How I Built a 299-Category Software Decision Engine

I oversee marketing and technology at a major apparel manufacturer. Three brands, B2B and DTC channels, a 300+ application stack. And I’ve had a growing suspicion that we were spending money on software that AI could handle.

As I took on the challenge of understanding everything that we’ve bought, built, and put to pasture over the years, that suspicion turned into a framework that helped me make better decisions. The framework turned into a database. The database turned into the B4 Index — 299 independently scored software categories across 19 domains and 17 industries. Here’s how it happened.

The problem that started it

Two years ago, I tried to build a custom workflow tool using AI. It failed. Well, I got frustrated with how hard it was and gave up. The technology wasn’t ready. Last year, I tried the same thing. I build 90% of what I wanted over a weekend. Was it perfect. No. But it was almost magical how far development has come in such a short time.

That experience broke something in my mental model. If the cost to build dropped that fast in one category, what about the other 300 tools in our stack? Which ones were still worth paying for? Which ones had AI breathing down their neck directly or was something you could use to build it internally? And which ones were we locked into for no good reason?

I looked for a resource that answered these questions. Something that scored software categories — not individual vendors, but the categories themselves — on whether you should build, buy, or do something in between. G2 ranks vendors. Gartner sells quadrants to the highest bidder. Nobody was asking the actual question: in 2026, given what AI can do, should this category of software even exist as a purchased product?

So I built one.

The framework: Build, Buy, Bridge, or Beware

I started with five dimensions. Each one is scored 1 to 5.

Specificity — how unique is this to your company? Payroll is the same everywhere. Your sales compensation logic is one-of-a-kind.

AI Feasibility — can AI generate 80%+ of the core value today? Not in a demo. In production.

Strategic Control — is this a competitive differentiator or table stakes?

Vendor Value — what percentage of the vendor’s features do you actually use?

Cost Trajectory — are SaaS prices rising while build costs are falling?

Two of those dimensions (Specificity and Strategic Control) average together to form the X-axis: Strategic Differentiation. AI Feasibility is the Y-axis. A threshold of 3.5 on both axes creates four quadrants:

BUILD — high differentiation, high AI feasibility. Your competitive edge. Own it by building it yourself.
BUY — low differentiation, low AI feasibility. Commodity. Buy what works for your use case.
BRIDGE — high differentiation, low AI feasibility. Strategic but AI isn’t there yet. Buy the platform today, build toward owning it.
BEWARE — low differentiation, high AI feasibility. The ground is shifting. Vendors are raising prices while AI is changing the category.

The other two dimensions — Vendor Value and Cost Trajectory — don’t change the quadrant. They determine urgency. How fast should you act?

From 162 to 299 in one session

I started with 162 categories across 12 domains. The original scoring, mostly my work. CRM, ERP, marketing tech, security, analytics… the horizontal software every company evaluates.

Then I realized the gaps. No AI & Machine Learning domain. No Customer Service & Support. No Commerce & Payments. No industry verticals beyond a handful. If someone in healthcare or construction looked at the index, they wouldn’t find their world.

So I expanded it. Through a custom multi-agent research system, I added 125 new categories. Seven parallel AI agents, each handling a domain batch, each researching vendors via Perplexity, scoring against the methodology, writing justifications for every dimension. The agents didn’t guess — they looked up current vendor pricing, checked AI capability claims, and applied the rubric.

Six new horizontal domains. Thirteen industry groups within the Vertical domain. Healthcare, financial services, construction, education, energy, government, automotive, legal, agriculture, media, nonprofits, professional services, transportation.

299 categories. 19 domains. 17 industries. 16,000+ vendor mappings (with more coming)

What the data tells you

The quadrant distribution is revealing:

BUILD: 44 categories (15%) — This is smaller than people expect. Most software is still better to buy. But the BUILD categories are growing fast as AI gets more capable.
BUY: 115 (38%) — The largest quadrant. Vendor scale still wins for most infrastructure.
BRIDGE: 88 (29%) — The sweet spot for mid-market companies. Buy the platform, build the edge.
BEWARE: 52 (17%) — The controversial one. These are categories where vendors are overcharging for what AI already commoditized or is about to.

How I actually built this

The B4 Index itself is a case study in the framework. I built it instead of buying a database product. Here’s the stack:

Supabase (PostgreSQL) for the database. Triggers auto-compute quadrants when scores change.
Astro 5 + React for the website. Tailwind v4. Deployed on Vercel. I update everything directly from Claude Code.
Claude Code for implementation. The AI coding tool wrote most of the components, handled the data pipeline, and executed the expansion.
Perplexity for most of my vendor research. Current pricing, market positioning, feature comparisons.

The whole thing runs on less than $5/month in infrastructure. The expansion from 162 to 299 categories happened in a single afternoon. Seven parallel agents, each scoring 15-25 categories with full vendor research and 5-dimension justifications.

That’s the point. The economics of building have changed. What would have taken a team of analysts months to produce, I did in an afternoon with AI assistance. I even ran a full AI assisted audit with other tools to refine the work — AI handles the research and scoring grunt work while I focus on methodology and judgment calls.

What’s next

The index gets re-scored quarterly. Categories shift. What’s BEWARE today might be BUILD next quarter as AI capabilities improve. What’s BRIDGE today might be BUY as vendors consolidate. Each shift is a story, and each story becomes content in The Build Report.

The whole thing is live at benroberts.ai/framework. Pick your industry. See your categories. Make better decisions. I would love to hear your thoughts. We have more tools coming. MCP server to the database, a Claude Skill, downloadable project and a book to connect all the dots.

Ben Roberts is CMTO at a mid-market manufacturer and creator of the B4 Framework. He writes about software decisions, AI-assisted development, and what happens when you let AI challenge your assumptions about what to build and what to buy.