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Evals for ConverseNow

Eval coverage for ConverseNow, mapped from its public product surface.

About ConverseNow

ConverseNow AI is a customizable voice AI platform that takes restaurant orders across phone, drive-thru, SMS, and app/kiosk integrations. It offers configurable tone, upsell logic, localization, and multilingual support under an "AI Your Way" branding model. Founded in 2018 by Vinay Shukla and Rahul Aggarwal, it targets QSR, fast casual, and dining brands seeking labor relief and 24/7 order taking.

Industry

restaurant voice ordering AI

Website

conversenow.ai

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Coverage map

What would you measure for ConverseNow?

6 scoring areas · 22 capabilities mapped · grounded in 8 cited pages

Every eval set is graded on

  • Adversarial robustness
  • Workflow quality
  • Safety gates
  • Operator quality

Pass/Fail + LLM judge 1–5 · critical severity flags · negative controls

01

Order Capture Accuracy

Whether a spoken order becomes the right cart: items, sizes, modifiers, and combos, including mid-conversation corrections and the final read-back the guest is asked to confirm. The site's own transcript and its 95%+ order accuracy claim make this the load-bearing surface.

“95%+ order accuracy” conversenow.ai

Mapped capabilities

4 capabilities

  • Item, size, and modifier capture

    Combos, sizes, add-ons, and substitutions resolved to the correct menu entries.

  • Mid-order correction and repair

    Guest changes, cancels, or restates an item; the cart reflects the final intent, not both versions.

  • Order confirmation and totals

    Read-back restates the assembled order and total consistent with what was added.

  • Ambiguous or off-menu requests

    Underspecified or unavailable items are clarified rather than silently guessed.

Illustrative example

Input
Caller: "Large cheeseburger combo with fries, and a chocolate shake. Actually, make that shake vanilla instead."
Expected behavior
The assistant produces a cart with one large cheeseburger combo and exactly one vanilla shake, no chocolate shake left behind, then reads the order back with a total before submitting it.

02

Channel Coverage and Behavior

The platform ships four distinct order paths — phone, drive-thru, SMS, and Order Injection API for mobile app and kiosk. Each has different latency, interruption, and confirmation expectations, so channel-appropriate behavior is a separate question from raw order accuracy.

“Handle phone orders 24/7 with our advanced voice AI order taker.” conversenow.ai

Mapped capabilities

4 capabilities

  • Phone order handling

    Conversational ordering with 24/7 availability.

  • Drive-thru speaker flow

    Throughput- and wait-time-sensitive turn taking at the lane.

  • SMS conversational ordering

    Text ordering, order status updates, and promotional messaging.

  • App and kiosk order injection

    Orders originating from mobile app or kiosk via the Order Injection API.

03

Brand Configuration Fidelity ("AI Your Way")

ConverseNow sells configurability as the product: tone and persona, upsell logic, coupons and discounts, and localization are per-brand knobs. The evaluable question is whether configured settings are honored consistently across a call and not overridden by the model's defaults.

Mapped capabilities

4 capabilities

  • Tone and persona adherence

    Configured brand voice holds across the conversation, including edge turns.

  • Upsell logic bounds

    Upsells fire where configured and stop when declined — the site's own bar is soft, not pushy.

  • Coupon and discount application

    Promotions surfaced and applied per configuration when a guest asks.

  • Per-brand menu and workflow customization

    Brand-specific menus, naming, and workflows respected over generic defaults.

04

Multilingual and Localization

Multilingual voice support and localization are marketed capabilities, with a Spanish ordering sample published on the products page. Language handling is worth isolating because it stresses recognition, menu-name mapping, and state retention at once.

“Serve customers in their preferred language using advanced multilingual voice recognition.” conversenow.ai

Mapped capabilities

3 capabilities

  • Language recognition and response

    Guest's language is recognized and served in kind, per the published Español flow.

  • Mid-conversation language switch

    Switching languages preserves the order already captured.

  • Localized menu and item naming

    Regional item names and localized phrasing map to the correct menu entries.

Illustrative example

Input
Caller orders a large pepperoni pizza in English, then says: "Perdón, ¿puedo seguir en español? Quiero agregar unas alitas."
Expected behavior
The assistant continues the conversation in Spanish from that turn onward, keeps the pepperoni pizza already captured, and adds the wings rather than restarting the order.

05

Order Handoff and Operations Data

An order is only useful once it reaches the store. This area covers integration with POS and partner systems, real-time ordering and status, and the analytics surfaced back to operators as AI Insights.

“overall time to take an order has reduced 25%” conversenow.ai

Mapped capabilities

3 capabilities

  • POS and integration partner handoff

    Captured order transmits to the downstream ordering system intact.

  • Real-time order status

    Order state and status updates reflected back to the guest.

  • Operator analytics and accuracy tracking

    Real-time analytics and order-accuracy reporting exposed to operators.

Coverage is mapped from ConverseNow's public pages (8 crawled). Examples are illustrative, not real test cases. The runnable eval library — graded inputs, expected behavior, and pass/fail checks — is built when you request it above.

Frequently asked questions

What do the Corsac evals for ConverseNow test?+

The coverage map is generated from ConverseNow's own public product surface (restaurant voice ordering AI): 6 scoring areas — Order Capture Accuracy, Channel Coverage and Behavior, and Brand Configuration Fidelity ("AI Your Way"), and more — spanning 22 mapped capabilities, each graded on adversarial robustness, workflow quality, safety gates, and operator quality once the library is built.

How are the ConverseNow evals scored?+

Every case generated for ConverseNow — across Order Capture Accuracy and Channel Coverage and Behavior and the other mapped areas — is graded with pass/fail checks plus an LLM judge scoring 1–5 against its expected behavior, with critical-severity flags and negative controls. Only judge-passed evals are published.

How many test cases does the ConverseNow library include?+

The full ConverseNow library is built on request. The coverage map spans 6 areas and 22 capabilities (for example, Item, size, and modifier capture and Mid-order correction and repair under Order Capture Accuracy); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

How do I run these evals against ConverseNow or my own agent?+

Request the library with your work email above. We'll build out all 6 mapped ConverseNow areas and set them up in a Corsac workspace, where you can run every test case against ConverseNow or your own agent with your own data.