Why Sigma: AI evaluation for real-world deployments
AI is advancing fast. Evaluation is not. Most AI systems today are still evaluated the way they were a few years ago — with automated
Why better data builds better AI
The role of data in teaching nuanced AI Generative AI doesn’t just need labeled data; it needs representative data. That means multilingual, multi-domain corpora designed
Teaching AI to truly understand what we mean
Why meaning matters for AI LLMs trained only on raw text often produce plausible but incorrect interpretations. The result: outputs that sound convincing but fail
Why red-teaming your AI protects your brand and your users
Why traditional testing isn’t enough Most organizations validate AI systems with internal QA or benchmark datasets, but these don’t simulate adversarial conditions. Real users (or
Connecting the dots: why integration annotation powers better AI
Why multimodal matters Generative and agentic AI are moving beyond single prompts to multi-step scenarios. For example: Without integration, these systems return fragmented responses —
Teaching AI to hear what we mean, not just what we say
When accuracy isn’t enough When a customer hears, “I’m happy to help,” they instantly know if the speaker truly means it — by tone, pacing,