SuperAnnotate

🚀 Great conversations on evaluation, labeling, and model quality at #DataAIWorldTour in London!

Most AI Agent failures aren’t technical, they’re evaluation failures. POCs stall because teams lack a structured way to measure performance, leaving ML teams without the data they need to improve, and leadership without the confidence to make a launch decision. That’s why we put together The Practical Guide to Evaluating Agentic AI Systems. 📗 You’ll learn how to: - Define metrics that align with business goals - Combine human-in-the-loop review with LLM judges - Build LLM judges that really work - And the processes for evaluation in development and in production Read the full guide: https://lnkd.in/eiHwE2xs #AI #AgenticAI #Evaluation #HumanInTheLoop #AITrust #SuperAnnotate

🚀 We’ve expanded Agent Hub to make it even easier to use LLMs to automate parts of your data annotation and model evaluation pipelines. - Connect to any model on Fireworks AI, Google Cloud Vertex AI, Databricks, or Amazon Web Services (AWS) Bedrock - Build large-scale, automated pre-labeling & evaluation pipelines in Orchestrate - Work faster with new usability improvements Read our latest article to learn more: https://lnkd.in/e24aHGZG

AI has an evaluation problem. At the Industrial Future Summit in Stockholm a few weeks ago, I kept hearing the same story from enterprises I talked to. - Metrics in AI pilots look great, but the end users are still disappointed - Teams can't agree on what success looks like. - Leadership lacks the data they need to make deployment decisions. I had the opportunity to share how we at SuperAnnotate help companies like Flo Health Inc., Databricks, and ServiceNow build better AI and productionize it. The secret is to take evaluation seriously. That means spending the time needed to define the right KPIs for the use case and setting up comprehensive pipelines to measure that, using a combination of human-in-the-loop, programmatic checks, and LLM judges. This helps the team gather the information they need to improve systems in a data-driven way, and to base the decision to take a model from pilot to production on. Right now, we are in the final steps of putting together an ebook with our eval playbook. Drop a comment if you want me to send it your way once it's ready!

🎬 The second episode of AI in 10 explores why evaluation is critical across the AI lifecycle, when to introduce judges, and how to balance automation with expert oversight to keep models aligned, safe, and production-ready. Jason Liang, Co-Founder & SVP of Business Development, and Julia MacDonald, VP of AI Ops, discuss the following topics: - What “LLM as a judge” actually means - Why enterprises can’t rely on models to evaluate themselves - How to blend human expertise with scalable AI evaluation - Common mistakes in deploying agentic systems - When to start evaluating your agents Watch the episode: https://lnkd.in/e-uumhNZ

Need to ship high-quality agentic, multimodal, and frontier AI faster? Join us: ⚡ London, Oct 16 📍 Databricks Data + AI World Tour | Booth #K5

Flo Health Inc. boosted the accuracy of their medical assistant AskFlo from 78% to 91% by evaluating with medical experts in SuperAnnotate. The platform streamlined human review, scaling evaluations 10× faster. But how do you actually evaluate an agent with humans in the loop? Here’s a quick walkthrough.

With Google Gemini now seamlessly integrated into SuperAnnotate’s platform, teams can build, evaluate, and scale AI applications and agents faster - powered by high-quality annotated data and state-of-the-art models. The SuperAnnotate & Google Cloud partnership makes it easier than ever to develop safe, reliable AI. From advanced Human-in-the-Loop workflows to scalable data pipelines, unlock the full potential. 👉 Check out the eBook - https://lnkd.in/e2mwUaH2

Every AI team faces the same question: build annotation tools in-house, or buy a platform. Building can work when projects are small and predictable. But the cost of maintaining your own system grows fast once needs expand. Buying is the faster path when projects scale and demands keep changing. It takes scaling and maintenance off your team’s plate so engineers can focus on the core product. If your workflows are too niche for standard tools, the best option is a customizable platform. It gives you flexibility without the long-term burden of building from scratch. In this piece, we break down when to build, when to buy, and where customization fits in. Read more: https://lnkd.in/eckDm2yy

Learn what Agentic AI is, how it works, its benefits and failures, and the best practices enterprises use to make AI agents more reliable. Read the article: https://lnkd.in/eDKhjWWk