ETL Data Tool

Every company wants AI. Almost none of them can use it. The bottleneck isn't models — it's the data those models need to think with.

The Job​

When a company wants to use AI for decisions, automation, or intelligence, help them get their data out of silos, validated, and structured so an AI can actually query it — without hiring a data engineering team or waiting six months.

The job: "Make our data usable for AI without rebuilding everything."

The hidden objection: "We've tried ETL tools before — they work for the demo, then break when our data gets weird." The answer is configuration-driven transforms that handle the weird, with validation that proves it worked.

How It Works​

Five stages. Each one removes a category of risk.

This is the Mycelium data readiness primitive. Every mushroom cap venture that needs data — CRM, Prompt Deck, Data Interface — depends on this pipeline running cleanly underneath.

Feature / Function / Outcome​

Competitive Position​

vs Traditional ETL (Fivetran, Airbyte, Stitch)​

vs Custom Development​

Case Studies​

Internal proof: 96% code reduction from manual ETL scripts to platform configuration. Validated with 31 integration tests across source types.

Business Model​

Three tiers. Each one removes more friction.

Revenue model: Subscription SaaS + professional services for complex onboarding.

Unit economics hypothesis: If avg customer is Managed tier at $8K/month = $96K ARR. At 80% gross margin, need 15 customers to hit $1M ARR.

Business Dev​

Product and distribution must co-evolve. The platform only wins if data gets queried after loading.

GTM Sequence​

1. Founder-led pilots: Solve one painful data integration for 3-5 companies. Document before/after.
2. Case study packaging: Publish the construction, SaaS, and real estate stories with hard numbers.
3. Channel partnerships: AI consultancies need clean data for their implementations — become their data prep layer.
4. Content authority: Publish the "AI Data Readiness" assessment framework. Own the category name.
5. Platform expansion: Each new source connector increases platform value for all customers.

Validation Stack​

Following the validation stack:

Commissioning​

Status: SHIPPING — live with internal paying usage. Core extraction-to-load pipeline is production-grade. Query interface and admin layer need build-out for external customers.

Risks + Kill Signal​

Kill signal: If data loads but nobody queries it, the tool is a fancy file mover, not an AI readiness platform. North star: Query Latency < 2s. The metric that matters isn't data loaded — it's data used. Track query volume per customer per week. If it trends to zero after onboarding, the value proposition is extraction theater, not AI readiness.

Shape Up​

Appetite: 6-week cycle. Next cycle: admin dashboard for customer self-service, query interface polish, first external pilot onboarding.

Mushroom Cap Dependencies​

This is the data layer everything else stands on.

When the ETL capability works, every venture gets clean data without building its own pipeline. When it doesn't, every venture reinvents extraction. This is the highest-leverage Mycelium primitive.

Next Steps​

1. Ship admin dashboard — external customers need self-service visibility
2. Polish query interface — the moment of truth for "AI-ready" claim
3. Onboard first external pilot — construction company with CSV pain (warmest lead)
4. Document onboarding playbook — productize what works, kill what doesn't
5. SOC2 preparation — enterprise tier requires it, start early

Smallest move: One external company loads their messiest data source and runs a query against it within 4 hours. That's the proof.

Context​

• Jobs To Be Done — Demand-side thinking: what progress, not what features
• Validate Demand — Awareness levels and kill signals
• Shape Up — Fixed time, variable scope
• AI Data Industry — Market context and competitive landscape
• Flow Engineering — Maps that produce code artifacts
• Type-First Development — Schema-driven build approach
• Vertical SaaS — The vertical playbook this capability enables
• Horizontal SaaS Toolkit — Shared surfaces ETL feeds into
• Phygital Mycelium — The capability network this PRD belongs to
• Mushroom Caps — The ventures that consume this data primitive
• Data Interface — The access layer that sits on top of ETL output
• Business Flow — Idea → Strategy → Operations → Growth