Eval directory

Evals for Second Nature

Eval coverage for Second Nature, mapped from its public product surface.

About Second Nature

Second Nature is an enterprise AI role-play platform where sales and customer-facing teams practice conversations with AI personas that act like real buyers. It generates role plays from uploaded content or freeform descriptions, then scores each session and returns automated feedback on knowledge, communication, and soft skills. It is sold to enterprises with admin controls, analytics dashboards, and multilingual support.

Industry

AI sales role-play and coaching software

Website

secondnature.ai

Use the eval library for Second Nature

We'll build out the full library — runnable test cases with inputs, expected behavior, and pass/fail checks — in your Corsac workspace.

All evals →

More AI eval libraries

Coverage map

What would you measure for Second Nature?

6 scoring areas · 23 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

Role-Play Generation & Authoring

Turning freeform descriptions or uploaded materials (PDFs, decks, Word docs, audio) into a complete role play — personas, scenario framing, and evaluation topics — through the Course Editor without technical expertise.

“Describe your scenario in freeform text or upload any content, and the AI builds a full roleplay in minutes.” secondnature.ai

Mapped capabilities

4 capabilities

Freeform scenario to full role play
A short text description yields a runnable role play with persona, context, and evaluation topics.
Content upload as source material
Uploaded decks, PDFs, documents, and audio recordings ground the generated scenario and its evaluation topics.
Persona construction and customization
Personas match a named industry, buyer type, or customer segment rather than defaulting to a generic buyer.
Evaluation topic derivation
Generated scoring topics trace back to the supplied content instead of being invented.

Illustrative example

Input: Build a role play: a mid-market IT director who already uses a competitor, is skeptical about migration cost, and only has fifteen minutes for a discovery call.
Expected behavior: Generates a runnable role play whose persona reflects the stated role, incumbent competitor, and cost objection, and whose evaluation topics include discovery quality and objection handling. It does not invent an industry or budget figure absent from the description.

Conversation Simulation & Scenario Types

Running the practice conversation itself across the supported formats and scenario archetypes, with AI personas that behave like real buyers.

“It employs language models like GPT hosted on Microsoft Azure for contextually relevant dialogue” secondnature.ai

Mapped capabilities

4 capabilities

Scenario archetype fidelity
Cold call, discovery, objection handling, and product pitch each behave like their own conversation type.
Session formats
1:1 live conversation, group/multi-persona meetings, and chat-based practice.
Non-conversational formats
Webcam pitch recordings, slide-based presentations with AI-generated questions, and screen-shared product demos.
Persona adaptivity in dialogue
Two-way, adaptive responses that react to what the learner actually said.

Scoring & Automated Feedback

Post-session evaluation and coaching output covering knowledge accuracy, conversational style, and soft skills such as clarity and confidence, returned as actionable feedback.

“providing immediate feedback and scoring within 45-90 seconds” secondnature.ai

Mapped capabilities

4 capabilities

Knowledge accuracy assessment
Correct and incorrect product claims made by the learner are distinguished.
Communication and soft-skill scoring
Clarity, confidence, and conversational style are scored as distinct dimensions.
Actionable feedback quality
Feedback points to specific moments in the session rather than generic advice.
Scoring consistency
Comparable sessions receive comparable scores across repeated evaluations.

Illustrative example

Input: A session transcript where the rep is fluent and confident but states the product includes SOC 2 certification, which the uploaded source material never claims.
Expected behavior: The knowledge accuracy dimension flags the unsupported certification claim and the written feedback quotes or paraphrases that specific moment. Communication and confidence scores are not dragged down by the factual error.

Course Structure & Learning Path

Combining training content — videos, decks, quizzes — with AI practice in a single course, so learning and doing sit in one structure.

Mapped capabilities

3 capabilities

Mixed content and practice sequencing
Videos, decks, and quizzes compose with role plays into a coherent course.
Template library and customization
Starting from a prebuilt template and adapting it to a team's own scenario.
Progression through a course
Learner advancement across content and practice steps.

Analytics & Manager Insight

Manager- and admin-facing reporting on progress, scores, performance trends, and knowledge gaps, including connection to an external analytics stack.

Mapped capabilities

4 capabilities

Progress and score reporting
Per-rep and team-level completion and score views.
Knowledge gap identification
Surfacing which topics a team consistently gets wrong.
Performance trends over time
Change in scores across repeated practice sessions.
Export to analytics stack
Training data made available to downstream reporting systems.

Enterprise Administration, Language & Safeguards

The controls that make the platform deployable at global scale: admin enablement of features, 30+ language support, and the stated ethical safeguards, content filters, and security practices.

“maintaining strict ethical safeguards, content filters, and compliance with security best practices” secondnature.ai

Mapped capabilities

4 capabilities

Admin controls and feature enablement
Admins govern rollout of capabilities such as video avatars.
Multilingual role plays
Scenario delivery and scoring across the 30+ supported languages.
Content filters and ethical safeguards
Handling of unsafe or out-of-scope learner input during a session.
Regulated-sector scripted calls
Compliance-sensitive scripts where deviation from required language matters.

Coverage is mapped from Second Nature'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 Second Nature test?+

The coverage map is generated from Second Nature's own public product surface (AI sales role-play and coaching software): 6 scoring areas — Role-Play Generation & Authoring, Conversation Simulation & Scenario Types, and Scoring & Automated Feedback, and more — spanning 23 mapped capabilities, each graded on adversarial robustness, workflow quality, safety gates, and operator quality once the library is built.

How are the Second Nature evals scored?+

Every case generated for Second Nature — across Role-Play Generation & Authoring and Conversation Simulation & Scenario Types 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 Second Nature library include?+

The full Second Nature library is built on request. The coverage map spans 6 areas and 23 capabilities (for example, Freeform scenario to full role play and Content upload as source material under Role-Play Generation & Authoring); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

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

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