I built Alpha Insights: AI business research with validators, not just prompts

Eric Young — Thu, 21 May 2026 09:24:38 +0000

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

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.