Own AI Output QA Layer.

What you'd own.

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

• ●Production-ready codebase with multi-tenant SaaS architecture and database schema
• ●LinkedIn outreach playbook targeting operations leads at SMBs with AI automation
• ●Customer success framework documenting onboarding, retention, and expansion playbooks
• ●Marketing collateral including Reddit, Discord, and Twitter content templates
• ●Founder's QA domain model and detection logic for output quality issues
• ●Financial model with unit economics, customer acquisition costs, and projections
• ●Brand identity, messaging framework, and conversion-tested landing page

What Roll Digital builds for you.

Custom scope to take AI Output QA Layer from MVP shell to operating business.

• ▸Multi-tenant backend with Postgres database and FastAPI REST API
• ▸Stripe recurring billing integration for monthly subscription management
• ▸Email and OAuth2 sign-in with role-based team access controls
• ▸API detection for OpenAI, Anthropic, and custom model output monitoring
• ▸Admin dashboard for QA rule configuration and output review
• ▸Automated alert system for quality violations via email and webhook
• ▸Customer onboarding playbook and 30-day technical success support protocol
• ▸Production deployment pipeline with Docker, monitoring, and infrastructure automation

Why this could work.

The GTM targets an audience with acute pain: ops leads actively botsitting. The TAM is large, undersaturated, and already has $1,200/year QA spending. Early customers at $100/mo hit unit economics. The core risk is not demand but mindset: moving customers from 'we'll hire an intern' to 'we'll pay $100/mo for software.' This shift accelerates as AI pipeline adoption spreads through SMBs.

What you'd risk.

OpenAI, Anthropic, or LangChain could ship native evaluation and monitoring within 12 months, eliminating a standalone QA layer's value. The pain is real but budget does not yet exist; companies botsitting are more likely to hire a VA or intern than spend $100/mo on software. Generic QA lacks stickiness. Building domain-specific quality checks requires vertical expertise you may not have, limiting total addressable market to one niche per founder.

Who this fits.

This suits an operator with ops or AI automation background and existing customer relationships. You'll feel the botsitting pain or have worked adjacent to it. You'll need to execute GTM through month 2-3 directly; this is not a play-and-delegate business early. You'll refine the QA domain model through customer conversations, then hire specialists for vertical expansion. Best fit if already embedded in the ops or automation community.

Timeline.

From contract signing to operating business.

1. Day 1
   Receive codebase, GTM playbook, founder models, and customer data transfer
2. Week 1
   Deploy multi-tenant infrastructure and configure first customer pilot environment
3. Week 3
   Launch LinkedIn outreach to prioritized ops leads and SMB targets
4. Week 6
   Onboard first paying customers and measure retention after initial success
5. Day 90
   Evaluate month 3 metrics against year 1 model assumptions and plan

Pricing.

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