Aible Launches SafeClaw Long-Running Agents and Presents at Eight Partner Booths and Sessions at NVIDIA GTC 2026

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• We built our first Aible AI agent running on NVIDIA DGX Spark in 5 minutes. The ROI on Aible agents will be immediate. By reducing attorney response time on research questions we save a few hundred dollars per hour per attorney, the AI agent pays for itself within days

  60% confidence

• Running on our NVIDIA DGX Spark in a secure air-gapped environment allows me to prototype confidently, eliminate surprise spending, and scale as we prove the ROI from each use case

  60% confidence

• The end-to-end serverless AI platform is up to 200X more cost efficient

  60% confidence

• What stood out was the model accuracy, because Aible eliminated hallucinations compared to other AI platforms we evaluated

  60% confidence

• We have extensive experience running Aible agents in production and have seen firsthand how they can safely drive measurable operational impact at scale. Our proactive patient rescheduling agent has expanded from a single department to seven clinical service lines in just over a year, meaningfully improving patient access to timely care

  60% confidence

• Enterprise long-running agents can unlock significant value - particularly across silo'd core systems - but require the kind of IT governance and model monitoring enabled by Aible SafeClaw and NVIDIA NeMo Guardrails

  60% confidence

• Aible SafeClaw takes a different approach where users can test the plan generated by the long-running agent at setup time, approve which tools and models it can use and when, approve exactly which data sources it can access and how, and then approve that plan for ongoing operations

  60% confidence

• Each tool that Aible SafeClaw leverages is completely deterministic and logged in the customer environment as Python code

  60% confidence

• This project used the Aible software environment together with an NVIDIA DGX Spark / Dell Pro Max GB10 system to conduct exploratory analyses on locally stored health‑related data. We ran all model components on the standalone device so that data remained within institutional control. After internal validation, the analytical agent created on the local system was automatically transferred to the Nebius cloud environment without transferring underlying research data

  60% confidence

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