Conversational Voice Ordering Assistant
A voice ordering assistant where the AI proposes one structured action at a time and deterministic code validates, prices and executes it, so guests never hear a hallucinated price or an item that doesn't exist on the menu.
Platform Architecture Overview
The assistant listens, identifies intent from a bounded set of actions, validates that intent against a schema, and only then lets a deterministic order engine touch the cart. Prices and allergen facts always come from data, never from the model.
Speech Recognition Layer
Intent Model
Validation Layer
Order Engine
Guardrails & CheckoutConversation Layer
Validation Layer
Order Engine
Delivery Layer
End-to-End Platform Architecture
From a spoken request to a confirmed order on the POS
Guest Speaks
Speech to text runs through the browser by default, or through Deepgram or Whisper depending on the deployment.
Intent Model
The model proposes exactly one intent from a fixed set of around twelve, using tool calling against a JSON schema.
Validate
Every argument is checked at the service boundary. Anything unparseable or invalid becomes a clarifying question instead of a guess.
Order Engine
A pure reducer applies the validated intent to the cart, handling price math, modifier deltas and allergen roll up in code.
Guardrails
Checkout runs as a state machine, from browsing through review to an explicit confirmation, before anything is placed.
Respond
Text to speech and the avatar reply to the guest while the order is sent through to the POS.
Core Technical Capabilities
Key capabilities powering accurate voice ordering
Bounded Action Space
The model can emit only one intent from a fixed set of around twelve, covering things like adding an item, checkout, or asking a menu question. Nothing open ended.
Constrained Generation
Intents are produced through function or tool calling against a JSON schema. Any parse or validation failure is coerced into a clarifying question rather than a guess.
Deterministic Order Engine
A pure reducer handles all price math, modifier deltas and allergen roll up in code, so the same order always produces the same, auditable result.
Grounded Answers
Menu data (prices, dietary information, allergens, modifier compatibility) and the current cart are injected as context, so the model can only phrase answers from retrieved facts.
Checkout State Machine
Moves through idle, browsing, in cart, review, awaiting confirmation and done, reading the full order and total back before requiring an explicit yes.
Net Guarantees
No hallucinated prices, no invented items, no order placed without confirmation, and allergen answers sourced only from menu data.
Extended Capabilities
Advanced voice ordering features
Multi-Provider Speech to Text
Brand Voice Text to Speech
2D or Photoreal Avatar Options
Vector Grounded Menu Retrieval
Telephony and Drive-Thru Ready
Multi-Tenant Persona Configuration
Who Integrates With the Platform
Four primary integration personas
Quick Service Restaurants
Deploy the assistant across kiosk, drive-thru, web or phone channels, all from the same underlying engine.
Multi-Brand Operators
One deployment can serve many concepts, since menu, persona and guardrail settings are configured per brand and per location.
POS and Payments Partners
Orders flow through the order management layer and checkout is handled through the payment gateway, keeping the assistant itself out of PCI scope.
Operations and QA Teams
Every turn logs intent, validation, guardrail decisions and the spoken response, giving a clear trail for monitoring and dispute review.
Platform Facts
Key characteristics of the ina Ava platform
~12 Bounded Intents
The entire action space the model can choose from. Nothing open ended, nothing improvised.
1 to 2 Second Turns
Typical perceived latency from a spoken request to an updated on-screen order.
Zero Hallucinated Prices
Every price and allergen fact is sourced from menu data, never generated by the model.
Stateless and Multi-Tenant
Session and cart state stay small and serializable, so the system scales horizontally behind a load balancer.
Staged Rollout
Goes live in stages, from shadow mode to a single kiosk or lane to a full channel, with observability on throughout.
FAQ
How does a voice ordering assistant prevent hallucinated prices or invented menu items from reaching a guest's order?
Does the assistant ever touch card data, and where does that create PCI exposure?
How does one deployment support multiple brands or locations without separate builds for each?
What happens when speech recognition misses a word or the model proposes an invalid action?
How long does it typically take to onboard a new brand or location onto the platform?
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