Strategy question
Imagine Google engineers came up with an API service that converts freeform text to SQL statements. How would you go about making this into a service, taking it to market?
- Strategy
- Hard
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What this question tests
Tests go-to-market strategy for a developer and enterprise API product, including positioning, pricing and channel.
How to approach it
- Target data analysts, BI tool builders and enterprise software teams who need natural-language data access, not consumers.
- Assess competitors: BigQuery's own natural-language features, Snowflake Cortex, and various startups already in this space.
- Launch as an API and BigQuery integration first, leveraging existing GCP enterprise relationships, before a broader multi-cloud API.
- Price on usage, per query or per token, with a free tier for developer trial and volume discounts for enterprise.
- Define success as query accuracy on real customer schemas, API call volume, and conversion from free tier to paid contracts.
What a strong answer includes
- Picks a concrete initial distribution channel, bundling with BigQuery and GCP, that leverages Google's existing enterprise relationships.
- Names query accuracy on real, messy schemas as the core quality bar, since that's what actually blocks adoption of text-to-SQL tools.
- Anchors pricing to standard API economics rather than inventing a novel model.
Common mistakes
- Treating this as a consumer product instead of an enterprise or developer API.
- Ignoring accuracy on messy real-world schemas as the actual adoption blocker.
Likely follow-up questions
- How would you measure and improve accuracy on ambiguous natural-language queries?
- How would you price this against free open-source LLM alternatives?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop