Alibaba Cloud

Trading Desks to Clinical Trials: Parallels in Applied Vertical AI — Ayush Bhardwaj, Allos AI

Aug 19, 2026 · 20:02

Ayush Bhardwaj, who applied AI at a hedge fund before pharma tech startup Allos AI, argues vertical AI projects die from not being able to judge whether they work. His seven-step recipe: narrow tasks, proprietary data, expert-modeled prompts, observability, then hiring the user—a domain expert—because using LLM-as-judge is a "stupid mistake" that jargons its way out. Verifiable rewards fail in finance and pharma: no answer keys, and data is withheld—30% of firms never disclose clinical trials, and the FDA publicly reminded over 2,000 sponsors in 2026. He says 89% of enterprise AI agents never reach production; the fix is endless expert-in-the-loop learning, starting with error analysis as highest ROI. Moat is never model or infra; only curated domain expertise and proprietary data matter.

The RAG Stack We Landed On After 37 Fails - Jonathan Fernandes

Jun 3, 2025 · 18:52

Jonathan Fernandes, independent AI engineer, details the RAG stack his team settled on after 37 failed attempts, covering orchestration (LlamaIndex), embeddings (BAAI BGE small), vector database (Qdrant), LLMs (GPT-4, Qwen, Llama), reranking (Cohere), monitoring (Arize Phoenix), and evaluation (RAGAS). He demonstrates a live prototype in Google Colab using a London railway knowledge base, showing how a naive RAG returns irrelevant results (e.g., suggesting black cabs for "where can I get help at the station"). By swapping components—replacing in-memory storage with Qdrant, using an open-source embedding model, upgrading to GPT-4, and adding Cohere reranking—the answer improves to "go to booth number five next to the Eurostar ticket gates." For production, he deploys via Docker Compose with NVIDIA embedding/reranking models and Ollama for serving. The episode also stresses the importance of tracing latency per component and using RAGAS for systematic evaluation across many queries.