Trading Desks to Clinical Trials: Parallels in Applied Vertical AI — Ayush Bhardwaj, Allos AI
The talk outlines a structured methodology for building and iterating in applied vertical AI, arguing that model infrastructure and APIs are commodities. The true 'moat' lies not in the technology itself, but in proprietary data and deep domain expertise. The process requires narrowing the problem scope, curating unique datasets (like failed experiments or trade theses), and crucially, integrating a human domain expert ('the user') to create an endless learning loop that guides prompt refinement and error analysis.
Key takeaways
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The Moat is Domain Expertise
12:30
In vertical AI, the competitive advantage does not come from the model (e.g., Claude or ChatGPT) or the infrastructure; it comes from proprietary data and domain expertise which must be curated by human experts.
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Focus on Narrow Tasks
6:35
When formulating a problem for an AI agent, do not try to solve everything at once. Break down complex tasks into very narrow, specific steps (e.g., ranking stocks based on IT capital expenditure in US equities).
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The Importance of Proprietary Data
8:00
Generic data sources are insufficient. The most valuable assets are proprietary datasets that are expensive to acquire or withheld by industry players (e.g., failed clinical trials, internal trade theses).
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Hire the User, Not Just the Model
15:30
The single most critical step is hiring a domain expert ('the user'). This person guides the tool by curating sources, refining prompts, and performing judgment—a process that transforms generic AI tools into industry-specific assets.