Conversational Design Creator.

The problem

Companies now ship conversational AI everywhere, but the reasoning behind good conversational design — why a fallback cascades a certain way, when to escalate, what brand voice applies per channel — lives in expert reviewers' heads, scattered Slack threads, and Figma comments. Review is the only enforcement mechanism, so quality collapses when the reviewer is busy, absent, or nonexistent.

AI generators make this worse: they default to generic "professional B2B tone" because they have no idea what a company's tone actually is. The downstream cost is measurable — 60% of consumers report negative chatbot experiences, only 30% of companies enforce brand guidelines, and RAG-grounded chatbots don't help because they encode product knowledge, not conversational reasoning.

The solution

The Conversational Design Creator captures a team's tacit conversational design reasoning as a structured, machine-readable corpus, then uses it to generate and audit design artifacts. A producer enters a brief; the Creator generates conversational design drafts grounded in the corpus, with each element citing the specific rules applied.

The distinctive architecture is a two-mode corpus from a single source of truth: settled convention rules fire silently with citations, while judgment-call rules surface alternatives with reasoning for the producer to decide. This operates at the design layer — flows, decision points, policies — upstream of runtime chatbot platforms, and its output feeds into tools like Voiceflow and Sierra rather than competing with them.

How it works

The workflow moves from brief intake through a confirmation gate — no generation without producer sign-off — to corpus-grounded draft generation, per-unit review with visible citations, and export into a complete specification for engineering. Each design unit is tagged as new, modifies, replaces, or preserved, and carries inline citations from the convention rules applied.

At judgment-call rules, a path-discovery step presents corpus-grounded alternatives; the producer picks, and dependent convention rules cascade silently. Regeneration supports two behaviors — convention rule push-back when a producer overrides a settled rule, and judgment-call refinement — so the AI never silently rejects. The capstone corpus is hand-curated at 15–20 rules across 3–4 categories, calibrated to a real multilingual WhatsApp system at a global civic advocacy non-profit.

Who it's for

This is a B2B product with three layered roles. Primary end users are design-team-agnostic — conversation designers, UX writers, generalists, engineers building conversational features, PMs, contractors, and AI-tool operators — anyone producing conversational work who needs it grounded in company reasoning.

The buyer is a senior leader with conversational AI transformation budget: head of design, VP of product design, design ops lead, a VP or director of conversational AI/CX, CTO, or head of product, varying by company maturity. Secondary beneficiaries are the wider product organization and, ultimately, the customers interacting with the conversational products being built.

Why it matters

The conversational AI market is growing at roughly 21% CAGR toward $82B by 2034, and the design layer — approximated at 5–10% of that spend — is a $1–2B market in 2026 with no direct competitor. Delivery is high-ticket, consulting-led: $50K–$500K per engagement, because the AI-accelerated extraction methodology is the irreproducible IP and the corpus is the asset that justifies the price.

Defensibility is built around a 12–24 month window before adjacent brand-voice governance tools (Acrolinx, Averi, Markup AI) could pivot — and adding a second corpus mode is a structural rebuild, not a feature. The business is pre-launch, targeting 3–5 engagements in year one, scaling toward 10–20 as the methodology matures.

At a glance

Project

Conversational Design Creator

Built by

Nathalia Carvalho

One-liner

The Conversational Design Creator captures a team's tacit conversational design reasoning and turns it into a structured corpus that an AI can use to generate and audit outputs.