Perle

With AI, we complain about its inconsistency to be reliable; we complain about it being rigid and not diverse enough. Ahmed Rashad who works on the frontline with human experts, shared something that serves as a sharp reminder why we need humans: "...  that inherent variability in humans that everyone complains about. It's actually a beautiful thing." The uniqueness of human is where the wisdom lives - and believe it or not, Ahmed is trying to make it work with AI: "We're actually trying to extract that human wisdom and that variability". Tune in to our latest founder wisdom sharing with Ahmed Rashad on TwoSetAI. [link in comment] For everyone working from home today feeling a bit lonely - share one piece of wisdom crossing your mind right now? :)

I started Perle to solve a problem I’ve seen in every AI project I’ve worked on: even the best models fail without great data. That’s why we created Perle Labs, a platform that connects enterprises and frontier labs with verified human expertise for model training, QA, and evaluation. Today, I’m excited to share that the Perle Labs beta launch is LIVE. Built on Solana, it’s the first step toward a global contributor network where qualified experts complete real annotation tasks, earn rewards, and build reputations for delivering the precision and judgment that production AI depends on. For customers, it means a scalable source of verifiable human feedback. For contributors, it’s a better way to join the AI economy. We’re building the bridge between human expertise and machine intelligence. More info below. 🔗 Link in comments. 

We recently worked with a healthcare AI company developing autonomous medical agents for note generation. Their goal was to reduce clinician documentation workload and improve record accuracy. Perle helped strengthen the model architecture, built a benchmark framework with medical experts to capture real-world edge cases, and supported multilingual quality assurance in Arabic, Spanish, and other languages. The outcome was a more reliable generative stack, clinically relevant evaluation, and broader accessibility for diverse patient populations bringing their medical AI closer to safe, scalable deployment. Learn more in our full case study: https://lnkd.in/etYz5inX

Most AI models fail because of their data. When annotation is handled by generalists, you end up with: ❌ Higher iteration costs ❌ Missed edge cases ❌ Slower model performance gains The fix? AI scientists. They bring the domain knowledge, technical context, and precision that transform annotations from “just labels” into the foundation of high-performing, trustworthy models. At Perle, we don’t just label data. We use experts annotators to ensure high-quality data. Learn more: https://lnkd.in/eX9df3kj

Too many AI initiatives start with bold ambitions then derail in weeks as requirements shift, edge cases pile up, and teams scramble to catch up. In annotation-heavy projects, scope creep is especially insidious. The real costs of loose scoping: ❌ Vague business objectives that leave teams guessing what “success” means ❌ Cross-functional misalignment where data, engineering, and product diverge on priorities ❌ Endless loops: more labels, new categories, rework over rework ❌ Undefined technical specs forcing teams down expensive trial-and-error paths At Perle, we believe clarity from day one is non-negotiable. We’ve built tools and processes to: ✅ Translate big-picture goals into precise, executable data specs ✅ Align data scientists, engineers, and product owners on the same scope ✅ Minimize rework by locking down requirements early If your AI project feels like it’s chasing its tail instead of advancing, scope creep is probably the culprit. Learn more: https://lnkd.in/ev_2Jjyn

Smarter AI starts with smarter evaluation. Perle’s fast, expert-driven insights improve and validate your AI models. We evaluate models based on what really matters: ✅ Fast, expert-in-the-loop assessments ✅ Side-by-side model testing and A/B experiments ✅ Flexible workflows to track accuracy, recall, and key metrics ✅ Expert reviews for bias, safety, and compliance From early prototypes to production systems, we help you evaluate AI with confidence. See how it works: https://lnkd.in/e4ZQ8VSA

Generic human oversight doesn’t cut it in AI. In high-stakes domains like healthcare, law, and finance, you need experts-in-the-loop. Here’s why expert guidance matters: 👉 Well-annotated data separates a great model from a mediocre one 👉 Experts catch edge cases that generalists miss 👉 Accurate labels reduce iteration cycles and compliance risk At Perle, we make sure your data pipelines are guided by domain specialists. Read more: https://lnkd.in/eVCsTHwA