Anonymized commercial system · governed multi-channel conversational commerce
Persian Conversational Commerce
A governed sales assistant that separates what a model may interpret from what the catalog and deterministic policy are allowed to assert — it sells consultatively, refuses unsafe or fabricated answers, and serves customers across web widget, Telegram, and Instagram DM. The public surface below is a sanitized representative replay of the product experience, not a live production endpoint.
How can an AI sales assistant guide customers toward suitable products without inventing facts, ignoring safety, or improvising commercial policy?Owns · Governed multi-channel conversational commerce
System model
Interpret language. Ground facts. Enforce policy. Recommend within bounds.
Transition model
Why the conversation moves — every stage has a reason to hand over.
The system is not a single prompt. It is a staged flow where each transition is earned by the previous stage's outcome, so commerce actions only happen after safety and truth have been established.
Delivery surfaces
One governed brain, multiple delivery surfaces.
Web widget, Telegram, and Instagram DM are representative delivery surfaces of the system design. The conversations shown in this case study are representative reconstructions — no production customer logs, traffic, or conversion data is published.
Embedded assistant on the storefront with full consultative replay
Conversation continues in Telegram with the same governed policy
Direct-message surface bound to identical grounding and safety rules
Representative UX scenarios
Run five conversations that show the governance working.
Each scenario exercises a different governed behavior: consultative selling with persona, safety-before-sales under insistence, honest failure when WooCommerce is unreachable, recent-turn reference resolution plus the medical boundary, and resistance to injected authority claims. User questions are preserved; stronger health claims are softened in the public transcript so the case study remains about inference and commerce behavior rather than medical advice.
Representative UX conversations — sanitized demonstrations of system behavior, not production customer logs.
Displayed commerce values come from representative WooCommerce snapshots and are not current prices. In the shipped system, WooCommerce remains the live authority for price, availability, and purchase links.
Product behavior surface
What the system reliably does — and what it refuses to do.
System boundary
The model interprets. The system decides what is true and allowed.
The design keeps language flexibility without giving the model authority over catalog facts, safety constraints, or commercial policy.
Interpretation
Language reasoning extracts intent, constraints, and conversational context.
Grounding
Only approved catalog records are factual authority for product attributes.
Policy
Deterministic rules can filter, rank, prohibit, or require clarification.
Commerce
Approved product relationships shape bounded comparisons and offers.
Evaluation
Provider-free fixtures verify grounding, continuity, and unsupported-claim behavior.
Commercial intelligence
A recommendation is a governed relationship, not an improvised upsell.
The system can recognize product comparisons and adjacent commercial opportunities, but the recommendation path remains bounded by approved relationships and current context.
Adjacent, approved fit
A related product can appear only when the catalog relationship exists and the current eligibility decision permits it.
Bounded upgrade path
A higher-tier option is traceable to an approved relationship instead of being invented from model preference.
Known composition
Bundle advice is grounded in approved pack relationships and product membership rather than improvised combinations.
Alternative under constraint
Substitution can be offered when a product is unsuitable or unavailable, but only from an approved substitute relationship.
Engineering decisions
Interpretation remains flexible; authority remains bounded.
Separate language interpretation from product truth
The model may interpret intent and context, but approved catalog records remain factual authority.
Keep commercial relationships explicit
Cross-sell, upsell, bundles, and substitutes come from approved relationships rather than improvised model preference.
Gate selling eligibility deterministically
Approved selling edges are required before offers; caller-provided IDs are necessary but never sufficient.
Require valid commerce lookups for prices and links
A missing or failed WooCommerce source produces a stated failure and a labeled prior snapshot — never an estimated fact.
Benchmark surface
What the system is designed to measure.
Conversation quality is bounded by grounding, policy correctness, persona consistency, continuity, and the absence of invented product facts.
Persian intent fixtures, grounded-response suites, persona/style gates, golden behavior constraint cases, multi-turn replays, eligibility and injection boundary tests, and catalog mutation tests.Evaluation
Offline gates make the claim inspectable before live-model behavior enters the picture.
Explicit boundaries
What this public surface does not claim.
Disclosure
Public enough to inspect. Private enough to respect the commercial boundary.
Product behavior
Sanitized conversational flows, architecture boundaries, offline evaluation counts, catalog scale, and the distinction between model interpretation and deterministic authority.
Commercial internals
Client identity, proprietary catalog dumps, credentials, production traces and customer conversations, private business rules, current prices, and any unsupported traffic, conversion, or revenue metrics.