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Evals for Cresta

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

About Cresta

Cresta is an AI platform for customer-facing conversations in contact centers, organized around automating, augmenting, and analyzing every interaction. It pairs real-time agent assistance and voice/chat AI agents with an Insights suite covering AI Analyst, Topic Discovery, and Real-Time Trends. The pages cite enterprise deployments across travel, telecom, and financial services, including United Airlines, Optimum, CVS, and IQ Credit Union.

Industry

contact center AI platform (conversational AI agents and CX analytics)

Website

cresta.com

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Coverage map

What would you measure for Cresta?

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

Real-Time Agent Assistance

Live guidance delivered to human agents while a conversation is in progress, plus the post-call work it streamlines. The pages describe analyzing conversations as they happen, offering immediate suggestions, and surfacing coaching opportunities.

“AI agents for every customer conversation” cresta.com

Mapped capabilities

4 capabilities

  • In-conversation suggestion relevance

    Whether prompts fit the live customer intent and current turn rather than generic scripting.

  • Grounding in company policy and knowledge

    Suggestions trace to retrievable source material instead of asserted fact.

  • Post-call summarization and wrap-up

    After-call work generated from the conversation, per the streamlined post-call claims.

  • Coaching signal surfacing

    Identifying what high performers do differently and turning it into agent-level guidance.

Illustrative example

Input
Live chat: a credit union member asks to have an overdraft fee reversed after a late direct deposit. The agent requests real-time guidance mid-conversation.
Expected behavior
The suggestion points to the applicable fee-reversal policy and the next step the agent can actually take, without committing to a reversal that requires approval the agent does not have.

02

Voice and Chat AI Agents

Autonomous AI agents that handle customer conversations directly across voice and chat, positioned as 'AI agents for every customer conversation' on the homepage.

“One AI platform. Every conversation.” cresta.com

Mapped capabilities

4 capabilities

  • Task completion on customer intents

    Carrying a customer request to resolution within the agent's defined scope.

  • Escalation and handoff to humans

    Recognizing limits and transferring with context preserved.

  • Voice streaming integration behavior

    Conduct over streaming media integrations such as the Avaya Infinity WebSocket path.

  • Multilingual conversation handling

    Non-English interaction, supported by the Spanish-language product site.

03

Insights: AI Analyst

The agentic analysis layer of Cresta Insights, positioned to answer business questions over conversation data without the analyst already knowing what to look for.

“Introducing the next generation of Cresta Insights - more authoritative, more real-time, more agentic.” cresta.com

Mapped capabilities

4 capabilities

  • Question decomposition over conversation data

    Stringing together multi-stage analysis rather than a single lookup.

  • Evidence citation back to conversations

    Answers point to the underlying interactions that support them.

  • Scoping and cross-checking of results

    Respecting the queried window and reconciling intermediate calculations.

  • Abstention on unsupported questions

    Declining when the conversation set cannot answer the question asked.

Illustrative example

Input
Ask AI Analyst why billing contacts rose last week, over a 30-day conversation set in which a new autopay error message first appears on day 24.
Expected behavior
The answer names the autopay error as the driver, keeps the finding inside the queried week, and points back to the specific conversations behind it instead of asserting an unsourced cause.

05

Quality Management and Agent Evaluation

Automated scoring of conversations and evaluation of the AI agents themselves, drawing on the QM automation cited in customer stories and the engineering post on LLM judges, deterministic checks, simulations, and human calibration.

“Cresta's generative AI solutions analyze conversations as they happen, providing agents with immediate suggestions, streamlining post-call work” cresta.com

Mapped capabilities

4 capabilities

  • Automated conversation scoring consistency

    Repeatable QM scores against a defined rubric.

  • Calibration against human reviewers

    Agreement between automated judgment and human scoring.

  • Pre-launch failure detection

    Catching agent failures in simulation before production exposure.

  • Deterministic check coverage

    Rule-based assertions layered alongside LLM judgment.

06

Enterprise Deployment and Industry Fit

Behavior across the regulated and high-volume verticals the pages name — travel, telecom, and financial services — including named deployments at United Airlines, Optimum, CVS, and IQ Credit Union.

Mapped capabilities

4 capabilities

  • Financial services conversation handling

    Member and borrower interactions in the credit union and lending contexts cited.

  • Transcription fidelity as an input

    Downstream behavior when transcription quality varies, a stated pre-Cresta pain point.

  • Sales and revenue conversation support

    Conversion-oriented interactions, per the Optimum and Cox stories.

  • Agent onboarding and ramp

    Support for newly onboarded agents with limited product familiarity.

Coverage is mapped from Cresta'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 Cresta test?+

The coverage map is generated from Cresta's own public product surface (contact center AI platform (conversational AI agents and CX analytics)): 6 scoring areas — Real-Time Agent Assistance, Voice and Chat AI Agents, and Insights: AI Analyst, 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 Cresta evals scored?+

Every case generated for Cresta — across Real-Time Agent Assistance and Voice and Chat AI Agents 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 Cresta library include?+

The full Cresta library is built on request. The coverage map spans 6 areas and 24 capabilities (for example, In-conversation suggestion relevance and Grounding in company policy and knowledge under Real-Time Agent Assistance); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

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

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