Product
Conversational AI Platform Guide: Evaluate the Operating System
Evaluate a conversational AI platform by the customer tasks it can support, the evidence and controls it exposes, and the operating work required after launch.
A conversational AI platform is the operating layer that receives voice or text interactions, maintains conversation state, uses approved knowledge, invokes permitted tools, applies policies and guardrails, transfers work to people, and exposes evidence for testing and operations. Evaluate it against your real customer tasks—not a generic feature list.
Define the evaluation packet first
- Two or three bounded customer tasks with observable completion events.
- Representative channels, languages, accessibility needs, and peak conditions for the intended release.
- Approved knowledge sources and freshness owners.
- Exact systems, reads, writes, permissions, and confirmations.
- Policy, identity, privacy, risk, and customer-requested handoff conditions.
- Human destinations, operating hours, context requirements, and outage fallbacks.
- Baseline measures and a versioned acceptance-test set.
This packet lets every vendor demonstrate the same work. It also prevents a broad platform promise from being mistaken for proof that a particular workflow, integration, language, or operating condition is ready.
Platform evaluation matrix
| Area | Evidence to request | Acceptance question |
|---|---|---|
| Channels | Configured voice and text samples, interruption and latency tests, fallback | Can customers complete or exit the intended task? |
| Knowledge | Source ownership, retrieval trace, freshness and conflict behavior | Does each answer use an approved current source? |
| Actions | Tool schema, permission, confirmation, idempotency, reconciliation | Can it perform only the intended operation once? |
| Guardrails | Policy enforcement, refusal, escalation, customer choice | Do boundaries hold under ambiguity and manipulation? |
| Integrations | Operation-level contract and failure tests | Are reads, writes, errors, retries, and outages defined? |
| Handoff | Trigger, destination, context packet, unavailable route | Does a reachable person receive usable context? |
| Operations | Logs, sampling, alerts, versioning, incident and change process | Can owners detect, explain, and manage behavior? |
Evaluate task completion, not conversational polish
Natural dialogue matters, but it is only one layer. Ask the platform to handle a correction, conflicting source, failed identity check, denied action, tool timeout, duplicate request, explicit human request, and unavailable destination. Inspect the resulting system state and audit evidence—not only the transcript.
Use the voice-versus-chat guide to decide which modality constraints belong in the evaluation, and the orchestration guide to examine tool selection, state, approvals, and handoff behind the conversation. voice AI versus chat AI guide · AI agent orchestration guide · foundation guides hub
Inspect knowledge behavior
- Provide a supported question and verify the approved source used.
- Provide an uncovered question and verify the system does not invent policy.
- Introduce two conflicting sources and check the ownership or escalation rule.
- Change a source and verify freshness, cache, indexing, and release behavior.
- Remove source access and confirm the customer receives an accurate recovery path.
- Test customer-specific information only with synthetic authorized records.
The platform should distinguish retrieved evidence from generated language and expose enough traceability for reviewers to reproduce important outcomes. Define what the customer sees, what operators see, and what is retained.
Inspect action and integration controls
For each action, request the operation name, input schema, authorization context, least privilege, confirmation rule, timeout, retry behavior, duplicate protection, success evidence, partial-completion handling, and manual recovery. The customer-service AI integrations guide provides a full contract and test matrix. omnichannel versus multichannel guide
Run a scored proof of concept
| Score | Meaning | Release consequence |
|---|---|---|
| 0 | Not demonstrated or evidence unavailable | Open research item; cannot satisfy this requirement |
| 1 | Demonstrated only in a prepared path | Add realistic and failure testing |
| 2 | Passes representative tests with limitations | Document limitations and owner |
| 3 | Passes configured acceptance set with operating evidence | Eligible for the defined release scope |
Weight requirements before seeing results. A total score should never hide a zero on a mandatory identity, authorization, confirmation, human-handoff, or recovery requirement. Retain test inputs, configuration versions, expected behavior, observed result, reviewer, and disposition.
Check accessibility, governance, and claims
For web interfaces, WCAG 2.2 supplies testable accessibility criteria. NIST’s AI RMF supplies a lifecycle structure for governance, mapping, measurement, and management. Use both in the context of the implemented platform and obtain any other qualified reviews the workflow requires. W3C WCAG 2.2 · NIST AI Risk Management Framework
Ask vendors to scope every claim to the tested configuration, dataset, task, population, period, and measurement method. The FTC advises businesses to substantiate AI performance, effectiveness, and comparative claims. FTC guidance on AI claims
Commercial and operating checklist
- Map price units to the expected traffic and scenario range using your own volumes.
- Identify implementation, integration, source preparation, monitoring, QA, support, and change costs.
- Record data locations, subprocessors, retention choices, export and deletion processes for qualified review.
- Define service levels, support escalation, incident notification, version changes, and exit assistance in the actual agreement.
- Confirm that required evidence can be exported and owned by the responsible team.
- Document which capabilities are native, configured, integrated, marketplace-provided, or planned only after evidence reconciliation.
Use one evaluation packet, one acceptance set, and one evidence register across every platform demonstration.
Explore AI customer serviceQuick answers
Frequently asked
What is a conversational AI platform?
It is an operating layer for voice or text conversations that can manage state, use knowledge, invoke permitted tools, apply policies, hand work to people, and provide testing and operational evidence.
How do I compare conversational AI platforms?
Give each platform the same bounded tasks, sources, systems, failure cases, handoff requirements, and acceptance criteria. Compare configured evidence and operating fit, not feature counts alone.
What should a conversational AI proof of concept include?
Include representative normal work plus ambiguity, correction, unsupported knowledge, authorization failure, denied or duplicate action, timeout, outage, human request, accessibility, and unavailable handoff.
Which platform requirements should be mandatory?
Mandatory requirements depend on the workflow, but identity, authorization, consequential-action confirmation, customer choice, human escalation, recovery, logging, and ownership should not disappear inside an aggregate score.
Evaluate the complete operating workflow
Use the article's artifact with your own tasks, systems, evidence, reviewers, and release criteria.
