Notes from the field.
Engineering deep-dives, research summaries and operational lessons from running production AI.
AI Agents vs AI Automation: What's the Difference?
Discover the difference between AI agents and AI automation, explore real-world business applications, and learn how enterprises combine both to build smarter, more reliable workflows.
Introducing Natton.ai — the independent platform for AI you can trust
Why we're building the most rigorous data, training, and evaluation stack for frontier labs and enterprises.
RLHF at scale across 11 Indic languages
Our pipeline for collecting, calibrating, and shipping preference data in Hindi, Tamil, Telugu, Marathi, Bengali, and more.
Evaluating voice agents: WER isn't enough
A framework for judging voice-agent quality that goes beyond word-error rate — politeness, intent, recovery, latency.
What we look for in Natton Crowd contributors
Recruitment, calibration, retention — and why we pay above market for native-language domain experts.
The Future of Enterprise AI
Explore how AI is transforming enterprises through automation, analytics, and intelligent decision-making.
AI Data Annotation Best Practices
Learn the importance of high-quality data annotation in building accurate AI models.
Why ground-truth data beats clever prompting
A practitioner's view on why fine-tuning + RLHF on calibrated data outperforms prompt-only approaches in most production settings.
Indic RLHF: lessons from 11 languages
How dialect calibration, code-switch tagging, and rater pools changed our reward-model behavior across Indic languages.
Red-teaming agents that use tools
Practical probe patterns for agents with API access — privilege escalation, exfil, and indirect injection via documents.
Evaluation that survives production
Why automated benchmarks aren't enough — and how to design human-eval suites that scale with your release cadence.
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