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Mankinds
Continuous AI testing, with audit ready proof
We put your AI under real-world attack conditions: 80+ test criteria, 50+ attack techniques. Mapped to 70+ regulations across 5 regions covering EU AI Act, DORA, NIS2, SOC 2, ISO 42001 and a lot more. Continuous, from development all the way to live production.
Before this, I spent 6 years as a CTO. And in those 5 years, I kept running into the same wall: we were shipping AI systems, but had no real way to validate them. Not against actual attacks. Not against the regulations that governed our sector. At some point, I realised I couldn't honestly tell my board whether our AI was safe or compliant. No tool existed to give me that answer.
That gap is what Baptiste and I built Mankinds to close.
Frame the risk: Automatic classification of every AI against the regulations that apply. 70+ frameworks, 5+ jurisdictions, sourced to the exact article.
Attack and score: Deterministic red-teaming across 80+ criteria and 50+ attack techniques. Every finding ships with a remediation path. Audit-grade in minutes.
Monitor, in production: Drift, hallucinations and policy violations flagged in real time, tied to the rule they break. Continuously.
We also open-sourced our evaluation library, mankinds-eval, for builders who want the primitives without the full platform. Free, composable, runs locally.
If you're shipping AI in a regulated environment, what does your current evaluation process look like? We'll be here all day.
About Mankinds on Product Hunt
“Continuous AI testing, with audit ready proof”
Mankinds was submitted on Product Hunt and earned 6 upvotes and 3 comments, placing #121 on the daily leaderboard. We put your AI under real-world attack conditions: 80+ test criteria, 50+ attack techniques. Mapped to 70+ regulations across 5 regions covering EU AI Act, DORA, NIS2, SOC 2, ISO 42001 and a lot more. Continuous, from development all the way to live production.
On the analytics side, Mankinds competes within SaaS, Developer Tools and Artificial Intelligence — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how Mankinds performed against the three products that launched closest to it on the same day.
Who hunted Mankinds?
Mankinds was hunted by Laurent Zhang. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Mankinds including community comment highlights and product details, visit the product overview.
Laurent Zhang
Hey PH! I'm Laurent, co-founder of Mankinds.
Before this, I spent 6 years as a CTO. And in those 5 years, I kept running into the same wall: we were shipping AI systems, but had no real way to validate them. Not against actual attacks. Not against the regulations that governed our sector. At some point, I realised I couldn't honestly tell my board whether our AI was safe or compliant. No tool existed to give me that answer.
That gap is what Baptiste and I built Mankinds to close.
Frame the risk: Automatic classification of every AI against the regulations that apply. 70+ frameworks, 5+ jurisdictions, sourced to the exact article.
Attack and score: Deterministic red-teaming across 80+ criteria and 50+ attack techniques. Every finding ships with a remediation path. Audit-grade in minutes.
Monitor, in production: Drift, hallucinations and policy violations flagged in real time, tied to the rule they break. Continuously.
We also open-sourced our evaluation library, mankinds-eval, for builders who want the primitives without the full platform. Free, composable, runs locally.
pip install mankinds-eval → https://github.com/mankinds/mankinds-eval/
If you're shipping AI in a regulated environment, what does your current evaluation process look like? We'll be here all day.