# I built Alpha Insights: AI business research with validators, not just prompts > Source: > Published: 2026-05-21 09:24:38+00:00 Most AI research tools can summarize. That is not the hard part. The hard part is making the model behave like a serious analyst when the context gets long, the evidence is messy, and the answer needs to support a real decision. That is why I built **Alpha Insights**. GitHub: [https://github.com/Ericyoung-183/alpha-insights](https://github.com/Ericyoung-183/alpha-insights) ## The problem When you ask a raw AI model to do business research, the failure mode is usually not dramatic. It is subtle: - it gives a clean answer before the research is actually done - it cites weak evidence with too much confidence - it skips framework steps when the context gets crowded - it mixes facts, assumptions, and recommendations into one fluent paragraph - it produces a report that looks finished, but is hard to audit In business analysis, that is dangerous. A polished answer is not the same thing as a decision-ready answer. ## What Alpha Insights does differently Alpha Insights is an open-source business analysis SKILL for Claude Code compatible runtimes and Codex Desktop. It is not a prompt pack. It is a research workflow with external constraints: - **19 business frameworks**: Porter's Five Forces, Value Chain, SWOT, PESTEL, BCG Matrix, TAM/SAM/SOM, JTBD, Blue Ocean, Three Horizons, Flywheel, SCP, and more - **9 analyst methodologies**: MECE, Issue Tree, Hypothesis-Driven, Pyramid Principle, Triangulation, Pre-Mortem, First Principles, ACH, Expert Interview - **10 research scenarios**: industry research, competitive analysis, product analysis, business model teardown, opportunity discovery, market entry, investment decision, strategic planning, due diligence, ad-hoc advisory - **Evidence chain**: conclusions are tied to source quality and confidence, instead of floating as polished prose - **Multi-track research**: public sources, optional knowledge bases, optional internal data, and expert-interview workflows The goal is simple: make AI stop acting like a generic summarizer and start following an analyst-grade research process. ## The technical idea: harness over prompt The most important design decision in Alpha Insights V4 is this: Prompt instructions are probabilistic. Harness checks are deterministic. So Alpha Insights adds a runtime harness around the AI workflow: - a **state machine** tracks the research stage, tier, loaded frameworks, and deliverables - **stage gate validators** check whether each step has actually produced the required artifacts - **hooks** guard report generation, trigger gate checks, and persist progress incrementally - **HTML write guards** prevent the model from jumping straight to a final report before the evidence and insight stages are validated - **dual-platform adapters** support both Claude Code compatible runtimes and Codex Desktop This matters because agent quality problems are often execution problems, not wording problems. If the model can silently skip a stage, it eventually will. If there is no artifact boundary, the report becomes unauditable. If evidence quality is not checked before recommendations, the output can look smart while resting on sand. ## Why this may be useful beyond business research Alpha Insights is a business analysis tool, but the engineering lesson is broader: For serious AI workflows, we should stop relying only on better prompts. A good agent should have: - explicit stages - persistent intermediate artifacts - validators before transitions - source and confidence tracking - hooks that enforce the boring-but-important parts That is the difference between "the model probably followed the instruction" and "the workflow can prove what happened." ## Install For Codex Desktop: ``` git clone https://github.com/Ericyoung-183/alpha-insights.git cd alpha-insights python3 scripts/install_codex.py --verify ``` For Claude Code compatible runtimes, install the folder as a skill package and keep the root `SKILL.md` frontmatter hooks intact, then run: ``` python3 scripts/verify_cloudcode.py ``` There is also an agent-first installation guide in the repository: ``` Install Alpha Insights from this repository. Follow INSTALL_FOR_AGENTS.md exactly. ``` ## Feedback welcome This is open source and MIT licensed. If you are building AI agents, research workflows, or business-analysis tools, I would love feedback on the harness design, the validator layer, and the dual-platform installation path. GitHub: [https://github.com/Ericyoung-183/alpha-insights](https://github.com/Ericyoung-183/alpha-insights) Stars are appreciated, but serious critique is even more useful. *Disclosure: This article was drafted with AI assistance and reviewed by Eric before publication.*