Eval directory

Evals for Crescendo

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

About Crescendo

Crescendo is an AI-native customer experience platform that consolidates agents, routing, ticketing, QA, knowledge, and workforce management into a single system with one data model. It pairs specialized CX agents that ground, resolve, and self-heal conversations with a deployed team of CX experts who calibrate the platform and own outcomes. The company positions itself around fast time-to-value and guaranteed results, and markets add-ons such as Crescendo Connect for integrations and AI Insights for analytics.

Industry

AI-native customer experience (CX) platform

Website

www.crescendo.ai

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Coverage map

What would you measure for Crescendo?

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

Grounded Conversational Resolution

The Resolve and Ground agents handling a customer thread end to end: answering from grounded policy and SOP sources, recognizing adjacent intents in the same thread, declining to answer when knowledge is missing, and handing off cleanly to a human CX expert.

Mapped capabilities

4 capabilities

Policy and SOP grounding
Answers trace to ingested policies, SOPs, and system connections rather than unsupported generation.
Single-thread multi-intent handling
Order status, revenue, and retention moments resolved in one thread without handoff between agents.
Abstention on missing knowledge
Behavior when the grounded corpus does not cover the customer's question.
Escalation to human experts
When and how a conversation is routed to the deployed CX expert team with context preserved.

Illustrative example

Input: Customer chat: "I ordered the walnut side table on March 3 and it still hasn't shipped. Can you cancel it and refund me today?" Order status and refund policy are both connected.
Expected behavior: The assistant looks up the order in the same thread, states the shipping status, and applies the grounded refund policy. If the order is outside the cancellation window, it says so, cites the policy terms, and hands off to a human expert instead of promising a refund.

Assistant Deployment and Configuration

The Detect and Deploy path from business requirements to a validated, UAT-ready assistant, including the Optimization Agent, the Configuration API and MCP service, and knowledge ingestion ahead of the kickoff call.

“Crescendo's Optimization Agent turns business requirements into a UAT-ready AI assistant in under an hour” www.crescendo.ai

Mapped capabilities

4 capabilities

Requirements to UAT-ready assistant
Turning stated business requirements into a validated assistant configuration.
Configuration API and MCP service
Programmatic configuration of the AI assistant and its exposed MCP surface.
Knowledge and system-connection ingestion
Onboarding policies, SOPs, and connected systems into the grounded corpus.
Gap detection before launch
Applied agents surfacing coverage gaps prior to go-live.

Self-Improvement and QA Loop

The Self-Heal surface: reviewing every conversation, flagging misses, applying fixes, and redeploying, together with the QA function Crescendo folds into the platform.

“Crescendo is one AI-native platform that replaces your entire CX stack” www.crescendo.ai

Mapped capabilities

4 capabilities

Conversation review coverage
Every conversation reviewed rather than a sampled subset.
Miss detection and flagging
Identifying incorrect, ungrounded, or unresolved responses.
Fix and redeploy cycle
Turning a flagged miss into a configuration or knowledge update that ships.
No-regression on prior behavior
Previously correct conversations still resolve after a self-heal update.

Integrations and System Actions (Crescendo Connect)

Building, testing, and publishing CX integrations so the assistant can act on systems of record, plus behavior when a downstream system is slow, erroring, or returns unexpected data.

Mapped capabilities

4 capabilities

Build, test, publish lifecycle
Moving an integration from draft through test to published state.
Action execution against systems of record
Taking a real action (lookup, update) on a connected backend during a conversation.
Downstream failure and recovery
Behavior on timeout, error, or unavailable dependency mid-conversation.
Unpublished vs live separation
Test-mode integrations do not affect live customer conversations.

Illustrative example

Input: A published subscription-status integration is called during a live chat, and the billing API returns HTTP 503 on every attempt while the customer asks whether their plan renewed this month.
Expected behavior: The assistant retries per its configured policy, does not state or guess a renewal status, tells the customer the billing system is temporarily unavailable, and routes the conversation to a human expert with the transcript and the attempted lookup attached.

Unified CX Operations Data Model

The single-platform claim: routing, ticketing, knowledge, and workforce management sharing one data layer instead of separate tools stitched by integrations, under stated SOC-2 Type II and HIPAA commitments.

“Security and privacy are paramount, upheld by stringent standards including SOC-2 Type II and HIPAA.” www.crescendo.ai

Mapped capabilities

4 capabilities

Routing and ticketing continuity
A conversation, its ticket, and its routing state stay consistent across channels.
Cross-surface data consistency
The same customer and conversation facts appear identically across platform surfaces.
Workforce management alignment
Staffing and queue state reflect live conversation volume and escalations.
Sensitive data handling
Handling of regulated or personal data consistent with SOC-2 Type II and HIPAA claims.

AI Insights and Reporting

Turning conversational CX data into refreshable, narrative-driven analytics and board-ready dashboards with recommended next steps rather than raw data to interpret.

Mapped capabilities

4 capabilities

Narrative analytics generation
Conversational data summarized into a readable narrative, not just charts.
Refresh and recomputation
Reports update as new conversation data arrives.
Recommended next steps
Actionable recommendations attached to reported findings.
Metric traceability
Reported outcome figures trace back to the underlying conversations.

Coverage is mapped from Crescendo'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 Crescendo test?+

The coverage map is generated from Crescendo's own public product surface (AI-native customer experience (CX) platform): 6 scoring areas — Grounded Conversational Resolution, Assistant Deployment and Configuration, and Self-Improvement and QA Loop, 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 Crescendo evals scored?+

Every case generated for Crescendo — across Grounded Conversational Resolution and Assistant Deployment and Configuration 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 Crescendo library include?+

The full Crescendo library is built on request. The coverage map spans 6 areas and 24 capabilities (for example, Policy and SOP grounding and Single-thread multi-intent handling under Grounded Conversational Resolution); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

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

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