Loading…
Technical depth, practical frameworks, and honest perspectives from engineers who build AI systems in production every day.
A practical framework for deciding between retrieval-augmented generation and fine-tuning when building production GenAI applications. We compare cost, accuracy, latency, and maintainability.
After delivering 200+ ML projects, we've seen patterns in failure. Distribution shift, missing MLOps, data quality debt — here's the field guide to sustainable ML.
Srinivas Narayanan
Jun 15, 2025
LangGraph gives you the scaffolding to build complex agentic systems. But reliability in production requires thinking carefully about state management, error recovery, and human oversight.
Sai Kartikeyan
Jun 5, 2025
Most AI projects don't fail because of the model — they fail because of the data. Here's how we architect data platforms that support AI at enterprise scale.
Gundumapalli Venkat
May 22, 2025
CFOs and boards are more AI-skeptical than ever. Here's a framework for calculating and communicating AI ROI that wins executive buy-in — with real numbers from our client engagements.
Srikanth Raju
May 10, 2025
Edge deployment of vision AI is hard — inconsistent lighting, model drift, hardware constraints, and latency requirements. Here's what we've learned deploying on factory floors.
Anand Krishnamurthy
Apr 25, 2025
Inference costs can be the biggest hidden expense in AI deployment. We walk through model quantization, batching strategies, spot instances, and caching to dramatically reduce your AI cloud bill.
Padmanabhan Iyer
Apr 12, 2025
Production prompt engineering is a discipline, not a trick. We cover systematic evaluation, prompt versioning, adversarial testing, and meta-prompting strategies for reliable enterprise LLM outputs.
Sai Kartikeyan
Mar 28, 2025
Whether you're exploring AI for the first time or scaling an existing intelligent system, our team is ready to help you create measurable impact.