side Answer Comparisons

View the AI-proposed response alongside the original human resolution. Spot gaps, hallucinations, and missed escalations at a glance.

Source-Grounded Quality Evaluation

Every response is scored across ten dimensions including factual accuracy, groundedness, completeness, and policy compliance.

Knowledge Gap Identification

Flag tickets where missing or outdated KB content prevented a good answer. Prioritise knowledge improvements before going live.

AI Configuration Benchmarks

Compare different models, prompts, retrieval settings, and confidence thresholds against the same frozen ticket set.

Shadow Testing Before Launch

Evaluate new incoming tickets silently — no reply sent, no ticket modified — and compare AI recommendations with subsequent human outcomes.

Exportable Readiness Reports

Download full simulation results as CSV or JSON. Share evidence of automation readiness with stakeholders before deployment.

Automation Readiness Insights

Understand which ticket categories are ready for automation and which still require human review — before a single live reply is sent.

See It in Action

The interface below shows synthetic demonstration data. Real simulations use your own historical tickets.

AI Simulation Lab

Demo data — not real results

Tickets

60%

Automation-Eligible

78%

Accuracy

KB Gaps Found

Unable to export report as PDFresolved94% conf.

Billing charge not matching invoicereview71% conf.gap

SSO login failing for new usersresolved88% conf.

API rate limit exceeded — 429 errorstriage52% conf.gap

How do I add a team member?resolved97% conf.

Why Teams Use Simulation Lab

Reduce AI deployment uncertainty.

Know how your AI will perform before it answers a real customer. Replace guesswork with evidence from your own historical data.

Discover weak knowledge coverage before customers encounter it.

Identify the exact topics and categories where your KB is incomplete, so you can fix them before they affect live tickets.

Measure progress as your support knowledge improves.

Rerun simulations after KB updates to track quality improvements over time and build confidence in your automation readiness.

Launch automation with clearer evidence and controls.

Present stakeholders with reproducible quality metrics, not vendor promises. Enable automation incrementally, category by category.

Built for Trust and Privacy

Simulation Lab is designed to give you confidence in your AI — without compromising your customers' data.

Read-only simulation

No customer-visible replies, ticket status changes, or external actions during any simulation run.

Tenant-isolated data

All simulation data is strictly scoped to your account. No data is shared between tenants.

Authorised ticket access

Only tickets from your authorised helpdesk integration are used. Access is read-only.

No customer-facing messages

AI-generated responses are stored for review only. Nothing is sent to customers during testing.

Source-grounded evaluation

Every response is evaluated against retrieved KB sources, not just compared to the human reply.

Human review controls

Reviewers can flag knowledge gaps, add comments, and mark evaluations correct or incorrect.

Frequently Asked Questions

Stop Guessing. Start Testing.

Discover how TicketResolve.ai performs on historical support requests before you activate live AI automation.

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