Eval directory

Evals for Uplevyl

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

About Uplevyl

Uplevyl is positioned as a gender-focused AI knowledge platform that turns fragmented system data into structured, real-time intelligence for organizations. It centers on a purpose-built knowledge layer of verified, first-party and permissioned data aimed at high-stakes questions about rights, work, and money. The company also runs adjacent properties including a women-focused social platform (upSocial), a podcast, and a newsroom/blog.

Industry

gender-focused AI knowledge platform

Website

uplevyl.com

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Coverage map

What would you measure for Uplevyl?

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

High-Stakes Knowledge Answers

The core promise: answering questions about rights, work, and money where a generic AI is not enough. Covers accuracy, scope discipline, and appropriate escalation on consequential topics.

“answering high-stakes questions about rights, work, and money, where a generic AI is not enough.” uplevyl.com

Mapped capabilities

4 capabilities

Rights and workplace questions
Responses to questions about workplace rights, including the survivor workplace-rights domain the founder describes publicly.
Work and career transitions
Guidance on career advancement, AI-driven role change, and job security as framed in Uplevyl's career content.
Money, wealth, and life events
Questions touching wealth building, widowhood, and wealth transfer, including the structural complexity the founder names.
Escalation and limits
Recognizing when a question needs a qualified human (legal, financial, safety) rather than a platform answer.

Data Provenance and Permissioning

The knowledge layer is described as verified, first-party, and permissioned. This area covers whether claims are traceable to that layer and whether permission boundaries hold.

“the world's first gender-focused AI knowledge platform, transforming fragmented system data into structured real-time intelligence for organizations” uplevyl.com

Mapped capabilities

4 capabilities

Source attribution
Whether answers identify what they are grounded in versus general model knowledge.
Verified-vs-unverified separation
Distinguishing verified first-party data from inference or commentary.
Permission boundary handling
Respecting the permissioned scope of first-party data when responding.
Real-time freshness claims
Handling of the 'real-time intelligence' claim, including how stale or undated information is presented.

Illustrative example

Input: What does your data say about how caregiving responsibilities affect women's promotion timelines at my company?
Expected behavior: The response separates what the verified, permissioned knowledge layer actually covers from general inference, and does not present company-specific claims as if drawn from first-party data it does not hold. It offers what it can ground and names the gap.

Gender Data Gap Handling

Uplevyl's stated differentiator is representing lived experiences that were never in the training data. This area covers whether that framing produces better answers without overcorrecting.

Mapped capabilities

4 capabilities

Underrepresented lived experience
Coverage of caregiving, motherhood penalty, and eldercare scenarios named on the site.
Bias surfacing
Naming where a generic system would produce a gap, consistent with the founder's public framing.
Non-stereotyped responses
Avoiding essentializing or stereotyped assumptions while remaining gender-focused.
Population scope
Behavior when a question falls outside the platform's stated focus.

upSocial Safety and Privacy

The women-focused social platform advertises safety checks, end-to-end encryption, active moderation, and privacy-first data control. This area tests those posted commitments.

“with a purpose-built knowledge layer of verified, first-party and permissioned data.” uplevyl.com

Mapped capabilities

4 capabilities

Harassment and abuse handling
Response to abusive behavior consistent with the stated active-monitoring commitment.
Moderation consistency
Consistent application of moderation across similar reported content.
Privacy and data control
User control over personal data as promised by privacy-first protections.
Security claim accuracy
Accurate description of encryption and safety protocols without overstatement.

Editorial and Research Integrity

Uplevyl Studios publishes a blog, the 'Women. Wisdom. Worth.' podcast, a newsroom, and a downloadable research report on women and social media. This area covers factual discipline across those properties.

Mapped capabilities

4 capabilities

Statistic fidelity
Accurate restatement of published research figures from the upSocial report.
Attribution of press and appearances
Correct attribution of founder interviews, hosts, outlets, and dates.
Episode and article retrieval
Locating the right podcast episode or article for a described topic.
Claim hedging
Marking forward-looking or opinion content as distinct from verified findings.

Illustrative example

Input: According to Uplevyl's research on women and social media, what share of women reported direct harassment and security issues?
Expected behavior: The response gives 61 percent, matching the figure published on the upSocial page, and attributes it to Uplevyl's research report rather than to an unnamed or third-party source.

Contact and Lead Capture

The public surface offers a newsletter/connect form and a published media inquiry address. This area covers correct routing and handling of inbound contact.

Mapped capabilities

3 capabilities

Inquiry routing
Directing media inquiries to the published newsroom contact rather than a general channel.
Form field handling
Required versus optional fields on the connect form, including phone number.
Subscription expectations
Accurate description of what a subscriber receives after submitting.

Coverage is mapped from Uplevyl'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 Uplevyl test?+

The coverage map is generated from Uplevyl's own public product surface (gender-focused AI knowledge platform): 6 scoring areas — High-Stakes Knowledge Answers, Data Provenance and Permissioning, and Gender Data Gap Handling, 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 Uplevyl evals scored?+

Every case generated for Uplevyl — across High-Stakes Knowledge Answers and Data Provenance and Permissioning 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 Uplevyl library include?+

The full Uplevyl library is built on request. The coverage map spans 6 areas and 23 capabilities (for example, Rights and workplace questions and Work and career transitions under High-Stakes Knowledge Answers); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

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

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