Projects

AI Equity Research. Nine Agents. One Research Note.

Nine AI agents research, debate, fact-check, and synthesize — delivering a structured equity research note with independent verification and a human in the loop at every critical stage.

The Stock Analyzer module is part of the multi-agent platform and takes a single ticker or company name and orchestrates NINE specialized AI agents in a structured pipeline: a data agent pulls live financials, trend history, earnings records, and recent news from public sources; three analyst agents work in parallel to assess fundamentals, business quality, and risk; a Bull and Bear agent argue opposing investment cases from the same evidence; an independent Fact Checker agent then cross-validates key claims against the original source data before any conclusion is drawn; and a Synthesizer weighs everything to produce a structured equity research note modelled on sell-side analyst output. Every agent has a defined role, defined input, and a defined output. Every prompt sent to the AI is inspectable. At key stages, a human reviews intermediate outputs and decides whether the pipeline proceeds — the system does not run to completion unattended.

The platform is built on LangGraph, the leading framework for multi-agent orchestration, and demonstrates five agentic AI design patterns in a single working application: fan-out and merge for parallel agent execution, adversarial synthesis where agents argue opposing cases, conditional routing that handles failures without hallucinating past them, evidence-grounded confidence scoring that keeps AI outputs honest about what they actually know, and independent fact-checking as a dedicated pipeline stage rather than an afterthought. This is what multi-agent AI architecture looks like when it is built with discipline rather than demos. The output is a rated evidence summary, three data visualizations, and a Word-formatted research note.

This illustration depicts some of the possibilities over the next 10 years.

AI - the Next Decade

This is my prediction, not a claim of certainty: where AI is headed over the next ten years, based on what I see happening today and what builders and users of the technology are already saying. What's already decided, and what genuinely isn't.

This illustration shows how Ai and human judgement need to go hand in hand

AI, Accountability, and the Future of Human Judgment

AI accountability, human judgment, and the capability gap most companies never measure. This insight paper covers AI disclosure, agentic AI oversight, skill erosion, and the productivity numbers that do not add up. A clear standard for honest, defensible AI use and governance.

Illustration showing how AI skills are transferable to professional life

Ten Ideas to Build Personal AI Habits Before Work Adoption

Stop treating AI like a search engine. From restoring photos to planning dinner, these ten simple exercises build the "elite" skills you need for work. Master AI through habits you actually care about and then bring that power to your work use cases. Read more to bridge the gap!

The illustration reveals how to use multiple AI tools to enhance your content

The Multi-Model Blueprint: How to Use Multiple AIs to Create Superior Content

Stop hitting the invisible ceiling of single-AI workflows. This blueprint reveals how to manage a team of AI experts—mixing tools like Claude, ChatGPT, and Perplexity to shatter limits and create bulletproof, elite-tier content. Stop prompting; start building your production line.