Own BigQuery AI Table Analyzer.

What you'd own.

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

• ●Branded product site, logo, email templates, and blog post library
• ●Customer acquisition playbook: Slack/Reddit/HN inbound targeting, LinkedIn outreach scripts
• ●Year 1 financial model with ARR projections and unit economics
• ●Founder persona and ICP definition for analytics engineers and data leads
• ●MVP codebase and agent spec for BigQuery table analysis
• ●30-day customer success playbook and trial-to-paid conversion strategy
• ●Competitor analysis against dbt docs, DataHub, and OpenMetadata

What Roll Digital builds for you.

Custom scope to take BigQuery AI Table Analyzer from MVP shell to operating business.

• ▸Production backend on FastAPI with Postgres for table metadata and AI analysis
• ▸Stripe billing integration supporting $99, $299, and $999 monthly pricing tiers
• ▸OAuth integration with Google Cloud for BigQuery dataset access authentication
• ▸Admin dashboard for trial management, team seat provisioning, and analytics
• ▸Email and Google OAuth signup flow with company-level account management
• ▸30-day automated customer onboarding and trial-to-paid nurture email sequence
• ▸Deployment infrastructure on AWS or GCP with monitoring and uptime
• ▸BigQuery analysis agent using Claude or GPT-4 for table documentation

Why this could work.

Analytics teams at 50-500 person companies genuinely need BigQuery documentation, and the inbound GTM through dbt Slack, r/bigquery, and HN reaches your qualified buyers efficiently. Converting 30 trials/month at 15% to paid generates $100k-120k ARR. Data engineers actively complain about documentation pain in these communities, signaling demand for a simple, hosted solution they don't have to self-host. The market exists.

What you'd risk.

Google adds a native 'describe my tables' feature to BigQuery within 18 months, eliminating the TAM overnight. Competing free tools like dbt docs, DataHub, and OpenMetadata already solve this for engineering teams willing to self-host or invest engineering time. Data engineers lack budget authority, requiring manager approval for $150/month, extending sales cycles past trial momentum. Even flawless execution faces structural headwinds.

Who this fits.

An analytics engineer or data lead with an existing customer network of 50-200 companies, or a solo SaaS founder comfortable with B2B sales and technical integrations. You need scrappiness to validate the GTM in your first 90 days and willingness to do founder sales until you establish repeatable unit economics. This fits an operator who understands data engineering and can speak credibly to the pain.

Timeline.

From contract signing to operating business.

1. Day 1
   Receive codebase, financial model, customer acquisition playbooks, and access to founders
2. Week 1
   Launch MVP on staging, set up Stripe test mode, begin customer research
3. Week 3
   Production deployment, first 5 customer onboardings, and trial conversion playbook refinement
4. Week 6
   Outbound LinkedIn campaign reaches 50 target data leads, track trial signup velocity
5. Day 90
   Aim for 15-25 trials, 2-4 paid conversions, and validated product-market signals

Pricing.

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