Engineering the Future
Technical deep-dives, case studies, and practical guides from our AI engineers and researchers.
A deep-dive into designing multi-step AI agents that plan, execute, and recover from errors in production — with practical examples from our work.
Not every task needs GPT-4. Learn when Small Language Models outperform their larger counterparts — in speed, cost, privacy, and accuracy.
End-to-end document automation handling 40+ document types — extracting clauses, flagging risks, and saving 2,400 manual hours per month with 98.3% accuracy.
Retrieval-Augmented Generation eliminates hallucinations and grounds LLMs in your proprietary data. Here's how to build production-grade RAG systems.
Single agents hit walls. Learn how multi-agent orchestration enables AI to tackle tasks that require parallel execution, specialization, and coordination.
GPT-4o + Claude-powered sales assistant integrated into Salesforce — auto-enriching leads, drafting hyper-personalized outreach, and predicting deal closure probability.
A practitioner's guide to fine-tuning Llama, Mistral, and Phi models for enterprise tasks — covering data prep, training, evaluation, and deployment.
Case study: Building an ML pipeline processing 50K transactions/second with explainable AI outputs — deployed at a major Indian bank.
How we replaced manual visual QC at an automotive parts manufacturer with a real-time CV system achieving 99.8% defect detection accuracy.
How we shipped a production AI mobile app in 8 weeks — voice input, image understanding, offline-first sync, and real-time AI responses on iOS and Android.
The next generation of software won't just use AI as a feature — AI will be the architecture. Here's what that means for how we build.
How we used Claude and GPT-4o together to automate end-to-end business workflows for an e-commerce company — saving 1,200 hours/month across finance, ops, and marketing.