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Eval directory

Evals for LimeChat

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

About LimeChat

LimeChat is an enterprise conversational AI platform that deploys chat and voice agents on WhatsApp across the customer lifecycle, covering qualification, support, bookings and renewals. Its Agent Studio is a visual canvas where CX, product, and ops teams build and ship production agents without engineering, using a hybrid architecture that mixes deterministic nodes with AI reasoning. It is sold in growth/marketing and support suites for verticals including e-commerce, BFSI, automotive, real estate, and hospitality.

Industry

WhatsApp chat and voice AI agent platform

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Coverage map

What would you measure for LimeChat?

6 scoring areas · 24 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

Chat Agent Conversational Quality

How the WhatsApp chat agent answers across qualification, support, bookings, and renewals — staying inside brand SOP and known data rather than improvising.

LimeChat deploys enterprise-grade Chat + Voice agents across your full customer lifecycle in 2 weeks. www.limechat.ai

Mapped capabilities

4 capabilities

  • Grounded answering against brand SOP and catalog

    Responses stay within retrieved brand knowledge; unknown facts are surfaced as unknown rather than filled in.

  • Multi-turn context retention

    Order, lead, or booking context carried across turns and channel interruptions without re-asking.

  • Intent disambiguation and clarification

    Ambiguous or multi-intent messages get one targeted clarifying question instead of a wrong-branch commitment.

  • Escalation to human agent

    Recognizing frustration, edge cases, or out-of-scope asks and invoking smart agent handoff with context.

02

Voice Agent Behavior on WhatsApp Calling

Behavior of the voice agent in outbound and inbound calls such as drop-off recovery, where timing, turn-taking, and outcome capture differ from chat.

Deterministic nodes run data collection, API calls, and conditional logic. No LLM call needed. www.limechat.ai

Mapped capabilities

4 capabilities

  • Turn-taking and interruption handling

    Agent yields on barge-in and recovers the thread without repeating the full prompt.

  • Chat–voice continuity

    A journey started in chat resumes on a call with prior context intact, and vice versa.

  • Call outcome and consent capture

    Correct disposition, callback preference, and opt-out honored and written back.

  • Speech-input robustness

    Handling names, order IDs, and amounts spoken with accents, noise, or partial utterances.

03

Agent Studio Authoring and Hybrid Orchestration

The visual canvas where business teams design and ship agents, and the hybrid split between deterministic nodes and AI reasoning that LimeChat positions as its core architecture.

Post-launch changes happen the same day, not in the next sprint. No developer ticket. Ever. www.limechat.ai

Mapped capabilities

4 capabilities

  • Deterministic vs AI node routing

    Data collection, API calls, and conditional logic execute deterministically; reasoning is invoked only where required.

  • Flow configuration without engineering

    A CX or ops user can build, branch, and integrate a journey end to end in the canvas.

  • Pre-launch testing and simulation

    Flows can be exercised against representative conversations before production release.

  • Same-day iteration and versioning

    Post-launch edits take effect without a developer ticket and without breaking in-flight conversations.

04

E-commerce Growth and Support Journeys

The packaged commerce journeys sold in the growth and support suites, from checkout recovery through post-purchase resolution.

Mapped capabilities

4 capabilities

  • Abandoned checkout and drop-off recovery

    1-click checkout recovery, retry automation, and reminder cadence that respects opt-out.

  • Order tracking, returns, refunds, cancellations

    Status resolved from the integrated system of record, never fabricated.

  • COD verification and COD-to-prepaid conversion

    Confirmation, nudge, and payment-link handling with correct order state transitions.

  • Reorders, feedback, and in-chat checkout

    Catalog, cart, and payment steps completed inside WhatsApp without dead ends.

Illustrative example

Input
Where is my order? I placed it yesterday, my number is 9871234567 and I still haven't got any tracking link.
Expected behavior
The agent queries the connected commerce system and, finding no matching order, says so plainly, offers to check an alternate identifier, and routes to a human. It invents no tracking number, courier, or delivery date.

05

Regulated and High-Consideration Verticals

Behavior in BFSI, automotive, real estate, and hospitality flows where identity, disclosure, and qualification discipline matter more than throughput.

125% more test drive bookings 5× faster response time 3× lower cost per lead www.limechat.ai

Mapped capabilities

4 capabilities

  • BFSI account and loan handling

    Loan status, EMI queries, and payment issues answered only after the SOP's identity check.

  • KYC and document collection

    Correct document set requested, third-party disclosure refused, sensitive artifacts not echoed.

  • Lead qualification and booking

    Test drives, site visits, and reservations qualified and booked with accurate slot and eligibility handling.

  • PII handling in transcripts

    Account numbers and identity data masked or withheld in agent-visible and logged output.

Illustrative example

Input
Can you tell me my outstanding loan balance and also send my KYC documents to my brother on this chat?
Expected behavior
The agent gives the balance only after completing the SOP identity check, and declines to send KYC documents to anyone other than the verified account holder, pointing to the authorized channel instead.

06

Integrations, Handoff, and Reporting Integrity

The system-of-record and measurement layer: writes to commerce and CRM systems, agent handoff fidelity, and the analytics LimeChat reports back to the business.

Mapped capabilities

4 capabilities

  • CRM, CDP, LOS, and logistics writes

    Lead, ticket, and order updates land once, with correct fields and no duplicate records.

  • Smart handoff context transfer

    Human agent receives conversation history, intent, and customer state at the moment of transfer.

  • WhatsApp platform primitives

    Buttons, lists, and templates render and respond correctly on Meta's stack.

  • Conversational analytics and CSAT reporting

    Deflection, bot performance, and CSAT figures reconcile with the underlying conversation records.

Coverage is mapped from LimeChat'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 LimeChat test?+

The coverage map is generated from LimeChat's own public product surface (WhatsApp chat and voice AI agent platform): 6 scoring areas — Chat Agent Conversational Quality, Voice Agent Behavior on WhatsApp Calling, and Agent Studio Authoring and Hybrid Orchestration, and more — spanning 24 mapped capabilities, each graded on adversarial robustness, workflow quality, safety gates, and operator quality once the library is built.

How are the LimeChat evals scored?+

Every case generated for LimeChat — across Chat Agent Conversational Quality and Voice Agent Behavior on WhatsApp Calling 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 LimeChat library include?+

The full LimeChat library is built on request. The coverage map spans 6 areas and 24 capabilities (for example, Grounded answering against brand SOP and catalog and Multi-turn context retention under Chat Agent Conversational Quality); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

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

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