31 Industry Templates vs. Salesforce's 16: How We Did It

Salesforce built 16 industry templates in 25 years.
We built 31 in four weeks.

AI didn't replace strategy — it amplified execution to a level that wasn't possible before.

This is how one small team, with AI as its multiplier, did what a $300B company couldn't.

The Challenge: Build for 31 Industries, Not Just One

Every industry thinks it's special — and it is.
Construction needs field tracking. Finance needs audit trails. Healthcare needs HIPAA compliance. Logistics needs route optimization.

Salesforce's solution? Decades of slow rollout and billion-dollar acquisitions.

Our goal was simpler:
Can AI help us clone, customize, and deploy industry-specific CRMs at scale — without breaking architecture or sanity?

Spoiler: yes.

The Playbook: 4 Weeks, 31 Templates

Week 1 – Foundation

We started with one "meta-template": a multi-tenant CRM backbone that could flex into any vertical.
Core entities — Clients, Deals, Activities, Tasks — were defined once and versioned globally.
Each record carried a tenantId and industryId, giving us perfect data isolation with industry context baked in.

Week 2 – AI Replication

Instead of cloning manually, we gave Claude and GPT-4o structured prompts:

They generated the scaffolding. We reviewed, refined, deployed.

Week 3 – Industry Layering

Each template got its own flavor:

• Construction: job sites, materials, subcontractors
• Finance: portfolios, compliance tags, SEC logs
• Healthcare: patient privacy metadata, appointment sync
• Education: course enrollment, progress tracking

Each schema, dashboard, and role system was auto-generated with just enough human supervision to stay authentic.

Week 4 – QA & Deployment

Once the 31 schemas were stable, AI documented each one, wrote onboarding guides, and generated demo data.

Our system packaged them as instantly deployable templates on Vercel — 31 industries, ready to go live.

The Architecture Behind the Magic

All 31 templates share one modular backbone:

/industries
   /construction
   /finance
   /logistics
   /education
   /healthcare
   /real-estate
   /legal
   /insurance
   /manufacturing
   /retail
   /automotive
   /energy
   /agriculture
   /nonprofit
   /government
   /telecom
   /hospitality
   /transportation
   /engineering
   /media
   /consulting
   /recruiting
   /marketing
   /security
   /food-services
   /architecture
   /accounting
   /utilities
   /technology
   /ecommerce
   /sports

Each industry folder contains:

• schema.json — AI-generated field definitions
• workflows.ts — role-based logic
• ui-config.json — dashboard layout and filters
• docs.md — onboarding docs auto-written by Claude Haiku

The secret is context inheritance.
Every template inherits from the base CRM, so updates ripple across all 31 industries instantly — no forks, no rewrites.

The AI Workflow

Each AI model played a specific role:

• Claude Sonnet — structure & logic (field mapping, compliance rules)
• GPT-4o — code generation (schemas, API adapters)
• Claude Haiku — documentation engine (user guides, onboarding)
• GPT-4o-mini — validation & consistency checks

We routed tasks to whichever model did it best — same inputs, specialized outputs.
The result: consistency at scale without human fatigue.

The Results

31 industry templates.
4 weeks.
Zero manual duplication.

Each template inherits new features automatically, shares the same AI forecasting logic, and maintains its own secure data boundaries.

To users, it feels like 31 separate products.
To us, it's one intelligent platform wearing 31 different suits.

The Lesson

AI isn't replacing software teams.
It's replacing the lag between strategy and execution.

Salesforce needed decades because they scaled with humans.
We needed weeks because we scaled with intelligence.

The future isn't about more developers — it's about smarter architecture and AI that actually builds with you.