Privado, a developer-first data privacy platform trusted by HP, Peloton, and ZoomInfo, needed to ship enterprise SSO to unblock stalled customer deals. The feature spanned four repositories with different technology stacks and required deep familiarity with authentication, billing, and user management systems. Bito’s AI Architect compressed a planned 7-to-10-day effort into 5 hours of working time, with code changes spanning 24 files across all four repositories.
5 hours
Total time to ship: PRD + implementation
7 to 10 days
Original estimated timeline
4,872 lines
Code added across 24 files
1,850 lines
Production ready PRD generated in 1 hour
23
AI Architect MCP calls across 4 repos
4
Repositories spanned across different tech stacks
The Challenge
Cross-service features are among the slowest categories of engineering work because developers spend most of their time reading unfamiliar code and waiting for meetings with system owners. Privado hit exactly this wall.
Enterprise customers required SAML and OIDC support before adopting the platform, and without it, deals were stalling. The feature touched four repositories spanning authentication, billing, user management, and the MCP server, each with different tech stacks. The developer on the task had strong experience with the management APIs but had worked less extensively in the authentication and billing codebases, both written in Java while his primary language was Go. The original estimate was 7 to 10 days.
“SSO was blocking enterprise deals. We needed it shipped fast, but the engineer assigned to it didn’t have deep experience with our auth or billing systems. Normally that means a week of ramp up before any code gets written.”
AI Architect had already built a knowledge graph of all four repositories. Connected via MCP to an agentic coding platform, it provided full cross-repo system context from the first prompt.
Cross repo understanding in minutes: 11 AI Architect calls surfaced auth patterns, validation endpoints, and billing APIs across all four services without reading a single file.
Principal engineer level PRD in 1 hour: 1,850-line spec covering SSO enforcement modes, token precedence, failover handling, config schema, and a phased migration plan.
Implementation grounded in codebase patterns: 12 additional MCP calls gave the agent controller patterns and schemas to write code following existing conventions.
Shipped in 5 hours: PRD, tech spec, ERD, implementation plan, and working code across 24 files (4,872 lines added), deployed end to end.
“When I reviewed the PRD, I was genuinely surprised. It correctly mapped our existing authentication flow, identified the right integration points in billing, and addressed edge cases like break glass access and failover, things I would have expected only from someone who had spent a lot of time in those codebases.”
5 hours to ship a feature estimated at 7-to-10 days: 23 MCP calls replaced the codebase reading and system owner meetings that consume most of a crossservice project’s timeline.
1,850-line PRD approved with minimal revision: Covered SSO enforcement modes, token precedence, failover handling, and a phased migration plan. The CTO found no gaps in authentication flow or billing integration points.
4,872 lines across 24 files, zero senior engineers blocked: The full implementation shipped without pulling anyone off their own work. The knowledge graph replaced hallway conversations and Slack threads.
Standout Result
14x faster execution, from an estimated 7-to-10 days to 5 hours: AI Architect gave the coding agent cross repository understanding that would normally require months of tenure. The resulting PRD correctly mapped authentication flows, identified billing integration points, and addressed edge cases the CTO expected only from deeply embedded engineers.
“What used to require pulling senior engineers off their work to walk someone through the system is now handled by AI Architect. The developer got the context he needed without blocking anyone else on the team.”
Every enterprise engineering team recognizes this pattern: a feature that spans multiple services, assigned to a developer who does not have deep familiarity with all of them. The typical result is knowledge bottlenecks, extended timelines, and senior engineers pulled off their own work to answer questions. AI Architect eliminates this by giving developers and their coding agents system level understanding through a knowledge graph that captures how services connect, which APIs exist, and how to extend them correctly.
About Privado
Privado (privado.ai) is a developer first data privacy platform that automates compliance with GDPR, CCPA, and other data privacy regulations. The platform scans codebases for data flows, builds real time data maps, and eliminates privacy risk for enterprise teams. Backed by $17.5M in funding from Insight Partners and Peak XV Partners, Privado counts HP, Peloton, and ZoomInfo among its customers. The company has 130 employees and continues to expand its engineering team.
