Build a Customer Context API for Support AI

1. 1

   Solve identity resolution first

   The same customer exists under different emails, phone numbers, and guest checkouts across the commerce platform, helpdesk, and subscription tools. Confidence-scored identity stitching across those systems is the genuinely hard problem and therefore the product; the API response is just its packaging.

2. 2

   Design the context payload for LLM consumption

   Return a structured summary tuned for prompt injection: relationship tenure, order and issue history in compact form, current open items, sentiment trajectory, and value tier, with token budgets in mind. A context block the bot vendor can drop straight into their prompt is what makes integration a day instead of a quarter.

3. 3

   Build for the stack that actually exists

   Deep connectors for the dominant commerce platform, the two or three leading helpdesks, and the subscription and review tools DTC brands standardize on. Ten excellent connectors covering the standard stack beat fifty shallow ones; brands cluster hard.

4. 4

   Treat privacy as an API contract

   You move personal data between systems on the brand's behalf: data processing agreements, regional residency options, field-level redaction controls, and audit logs are enterprise-necessary from the first customer. Compliance readiness is a sales asset with every serious buyer.

5. 5

   Sell both sides of the market

   Support-AI vendors embed you to make their bots smarter, a partnership sale, while brands running homegrown bots buy directly. Usage-based pricing per context lookup with platform minimums scales across both, and vendor partnerships compound distribution.

6. 6

   Prove the delta publicly

   The demo is the same conversation twice: context-blind bot versus context-rich bot handling a loyal customer's late order. Publish resolution-rate and escalation improvements from pilots; the delta is dramatic, and it is the entire pitch.

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