Perle

We started as Kiva AI, but we’ve become something even better: Perle. Our mission is to deliver wisdom to data by training high-quality AI models to give your team a solution that saves time, improves model outcomes, and keeps them focused on high-value work. Exceptional AI is here to drive your business forward.

Do you use LLMs to generate code? If so, you’re not alone. GitHub reported that 46% of code on their platform now comes from GitHub Copilot. But, security researchers have documented cases where LLM-generated code contained subtle flaws that could lead to injection attacks, memory leaks, authorization bypasses, and other serious security issues. How can you protect against this? Careful implementation, rigorous evaluation frameworks, and continuous human oversight throughout the development process. Sajjad Abdoli, Founding AI Scientist, explains more on our blog: https://lnkd.in/eYm8R4t4

Couldn't make it to HumanX? Nate Castro, on our Growth team, recently went to the event and recapped his key takeaways. In short: AI’s future isn’t about bigger models, but better data. Read more: https://lnkd.in/e6H3AzhA

Kate C. Rechenmacher shares the latest AI news this week. Check out the highlights ?? 1?? Transparency in AI is non-negotiable. China’s new mandate to label AI-generated content is just one example of regulators pushing for transparency and governance.? 2?? AI hardware is surging, but software isn’t keeping up. As hardware improves, the pressure on data infrastructure and annotation quality continues to grow. 3?? Security teams are increasingly overwhelmed by growing data governance responsibilities according to a new study by ISACA. As AI adoption scales, frameworks for data lineage, usage policies, and compliance are critical.? Ready to future-proof your AI data strategy? Perle can help.? Get more AI headlines on our blog: https://lnkd.in/e6_2eSHa

Take our new weekly poll: What’s the biggest challenge you face in data labeling?

AI in operating rooms: How can it be used? Jeremy Wano, MBA, MPA dives into AI-driven surgical diagnostics, how to improve accuracy, why experts are the key to success, and more. Read his insights here: https://lnkd.in/e9abjZEm

Josh Halliday reveals the real impact of scope creep in AI annotation projects: ??Ambiguous business objectives ??Cross-functional misalignment ??Endless iteration cycle ??Undefined technical parameters Perle’s solution — launching soon — uses AI to translate business objectives into precise, executable model-training specifications. That way, you keep your scope in scope — and have a successful AI model. Read more on the blog: https://lnkd.in/ev_2Jjyn

??How clear are your AI project requirements? They could be the cause of delays in your AI model development. Perle’s AI-powered assistant (coming soon!) solves this challenge. It translates your business and research goals into precise model-training specifications. Don’t waste time on efforts not in scope. Learn more about Perle’s AI solutions: https://lnkd.in/e4ZQ8VSA?

If your AI model requires deep domain expertise, why are you relying on generalist annotators? Don’t settle for higher iteration costs, low-quality data, unreliable models, and limited scalability. Learn more about: Why traditional annotation doesn’t work Why STEM experts are the key to high-quality AI training data The future of AI annotation work Read the blog post by Kate C. Rechenmacher: https://lnkd.in/ehM_W4A5

New poll of the week! ?? Q: What’s the most time-consuming part of your data labeling process?