Lazarus AI

This is the core issue we keep seeing: Models can look impressive in demos, but production value depends on the system around it. Reliability, evaluation, orchestration, and traceability are what turn AI capability into enterprise outcomes. lazarusaie.com/blog/understan…

【AGESTニュース】 AGESTは、米Lazarus社のエージェンティックAIによるゼロデイ・脆弱性探索ツール「Clearwing」を活用した自律型AIサイバーセキュリティ基盤の共同開発および商用化に関する業務提携契約を締結しました。 agest.co.jp/news/2026-06-1…

Read in English: agest.co.jp/news/2026-06-1…

Excited to announce our strategic partnership with AGEST. Together, we’re working on the joint development and commercialization of AGEST Defensive, an autonomous AI cybersecurity platform built on Clearwing for governments and enterprises. Clearwing launched on May 5th with a simple goal -- to make advanced AI-driven vulnerability discovery more accessible, deployable, and practical. Our collective work on AGEST Defensive carries that work forward into commercial deployment. AGEST brings deep expertise in cybersecurity operations, testing, verification, QA, and commercialization. Lazarus AI brings the agentic AI infrastructure behind Clearwing and our experience building AI systems for enterprise, government, and regulated environments. The focus is straightforward: help security teams discover, verify, and remediate vulnerabilities before attackers can exploit them. Across models. On-premises when needed. Without forcing sensitive security work through a single closed model or external API. AI cybersecurity has to move beyond impressive research and into critical environments where cost, confidentiality, and control matter. Read the full release: lazarusaie.com/blog/announcin…

Today, we’re excited to announce our strategic partnership with OKI. OKI has spent decades earning trust across critical infrastructure, defense, and core operations. These are exactly the environments where AI adoption has moved more slowly, and for good reason. Many organizations have seen AI systems look impressive in demos, only to have them fall short in production. The accuracy was not reliable enough, the sourcing was not transparent enough, and the security model was not strong enough. That is the gap this partnership is designed to close. Together, OKI and Lazarus AI will help bring reliable AI into the operational settings where the stakes are highest: supporting decision-making at critical facilities, preserving and transferring expertise from skilled personnel, and deploying fully on-premises when confidentiality is non-negotiable. AI that works where it matters most is not just valuable for the organizations deploying it. It is valuable for the people and communities that depend on those organizations getting things right. Full details in the press release: lazarusaie.com/blog/oki-signs…

Operators don’t have 18 months to wait for a custom AI build. They also can’t bet mission intelligence on a single closed model provider. Field-level UAS intelligence needs to deploy in weeks, run on open infrastructure, adapt to the environment, and return traceable answers in real time. Kyle Goodman breaks down how Lazarus AI’s AIE and ATLS make that possible. lazarusaie.com/blog/a-new-app…

Benchmarks are great until your model turns into a spokesperson. Reasoning? Strong. Coding? Strong. History? “Let’s talk about something else.” ReAligned removes the ideological wrapper from Qwen 3.5 and replaces it with the documented international record. Check it out. huggingface.co/collections/La…

This is the right framing: not “which model is good or bad,” but whether a model’s hidden policy priors are measurable and correctable. ReAligned is interesting because it treats alignment drift as an engineering problem, not a culture-war slogan.

@TheEricHartford @QuixiAI

The best open weight models in the world right now are Chinese. Qwen, DeepSeek, Kimi, MiniMax, GLM. They are excellent. On reasoning, on coding, on math, on multilingual tasks, they trade blows with the best closed models out of the US. If you want to self-host a frontier capability model in 2026, you are almost certainly downloading something from China. The problem? These models lie. For enterprise and government teams evaluating open models, that's not a "quirk" to work around. It's a material deployment risk. Today, Lazarus AI is publicly launching ReAligned, because that risk has been blocking real deployments for two years, and it shouldn't have to. ReAligned is Qwen 3.5, fine-tuned at six sizes from 0.8B to 35B, with the ideological behavior layer removed and replaced with a documented international record. Base capability retention is above 98.5% across standard benchmarks, native Chinese is fully preserved, and the classifier, taxonomy, and benchmark we built to do this work are all released openly under Apache 2.0. These models are open-sourced and available now. What surprised us was how thin the censorship layer was. A rank-32 LoRA can't teach a model the death toll of the Great Leap Forward from scratch, so the facts had to be in the base weights already. We just removed what was keeping the model from saying them. We've also fully ReAligned the two largest Qwen 3.5 models, and they're currently available to enterprise and government partners. And if you're looking for a model built for security research and red-team work that closed models routinely refuse, we've got that too. Tired of censorship limiting what the best open-source models can do for you? Reach out. The ReAligned Models: huggingface.co/collections/La… Read Eric Hartford's Blog Post: lazarus-2-0.webflow.io/blog/introduci…

Gartner found that 80% of companies cut headcount alongside AI adoption, with no correlation to ROI. The returns came from somewhere else. The reason is the AI Production Gap, the distance between a model that completes a task in a demo and a system that holds up in production. You don't close that gap with a better model, and you definitely don't close it with a layoff. You close it with execution: problem, prompt, and context engineering, wrapped in the orchestration, evaluation, and governance that production work actually requires. fortune.com/2026/05/11/ai-…

Not wrong. We've designed for and watched this for years. The consistency framing is honestly downstream of a bigger problem: enterprise AI keeps getting shipped without the workflow context, policy, or escalation logic that would make consistency mean anything. Same answer every time doesn't mean much if the system has no idea what's at stake when it answers.

I’m coming to the conclusion that the biggest challenge for Enterprise AI, and AI in general , as of now, is that it’s still impossible to make sure that everyone gets the same answer to the same question, every time. Which is a great response to the doomers. AI doesn’t know the consequences of its output. Judgement and the ability to challenge AI output is becoming increasingly necessary, and valuable. Which makes domain knowledge more valuable by the second. Am I wrong ?

Clearwing replicated Glasswing’s FFmpeg vulnerability discovery without relying on Mythos as the backend. As @TheEricHartford puts it: the workflow is the magic sauce. Models matter. Systems matter more. github.com/Lazarus-AI/cle…

AI ethics is not a policy document. It is system design. AI decisions must be explainable, consistent, and defensible. If not, the issue isn't just performance. It's risk. lazarusaie.com/blog/ai-in-ins… youtube.com/watch?v=73WhlD…