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Axari

Your AI Twin for Security
3 ratings4.34
Saves171
CategoryAI
PlatformWeb
PricingFreemium

Axari is an AI workforce designed to assist cybersecurity teams by working continuously across tools and teams to advance security outcomes, automate workflows, and manage security tasks proactively within Slack or Microsoft Teams.

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4.34

3 reviews

Product
4.34
Design & Polish
4.84
Performance
3.84
Mobile PageSpeed
Performance
58 out of 100, Needs improvement
Accessibility
95 out of 100, Good
Best Practices
100 out of 100, Good
SEO
92 out of 100, Good

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Information

CategoryAI
PlatformsWeb
PricingFreemium
AccountNot confirmed
ListedSeptember 2026
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Website checks

Mobile PageSpeed
Performance
58 out of 100, Needs improvement
Accessibility
95 out of 100, Good
Best Practices
100 out of 100, Good
SEO
92 out of 100, Good

Website checks are queued. Results will appear here when complete.

Around the Web

Product Hunt171 source points · 17 comments

“Indefatigable” is the word @prasen_shelar and I kept coming back to. Axari gives each security professional an AI twin in Slack or Teams—one that can take recurring responsibilities, coordinate across tools and teams, and stay with the work until it’s actually done. ...which is critical in security contexts where vibes will never be enough. Attackers aren’t going to use less AI. Neither should you. And that's why Axari Digital Twins are the unfair advantage your team needs to stay safe.

chrismessina · Imported from Product Hunt

How does Anthropologic make sure cultural differences and local context are understood correctly across different markets?

daniel_henry4 · Imported from Product Hunt

How active is it? That is; how much can you empower the agent to take action on your behalf, and how have you ensured this works well with the security of the company it's installed in?

haf · Imported from Product Hunt

"recurring responsibilities" for a security agent is the part I'd want to understand better before handing it over - if the AI twin is triaging alerts on an ongoing basis, what's the actual mechanism for catching a case where it consistently misjudges severity in one direction (say, quietly downgrading a class of alerts it shouldn't)? is there a sampling/audit loop that re-checks its closed tickets against what a human would have called it, or does drift like that only surface after an incident?

galdayan · Imported from Product Hunt