• Unchained Labs unveiled Stuntman, a natural‑language, AI‑driven automation platform at SLAS.
  • Stuntman is presented as a way to create lab automations using conversational language rather than code.
  • The launch signals growing interest in AI tools that simplify lab workflows and reduce technical barriers.
  • Labs considering adoption should weigh integration, validation, and data‑integrity steps before deployment.

What happened

Unchained Labs introduced Stuntman, described as a natural‑language, AI‑powered automation platform, during the SLAS conference. The announcement positions Stuntman as a tool that lets scientists and technicians build and run automated workflows by describing tasks in everyday language rather than writing complex scripts.

Why this matters

AI-driven, natural‑language automation promises to lower the technical barrier for automating routine lab tasks. For many labs, that could mean faster setup of protocols, fewer manual steps, and reduced time spent converting experimental procedures into machine‑readable instructions. At a time when organizations are racing to increase throughput and reproducibility, tools that simplify automation are drawing attention.

But the shift also raises practical questions. Successful adoption requires that new platforms integrate with existing instruments and data pipelines, meet internal validation and quality‑control standards, and preserve audit trails required for regulated work. Labs that move quickly without addressing these issues risk workflow disruption, data inconsistencies, or compliance gaps.

How Stuntman fits into the automation landscape

Unchained Labs’ Stuntman arrives as more vendors add AI layers to lab software, aiming to make automation accessible to non‑programmers. Launching at SLAS — a central event for laboratory automation and screening professionals — gives the product immediate visibility among potential users and integrators.

Industry adoption will depend on several practical factors: whether Stuntman supports the instruments and software already in place at a given site, how easily it can be validated for experimental and regulatory needs, and how well it handles edge‑case procedures that often require human judgment. The platform’s natural‑language approach may accelerate basic workflow creation, but complex protocols and safety‑critical steps will still need careful review.

What labs should consider next

  • Trial and validation: Run pilot projects to verify that automations behave consistently and maintain data integrity.
  • Integration planning: Confirm compatibility with existing lab information management systems (LIMS), instruments, and data storage.
  • Change management: Train staff on new workflows and establish review processes for AI‑generated protocols.

What to watch

As Stuntman moves from announcement to user trials, look for early case studies showing real‑world time savings, integration breadth, and how validation challenges are handled. If the platform lives up to its promise, it could broaden access to automation — but labs should proceed deliberately to avoid unintended risks.

Image Referance: https://www.genengnews.com/topics/artificial-intelligence/unchained-labs-debuts-stuntman-an-ai-driven-automation-platform/