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Conversational AI

Conversational AI UX is designing the dialogue itself — what the assistant is for, how it sounds under stress, and where a human takes over. If the product's job is to talk to someone well, not to execute multi-step actions on their behalf, this is the craft; see Agentic Workflow UX when the system needs to act, not just converse.

Part of Creation. Growth job: ship assistants users finish with, not bounce from after three confused turns.

Conversation is a UI modality. Not a substitute for product thinking. Scope, intents, tone under stress, and human handoff matter as much as the model behind the glass.

Bad conversational UX wastes containment and trust

Users hit a wall of "I did not understand that." The bot repeats marketing fluff. Escalation is a dead link. Tone breaks when the user is frustrated. Support volume shifts from containment to cleanup.

A conversational surface without designed failure and handoff trains users to avoid the assistant. Then leadership wonders why adoption is flat.

Who it's for

  • Teams launching support, sales, or in-product assistants where conversation is the primary interface
  • Brands that care about tone as much as containment, regulated, B2B, or high-trust categories
  • Products that need human handoff without dead ends or infinite loops
  • Orgs extending Creation when chat is the main surface, not a sidebar

Problems we design for

  • Unclear scope. Users ask things the bot cannot do
  • No escalation path, frustration with no human exit
  • Brittle failure handling, errors feel like user fault
  • Tone that breaks under edge cases or refusal scenarios
  • No disclosure about data use or AI limits
  • Transcripts that cannot be evaluated or improved

What we tackle

Conversation architecture and intents

What jobs the assistant owns, what it must refuse, and how intents map to product capabilities, not an open-ended "ask me anything."

Happy and unhappy paths

Success flows plus confusion, ambiguity, out-of-scope, and abuse. Designed with recovery prompts, not infinite "try again."

Escalation and handoff

When and how humans take over. With context preserved so users do not repeat themselves.

Evaluation criteria

Transcript review rubrics and test scenarios, so quality improves beyond launch-week tweaks.

What you get

- Conversation architecture and intent map - Tone, disclosure, and escalation specifications - Failure and refusal state patterns - Example transcripts (good, bad, edge) - Prototype flows for review and testing - Evaluation criteria and test scenario set - Handoff package for eng / conversation platform implementers

Typical work as a focused sprint per assistant scope. See Rates.

How it works

1. Jobs & intents: what conversation is for and what is out of scope. *Deliverable: intent map + scope charter.* 2. Happy + unhappy paths: especially escalation and refusal. *Deliverable: flow specs + example transcripts.* 3. Tone system: on-brand under stress, disclosure included. *Deliverable: tone guide + pattern library.* 4. Test: scenarios, rubrics, prototype walkthrough. *Deliverable: eval kit + handoff.*

Pairs with Creation when chat is one surface among many. For agents that take actions, see Agentic Workflow UX.

Engagement shapes

  • Support assistant: containment with designed escalation
  • In-product copilot (chat-primary): task help inside SaaS workflows
  • Sales / qualification bot: bounded intents with CRM handoff notes
  • Pilot → expand: one channel or locale first, then scale intents

Proof

Conversational patterns apply to the same complex B2B surfaces as Sendoso and Unlayer. Products where users need guidance without losing control. Governata illustrates trust-heavy data products where assistant scope must be explicit.

Related services

Fit / not for

For assistants with a defined job and owners who will maintain intents. Not for open-ended chat on a marketing page with no product behind it. Not for model benchmarking or LLM training, we design the experience.

Frequently asked questions

What makes conversational UX fail?

Unclear scope, no escalation, brittle failure handling, and tone that breaks under edge cases. We design those deliberately, not after launch complaints.

Do you build the LLM backend?

We design the experience and evaluation. Implementation partners vary. The UX and handoff specs are the deliverable unless build is explicitly in scope.

How do you keep it on-brand?

Tone rules, example transcripts, and refusal patterns. Treated as product UI, not afterthought copy.

Do you support voice interfaces?

Yes when scoped. Same intent, tone, and failure discipline; voice adds constraints we document in handoff.

How do you measure success?

Define metrics with you, containment, escalation quality, task completion, CSAT: and ship eval scenarios to track them. We do not guarantee model accuracy.

Should we audit before building an assistant?

If the base product confuses users, a chat layer will amplify confusion. [UX Audit + AI Readiness](/services/creation/ux-audit-ai-readiness) de-risks the rollout.

How long does a conversational UX project take?

A bounded assistant (support or in-product) often fits a focused sprint. Multi-locale or large intent libraries run as a short project.

When is conversational AI the wrong primary surface?

When the job lives inside an existing workflow — a builder, inbox, or admin table — embedded assist with disclosure and undo usually beats a chat sidebar. Start from [Creation](/services/creation) AI feature patterns or [Agentic Workflow UX](/services/creation/agentic-workflow-ux) when the system takes multi-step actions.

Design the assistant

Describe the job, channels, and what happens when the bot cannot help. We will scope intents and escalation, not a generic chat skin.

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