Own Quote-AI.

What you'd own.

Every artifact below transfers to your accounts on day one. The whole engine.

• ●Proven B2B GTM playbook targeting job shop owners via LinkedIn
• ●Landing page, sales collateral, and customer intake process
• ●Quote-AI application codebase and core ML quote algorithms
• ●Existing customer relationships and recurring subscription revenue pipeline
• ●Year 1 financial model with validated unit economics
• ●Brand positioning and competitive analysis against manual estimating
• ●Demo deck, sales scripts from 15-20 monthly demos

What Roll Digital builds for you.

Custom scope to take Quote-AI from MVP shell to operating business.

• ▸Production FastAPI backend with PostgreSQL and containerized deployment
• ▸CAD file parsing pipeline for DXF, STEP, PDF with GD&T
• ▸Real-time material cost data feeds from supplier and market APIs
• ▸Multi-tenant architecture with customer account isolation and data security
• ▸Stripe billing integration across tiered pricing tiers at launch
• ▸Email authentication system and customer onboarding automation workflow
• ▸Liability tracking system to flag high-risk quote estimates
• ▸Customer success playbook and initial 30-day onboarding support

Why this could work.

The TAM is large: 60k US job shops spending $2,400 annually on estimating software. The GTM is proven. LinkedIn/email outbound converts at 2-3 closes per 15-20 demos. The pain point is acute: manual quoting wastes 8+ hours weekly. At $220-280/month, unit economics scale quickly once word-of-mouth kicks in after month 6. B2B buyers in this segment are pragmatic, desperate for time savings, and willing to pay for reliability.

What you'd risk.

Success probability is 14%, reflecting execution and market risk. Liability is the biggest vulnerability: if AI underestimates a complex job and the shop eats a $4k loss, trust collapses and churn follows. CAD/drawing file parsing works in controlled demos but fails on real customer files. This is hard ML work, not a prompt problem. Material costs swing 20-30% quarterly; baked-in databases become obsolete within weeks. Year 1 take-home is negative $26,400.

Who this fits.

This fits a B2B SaaS operator with technical depth, $100k+ runway, and tolerance for early-stage risk. Ideally someone with agency background, existing manufacturing/fabrication networks, or proven solo founder experience. Requires comfort with ML uncertainty and liability exposure. Strong fit for someone who can execute GTM while managing customer success in a high-touch, quota-driven B2B environment.

Timeline.

From contract signing to operating business.

1. Day 1
   Code transfer, customer handoff, infrastructure audit complete
2. Week 1
   Production database migration, material cost API integration planned
3. Week 3
   CAD parser improvements complete, first staging customer live
4. Week 6
   Stripe billing live, multi-tenant testing in production environment
5. Day 90
   Full production launch, GTM playbook resumed with new owner

Pricing.

Three ways in, depending on how much you want to build yourself.