Identity-as-a-Service: Unifying a Fragmented Data Ecosystem
The Challenge
A marketing technology company had a powerful multi-partite identity graph — offline persons, digital devices, household clusters — but no unified service layer. Every team wrote direct queries against internal graph tables. Third-party data onboarding was manual per source. No shared auth, rate limiting, billing, or developer portal.
- Graph logic duplicated across services — no versioning or contract
- Real-time resolution undefined at sub-100ms scale
- Highest commercial demand area had no product definition
Stack: Databricks · Unity Catalog · PySpark · Redis · Python · FastAPI · MCP
What We Built
Layered IDaaS Architecture
A versioned Graph Library with tested query recipes encoding validated traversal logic. Platform Management Layer — unified auth, rate limiting, telemetry, billing, developer portal — turning fragmented services into one consumable platform.
Real-Time Identity Resolution Design
Sub-100ms resolution architecture across hundreds of millions of device records. Low-latency serving store with edge materialisation strategy defined by versioned library recipes — not ad-hoc table queries. Confidence thresholds built into every contract.
AI-Agent Ready: MCP Interface
Platform scoped for Model Context Protocol integration — enabling AI agents and automated workflows to access identity services programmatically. Agent-driven onboarding and audience-building patterns designed in from the start.
Results
Sub-100ms
real-time ID resolution design
5 services
unified under one platform layer
MCP-ready
AI agent access architected in
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