CONVERSATIONAL AI
Build conversations that are natural, grounded and operationally controlled.
Combine language understanding, approved knowledge, dialogue policy and channel-aware response design to support useful interactions without losing business context or human ownership.
Intent, entities and ambiguity
Approved knowledge and live context
Channel-aware dialogue orchestration
Quality, safety and outcomes
CAPABILITY ARCHITECTURE
A conversation pipeline built around evidence.
Good conversational AI does not jump directly from customer input to generated text. It resolves context, grounds the response, applies policy and adapts delivery to the channel.
Input normalization
Normalize text, speech transcripts, language, message metadata and conversation state.
Intent and context
Interpret goals, entities, urgency, ambiguity and relevant history for the current turn.
Knowledge grounding
Retrieve approved information and business facts, preserving source and freshness signals.
Dialogue policy
Choose whether to answer, ask, confirm, call a tool, refuse or transfer based on policy.
Channel response
Shape length, timing, format, media and turn-taking for SMS, RCS, voice or a human workspace.
PRODUCTION CAPABILITIES
Design for the difficult parts of conversation.
Professional dialogue systems handle uncertainty, repair and channel constraints—not only ideal questions.
Intent and entity handling
Recognize the user goal and required details, while allowing clarification when confidence is insufficient.
Grounded generation
Generate from approved knowledge and live business context, with source-aware behavior when information is missing.
Conversation memory
Retain task-relevant state across turns while applying data minimization, expiry and user boundaries.
Dialogue repair
Recover from misunderstanding, missing fields, conflicting information and repeated failure without trapping the user.
Multilingual design
Manage language detection, terminology, locale, tone and escalation quality instead of relying on literal translation.
Channel adaptation
Respect SMS brevity, RCS interaction patterns, voice turn-taking and the needs of human agents.
OPERATING MODEL
Six decisions in every useful turn.
The response is only the visible output; the quality depends on the decisions behind it.
LISTENCapture meaning
Normalize the utterance and channel signals.
RESOLVEFind context
Identify the customer, task, state and missing information.
GROUNDRetrieve evidence
Use approved knowledge and current business facts.
POLICYChoose behavior
Answer, clarify, confirm, act, refuse or hand off.
COMPOSEAdapt the response
Match language, channel, tone, length and interaction pattern.
OBSERVERecord the outcome
Capture quality signals, actions, feedback and unresolved states.
GOVERNANCE & CONTROL
Evaluation must cover more than fluent language.
A polished answer can still be incorrect, unsafe or operationally useless.
Groundedness
Check whether claims are supported by approved context and whether missing evidence is handled honestly.
Task correctness
Verify extracted fields, workflow state, tool parameters and final business outcome.
Conversation quality
Measure clarity, relevance, coherence, repair behavior and unnecessary turns.
Safety and privacy
Test policy boundaries, sensitive data handling, prompt attacks and inappropriate disclosure.
Channel fit
Evaluate message length, rich interactions, voice turn-taking and accessibility by channel.
Human escalation
Check trigger accuracy, transfer timing, conversation summary and ownership after handoff.
REAL-WORLD USE CASES
Conversations designed around real tasks
Service automation
Answer grounded questions and complete structured service requests.
Guided transactions
Collect and confirm information before an approved business action.
Knowledge assistance
Help customers or employees navigate governed information with context.
Proactive engagement
Continue event-triggered messaging as a useful two-way conversation.
MEASUREMENT & EVALUATION
A balanced conversation scorecard
Task success
Verified completion of the intended job.
Groundedness
Support from approved evidence.
Repair rate
Recovery after misunderstanding or missing data.
Turns to outcome
Conversation effort required to finish.
Escalation quality
Accuracy, timing and transferred context.
Response latency
Time from input to a usable response.
PRODUCTION ONBOARDING
Create a repeatable conversation quality program.
Use a defined corpus, evaluation criteria and release gates instead of subjective demo reviews.
Map journeys
Select intents, languages, user states, channels and expected outcomes.
Curate knowledge
Assign owners, sources, freshness rules and behavior for missing information.
Write dialogue policy
Define clarification, confirmation, refusal, tool use and handoff decisions.
Build evaluation sets
Include typical, long-tail, ambiguous, adversarial and multilingual conversations.
Pilot with review
Inspect transcripts and outcomes with operations, product, risk and service teams.
Release and monitor
Version the system, watch quality regressions and maintain rollback and escalation paths.
CONVERSATIONAL AI FAQs
Conversational AI is the broader capability stack for understanding, context, grounding, dialogue policy, channel delivery, actions and evaluation. A chatbot is one possible interface.
CONVERSATIONAL AI