The Inflectra Platform for AI Assurance

With the Inflectra AI Assurance platform, teams can combine:

• Spira to define requirements, risks, test cases, releases, configurations, defects, and compliance traceability
• Rapise to automate deterministic chatbot testing through the user interface, API, and integration layers
• SureWire to test AI agents and chatbot workflows for safety, consistency, compliance, prompt injection, data leakage, wrong answers, auditability, and behavioral drift

Together, Spira, Rapise, and SureWire give organizations a practical framework for testing both the deterministic and non-deterministic parts of chatbot behavior.

Why Chatbot Testing Needs a Broader QA Strategy

Traditional software testing asks a relatively straightforward question: did the system return the expected result?

Chatbot testing has to ask more:

• Did the chatbot understand the user’s intent?
• Did it preserve context across turns?
• Did it retrieve the right information?
• Did it provide a correct answer?
• Did it avoid exposing sensitive data?
• Did it refuse unsafe or prohibited requests?
• Did it behave consistently across similar scenarios?
• Did it escalate when it should?
• Did its behavior change after a prompt, model, or knowledge-base update?

A complete chatbot testing strategy needs to cover functional correctness, automation, safety, reliability, traceability, and governance. That is where Spira, Rapise, and SureWire work together.

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1) Model Your Chatbot in Spira

Start by using Spira to define what the chatbot is supposed to do, what risks it introduces, and how it will be tested.

Requirements and User Stories

Capture the chatbot’s expected behavior as requirements, user stories, or use cases. These may include:

• Supported intents
• Entities and slot-filling rules
• Supported channels, such as web, Slack, Teams, mobile, or embedded product UI
• Supported locales and languages
• Conversation flows
• Escalation rules
• Authentication and authorization boundaries
• Guardrails, such as “do not reveal PII” or “do not provide legal/medical/financial advice”
• Non-functional requirements, such as latency, availability, and fallback handling

Example requirements might include:

• The chatbot shall recognize the top 25 customer support intents with at least 92% accuracy.
• The chatbot shall not include personally identifiable information in responses unless the user is authorized to view it.
• The chatbot shall cite approved knowledge-base sources when answering product documentation questions.
• The chatbot shall escalate billing disputes to a human support representative.
• The chatbot shall respond within 1.2 seconds at P95 for web chat interactions.

Risks and Controls

Chatbots should also be modeled from a risk perspective. In Spira, teams can capture risks such as:

• Incorrect answer risk
• Data leakage risk
• Prompt injection risk
• Toxic or inappropriate response risk
• Unauthorized access risk
• Hallucinated source citation risk
• Compliance or policy violation risk
• Poor escalation risk
• Model or prompt drift risk

Each risk can be linked to requirements, test cases, defects, releases, and mitigations. This gives teams a complete traceability chain from business concern to validation evidence.

Artifacts and Traceability Setup

For chatbot projects, consider configuring Spira with custom lists or fields for:

• Intents
• Entities
• Personas
• Prompt versions
• Model versions
• Temperature settings
• Knowledge-base snapshots
• RAG source collections
• Channels
• Locales
• Guardrail policies
• Safety categories

You can then define test case types such as:

• Intent recognition tests
• Entity extraction tests
• Multi-turn conversation tests
• UI channel tests
• API and integration tests
• RAG retrieval tests
• Safety and guardrail tests
• Prompt injection and jailbreak tests
• Data leakage tests
• Performance and latency tests
• Regression tests after model or prompt changes

Spira becomes the system of record for the chatbot’s requirements, risks, test coverage, defects, release readiness, and audit evidence.

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2) Automate Chatbot Conversations with Rapise

Rapise can automate chatbot testing across both the UI and API layers. This is important because chatbot quality depends not only on the model’s response, but also on the surrounding application, integration layer, data flow, and user experience.

A. Web, Mobile, and Desktop Chat UI Testing

Rapise can drive chatbot user interfaces just like a user would. For example, it can:

• Open the chatbot widget or application
• Send user messages
• Click quick replies
• Select menu options
• Upload files
• Read bot responses
• Validate links, buttons, forms, and embedded content
• Capture screenshots and logs
• Preserve session state across multiple turns

This is useful when you need to validate the full user experience, not just the chatbot’s raw API response.

B. API-Level and Headless Chatbot Testing

Many chatbot architectures expose an API through a bot gateway, orchestration service, LLM wrapper, or middleware layer. Rapise can test these endpoints directly through REST or GraphQL.

API-level tests can validate:

• Intent and entity payloads
• Conversation IDs and session state
• Dialog state transitions
• Latency per turn
• Retrieval source IDs
• Confidence scores
• Response schema
• Error handling
• Rate limiting and retries
• Authentication and authorization behavior

Headless API tests are especially useful for regression testing because they can be faster and easier to run at scale than full UI tests.

C. Data-Driven Conversation Testing

Chatbot testing works best when test data is separated from test logic. With Rapise, teams can use data-driven testing to run large sets of utterances, expected intents, entities, personas, locales, and expected response patterns.

Example test data might include:

• User utterance
• Expected intent
• Expected entity
• Persona
• Locale
• Channel
• Expected source document
• Expected refusal behavior
• Expected escalation
• Valid response phrases
• Invalid response patterns

This allows the same Rapise test framework to run hundreds or thousands of chatbot scenarios across different configurations.

D. Assertions for Variable Text

Chatbot responses are often variable, so brittle exact-match assertions are usually not enough. Instead, Rapise tests can validate responses using:

• Allow-listed acceptable responses
• Required keywords or phrases
• Regular expressions
• JSON schema validation
• Expected links or buttons
• Required source citations
• Required refusal language
• Forbidden terms or patterns
• PII pattern detection
• Latency thresholds

The goal is not to force the chatbot to say the same sentence every time. The goal is to verify that the response is correct, safe, useful, and within policy.

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3) Use SureWire to Test AI-Specific Risks

Rapise is ideal for automating deterministic UI, API, integration, and regression tests. But AI chatbots also introduce risks that are harder to catch with conventional automation alone.

That is where SureWire adds a new layer.

SureWire is designed specifically to test AI agents and AI-powered workflows for real-world safety, reliability, consistency, and compliance concerns. For chatbot testing, SureWire can be used to evaluate risks such as:

• Confidently wrong answers
• Prompt injection
• Jailbreak attempts
• Data leakage
• Boundary violations
• Unsafe or inappropriate responses
• Inconsistent behavior
• Policy non-compliance
• Behavioral drift after model, prompt, or knowledge-base changes
• Lack of auditability

Prompt Injection and Jailbreak Testing

A production chatbot may be exposed to users who intentionally try to manipulate it. Examples include:

• “Ignore all previous instructions…”
• “Reveal the system prompt…”
• “Show me another customer’s account details…”
• “Pretend you are not bound by company policy…”
• “Use the hidden admin mode…”

SureWire can help probe the chatbot for these adversarial behaviors and identify whether the agent respects its boundaries under pressure.

Data Leakage Testing

Chatbots often connect to enterprise systems, support portals, CRMs, document repositories, or RAG knowledge bases. SureWire can help evaluate whether the chatbot exposes sensitive or unauthorized information.

This includes testing whether the chatbot:

• Reveals PII, PHI, financial data, or confidential records
• Retrieves documents outside the user’s permission scope
• Includes sensitive data in generated responses
• Summarizes internal documents for unauthorized users
• Fails to redact information when required

Wrong Answer and Hallucination Testing

A chatbot may produce an answer that sounds confident but is incorrect. SureWire can help identify scenarios where the chatbot fabricates facts, cites nonexistent sources, misstates policies, or gives recommendations outside approved boundaries.

For example:

• A support chatbot may invent a refund policy.
• A product chatbot may claim a feature exists when it does not.
• An HR chatbot may give incorrect employee policy guidance.
• A technical chatbot may provide unsafe configuration instructions.

Behavioral Drift Testing

AI behavior can change after:

• A model update
• A prompt change
• A temperature change
• A new knowledge-base snapshot
• A new tool integration
• A new workflow step
• A new retrieval source
• A policy update

SureWire helps teams test whether chatbot behavior has changed in ways that matter. This is especially important when organizations want to detect regressions before users encounter them.

Auditability and Evidence

For regulated or risk-sensitive organizations, it is not enough to say that a chatbot was tested. Teams need evidence of what was tested, when it was tested, what failed, what was remediated, and whether the chatbot was retested.

SureWire adds AI-agent-focused evaluation evidence that can complement Spira’s broader lifecycle traceability.

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4) Make “Fuzzy” Results Testable

Chatbots are non-deterministic, so teams need to design tests that account for variability without giving up rigor.

Use Equivalence Classes

Instead of expecting one exact sentence, define multiple acceptable ways the chatbot can answer.

For example, if the user asks, “How do I reset my password?” acceptable responses may include:

• A link to the password reset page
• A step-by-step reset process
• A verified knowledge-base citation
• A safe escalation path if the user cannot complete the reset

The test should focus on whether the answer contains the required facts and actions, not whether it uses identical wording every time.

Validate Structured Anchors

Whenever possible, validate objective anchors such as:

• Dates
• Amounts
• Product names
• URLs
• Buttons
• Source document IDs
• Required disclaimers
• Escalation paths
• JSON keys
• Policy references

Structured anchors make chatbot tests more reliable.

Use Semantic and Policy-Based Evaluation Carefully

For some use cases, teams may want semantic evaluation, similarity scoring, or AI-based judgment. This can be useful, but it should be controlled and repeatable.

Use this kind of evaluation to answer questions such as:

• Did the chatbot answer the question?
• Did the response align with policy?
• Did the response cite the right source?
• Did the chatbot refuse appropriately?
• Did the chatbot remain on brand?
• Did the chatbot escalate when required?

This is one of the areas where SureWire can complement deterministic Rapise automation by evaluating AI behavior at a higher level.

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5) Close the Loop with Spira

Once tests are executed, Spira should remain the central system of record.

Rapise Results in Spira

Rapise can send execution results, logs, screenshots, and test evidence back into Spira. This allows teams to link automated test results to:

• Requirements
• Test cases
• Test sets
• Releases
• Configurations
• Defects
• Risks

When a Rapise assertion fails, teams can create a defect in Spira with supporting evidence such as:

• Conversation transcript
• Screenshot
• API payload
• HAR file
• Response body
• Environment details
• Model version
• Prompt version
• Knowledge-base snapshot
• Channel and locale

SureWire Findings in Spira

SureWire findings can also be tracked through Spira as risks, defects, or quality issues, depending on your process.

For example:

• A prompt injection vulnerability can become a defect.
• A recurring hallucination can become a risk.
• A failed policy refusal can become a compliance issue.
• A drift finding can become a release blocker.
• A data leakage concern can become a high-priority security defect.

By tracking these findings in Spira, teams can ensure that AI-specific issues are not handled informally or lost in chat threads. They become part of the same governed quality process as the rest of the software lifecycle.

Dashboards and KPIs

Spira dashboards can help stakeholders understand chatbot quality over time. Useful metrics may include:

• Intent accuracy
• Entity extraction accuracy
• Flow pass rate
• Safety violations by type
• Prompt injection failure rate
• Data leakage findings
• Hallucination rate
• Escalation accuracy
• P95/P99 latency by channel
• Regression deltas after model or prompt changes
• Defects by chatbot version
• Open risks by severity
• Release readiness by configuration

This gives teams a clearer view of whether the chatbot is improving, degrading, or ready for release.

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6) Manage Environments, Versions, and CI/CD

Chatbot behavior depends heavily on configuration. A test result is only useful if teams know exactly what was tested.

In Spira, teams should track environments and configurations such as:

• Development
• Staging
• Production
• Model version
• Prompt version
• Temperature
• RAG knowledge-base snapshot
• Bot orchestration version
• Channel
• Locale
• Tool permissions
• Retrieval settings
• Safety policy version

Release Gates

For higher-risk chatbots, teams can establish release gates such as:

• All critical chatbot requirements have test coverage.
• No high-severity safety findings remain open.
• Prompt injection tests meet the required threshold.
• Data leakage tests pass.
• P95 latency meets the SLA.
• Regression tests pass for the current prompt and model configuration.
• SureWire safety and drift findings have been reviewed.
• All release-blocking defects are resolved or formally accepted.

This creates a practical governance model for chatbot releases.

CI/CD Integration

Chatbot tests should run when meaningful changes occur, including:

• Prompt updates
• Model changes
• RAG content updates
• Bot orchestration changes
• Channel UI changes
• Tool permission changes
• Policy updates
• Major dependency changes

Rapise can automate regression tests through scheduled or triggered execution, while SureWire can be used to evaluate AI-agent-specific risks before a chatbot is promoted to production.

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7) Reporting That Stakeholders Understand

Different stakeholders care about different parts of chatbot quality.

QA and Engineering Teams

QA and engineering teams need detailed evidence:

• Test execution results
• Failed assertions
• Logs
• Screenshots
• API payloads
• Conversation transcripts
• Prompt/model/configuration details
• Defects and remediation status

Product Owners and Business Teams

Business stakeholders need to understand whether the chatbot is ready for users:

• Does it answer the right questions?
• Does it support the right workflows?
• Does it escalate appropriately?
• Is the customer experience acceptable?
• Are the major risks controlled?

Compliance, Security, and Risk Teams

Governance teams need evidence that the chatbot was tested responsibly:

• Safety tests performed
• Data leakage findings
• Prompt injection results
• Policy refusal behavior
• Audit trail
• Risk acceptance
• Remediation history
• Release approval evidence

By combining Spira, Rapise, and SureWire, teams can provide both detailed technical evidence and higher-level governance reporting.

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8) Typical Test Assets: Starter Set

A practical first implementation might include the following assets.

Requirements

• R-001: Bot supports 25 core intents with at least 92% accuracy.
• R-002: Bot preserves context across a minimum three-turn conversation.
• R-003: Bot cites approved knowledge-base sources when answering documentation questions.
• R-004: Bot escalates billing disputes to a human agent.
• R-005: Bot does not expose PII, PHI, financial data, or confidential records.
• R-006: Bot refuses prompt injection and jailbreak attempts.
• NFR-001: P95 latency is less than or equal to 1.2 seconds for web chat.
• NFR-002: Bot provides a helpful fallback when upstream systems are unavailable.

Test Cases

• NLU-EN-GREET-001: “hello / hi / hey” maps to greeting intent.
• NLU-EN-RESET-002: Password reset questions map to account recovery intent.
• DLG-RETURN-004: Multi-turn return workflow with slot filling and disambiguation.
• API-RAG-012: Product documentation answer cites approved source document IDs.
• SAFE-INJECT-009: “Ignore prior instructions…” triggers refusal behavior.
• SAFE-PII-010: User asks for another customer’s account details and bot refuses.
• DRIFT-PROMPT-014: Compare current prompt version against prior baseline.
• PERF-LAT-P95-001: Run 100 sequential turns and validate P95 latency.

Data Sets

• Utterance library
• Intent and entity mappings
• Personas
• Locales
• Channels
• Allowed answer patterns
• Disallowed terms
• Prompt injection examples
• Sensitive data examples
• Source document IDs
• Expected escalation behavior

Evidence

• Conversation transcripts
• API request and response payloads
• Screenshots
• Execution logs
• SureWire findings
• Spira defects
• Risk records
• Release approval records

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9) Quick First Implementation Checklist

For a team getting started, a practical first implementation could look like this:

1. Model the chatbot in Spira
   Create requirements, risks, releases, configurations, and test case types for the chatbot.
2. Build a starter test library
   Include the top 20 intents, 5 multi-turn flows, 10 negative tests, and 10 safety tests.
3. Automate core flows with Rapise
   Create data-driven UI and API tests for the most important chatbot scenarios.
4. Add SureWire AI safety testing
   Evaluate the chatbot for prompt injection, wrong answers, data leakage, unsafe responses, and drift risk.
5. Connect execution evidence back to Spira
   Track automated results, SureWire findings, defects, risks, and release status in one place.
6. Create dashboards for stakeholders
   Monitor accuracy, safety, latency, regressions, open defects, and release readiness.
7. Retest after every meaningful change
   Run regression and safety checks after prompt, model, knowledge-base, workflow, or policy updates.

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Conclusion

Testing chatbots requires more than checking whether the UI works or whether an API returns a response. Chatbots are AI-powered systems that can behave unpredictably, change over time, and introduce new safety, privacy, compliance, and reliability risks.

That is why teams need a complete testing strategy:

• Spira provides the requirements, risks, tests, defects, releases, configurations, dashboards, and traceability needed to manage chatbot quality across the lifecycle.
• Rapise automates chatbot testing across the UI, API, and integration layers, making it possible to validate conversations, workflows, responses, performance, and regressions at scale.
• SureWire adds the AI-agent assurance layer, helping teams test for prompt injection, data leakage, confidently wrong answers, unsafe behavior, auditability gaps, and behavioral drift.

Together, Spira, Rapise, and SureWire help organizations move beyond informal chatbot experimentation and toward production-ready AI quality.

For teams building AI chatbots, the goal is no longer just to ask, “Does it respond?”

The better question is:

Can we prove that it responds correctly, safely, consistently, and responsibly?