The model was not the moat. The data layer was.
Jeff Brokaw rebuilt the commercial layer of a confidential 8-person AI-native SaaS in electrical distribution: positioning, GTM, data-moat strategy, catalog-intelligence narrative, and usage-metered AI pricing. The work turned technical capability into a business the market could understand.
Situation.
Electrical distribution looks like an AI problem until you get close. Then it becomes a messy data problem wearing an AI costume: PDFs, BOMs, emails, aliases, incomplete attributes, and ERP systems never designed for retrieval. The AI is the easy part. The data is the job.
Commercial problem.
The market did not need another vague AI story. It needed to understand how quote automation could be trusted: product matching, SKU normalization, retrieval, recommendation logic, and pricing grounded in ERP reality. Trust is not a feature. It is the prerequisite.
Results in advance: if the system cannot trust the product data, the LLM is just a confident intern with a calculator.
What I rebuilt.
Jeff mapped the commercial architecture underneath: catalog ingestion, SKU normalization, vector search, RAG retrieval, LLM quote assist, ERP-grounded pricing, and a usage-metered billing model that did not pretend compute was free. That last part matters more than most people admit.
Durable result.
The company could explain the business in commercial language: not "AI quoting," but a product-intelligence layer that makes quotes faster, more accurate, and harder to commoditize. The commercial architecture is what separates a defensible business from a feature someone else ships next quarter.
Artifact drawer
Positioning architecturePricing modelData-moat narrativeQuote workflow mapBuyer storyGTM wedge