Ai Automation
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LLM Evaluation Frameworks: Proving Your AI Feature Actually Works
"It seems good" does not survive procurement. Eval set construction, judge models, regression gating, and the metrics that hold up in an enterprise deal.
Gaurang Ghinaiya
July 31, 2026
AI Document Processing: OCR + LLM Extraction Pipelines That Hold Up
Invoices, intake forms, contracts: document extraction is now an LLM pipeline problem. Architecture, confidence thresholds, and the review queue design.
Gaurang Ghinaiya
July 30, 2026
LLM Cost Optimization in Production: Token Economics for Real Products
LLM bills grow faster than usage because context grows. Prompt caching, model routing, context budgets, and the attribution you need before optimizing.
Gaurang Ghinaiya
July 29, 2026
AI Agents for Business Automation: What Works Beyond the Demo
Agents are LLMs with permission to act. Where they beat RPA, how to design tools they cannot misuse, and the guardrails that make them deployable.
Gaurang Ghinaiya
July 28, 2026
RAG Pipeline Design for Non-Hallucinating AI: What We Learned Shipping to Production
Most RAG implementations that work in demos fail in production. Here's the architecture, including the chunking strategy, embedding model choice, hybrid retrieval, and confidence thresholding, behind a pipeline that achieves over 80% retrieval accuracy at scale.
Gaurang Ghinaiya
June 6, 2026
Why AI Chatbots Hallucinate and How to Fix It
Hallucination is an inherent property of how language models work, not a bug that will be patched. Here is how to architect AI systems that cannot hallucinate about the things that matter.
Gaurang Ghinaiya
June 3, 2026
Production RAG Architecture: Chunking, Embeddings, Hybrid Retrieval, and Anti-Hallucination. The Complete Guide
RAG is not a single thing. It is a pipeline with seven or eight discrete engineering decisions, each of which significantly affects accuracy. This is the complete architecture guide based on what we have learned shipping RAG systems to production.
Gaurang Ghinaiya
June 3, 2026
LLM Integration Patterns for B2B SaaS: From API Wrapper to Production-Grade AI Feature
Adding an LLM to your B2B product is not the same as building a consumer chatbot. Token costs, reliability, latency, multi-tenant data isolation, and auditability all look different when your customers are businesses using your AI feature in production workflows every day
Gaurang Ghinaiya
May 28, 2026
Engineering Post-Sale Customer Engagement: Retention Automation Without the Spam
Post-purchase sequences that convert are data-driven systems built on behavioral signals, purchase history, and product feedback loops, not emails written once and forgotten. This is the engineering behind retention systems that actually move the needle.
Gaurang Ghinaiya
May 15, 2026
How We Cut Product Image Costs by 80% for an E-commerce Seller Processing 1,500 Images Per Week
A UK-based e-commerce seller was spending three days and significant labour cost between photo shoot and live listing for every watch in their inventory. We built an automated image pipeline that reduced per-image cost by 80% and eliminated the backlog. Here is the architecture and what it actually cost to build
Gaurang Ghinaiya
May 6, 2026
The Anti-Hallucination Stack: Engineering LLM Products That Are Accurate Enough to Trust
Hallucination is not a bug you fix. It is a property of the model that you design around. The engineering work involves building detection layers, confidence mechanisms, and fallback behaviors that make a product trustworthy, even when the model is wrong.
Gaurang Ghinaiya
May 4, 2026
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