Engineering, Product, and Support - all self-serve on codebase knowledge.
"ProdE is the only tool we've used that actually understands our codebase end to end. Engineers and PMs trust it, and it cuts through a lot of back and forth."
Company Profile
Leena AI is a YC-backed (S18), Series B company with $18M+ revenue, building enterprise virtual assistants. Team of ~93 across engineering, product, and support. Microservices architecture. Clients across healthcare, FMCG, technology, aviation, telecom, hospitality, retail, media, pharma, education, and more.
What Changed
| Before | After (client reported) |
|---|---|
| 2-3 days waiting for answers | 15-30 minutes self-service |
| Weeks to onboard new engineers | Days to get productive |
| Hours debugging across services | Minutes to trace root cause |
| PRDs based on assumptions | PRDs grounded in actual code |
Teams Enabled
Engineering: Cross-service debugging in minutes, not hours. Zero coordination meetings.
Product: Answer enterprise client questions same-day. PRDs grounded in real implementation.
Support: 5x faster production issue resolution. Less escalation.
New Hires: Contributing in days, not weeks. Self-serve onboarding.
1. Engineering: Cross-service debugging
The problem: Changing something that touches another team's service meant interrupting them, waiting for docs, hoping you don't break integrations. Debugging across services meant hours of log-diving and pinging multiple teams.
With ProdE: Explore downstream services independently. Trace issues across the stack in minutes. Zero coordination meetings.
Real examples:
- SMS delivery flows across 5 different services
- Voice agent orchestration with multiple integrations
- Authentication flows spanning frontend, gateway, and backend
- Production bugs involving frontend state, backend logic, and database inconsistencies
Impact: Zero coordination meetings for cross-service changes. Fewer integration breaks. 5x faster debugging.
2. Product: Client questions + PRDs
The problem: Enterprise clients send detailed technical questions during onboarding. Before ProdE, answering meant 2-3 days coordinating with multiple engineers. PRDs were written on assumptions, leading to multiple review cycles.
With ProdE: PMs answer client questions in the same session. PRDs grounded in actual implementation from day one.
Real examples:
- Enterprise bank client questions about user provisioning, onboarding, offboarding - answered in one session without engineering
- Detailed configuration explanations for notification systems
- Impact analysis for proposed feature changes across services
Impact: Deals don't lose momentum. More accurate scoping = fewer implementation surprises. Engineering stays focused on building.
"ProdE is essential for any high-growth product team. It helps us ship documentation in record time and provides high-level technical and UX insights that keep our team aligned."
3. New Engineers: Faster onboarding
The problem: Onboarding took weeks. Shadow seniors, ask constant questions, wait for people to have time to explain things.
With ProdE: Self-serve Q&A on any part of the system. New hires contribute in days, not weeks.
Real examples:
- Complete system architectures without senior engineer walkthroughs
- Database schema and data model relationships across services
- Authentication and authorization implementation patterns
- Deployment pipelines and CI/CD workflows
Impact: New hires contributing in days, not weeks. Senior engineers stay focused on their work instead of answering onboarding questions.
4. Support/On-Call: Faster issue resolution
The problem: Debugging across microservices meant hours of log-diving, pinging original authors, waiting for context across teams.
With ProdE: Trace issues across services in minutes. Less escalation. Clients back online faster.
Real examples:
- Integration bugs spanning frontend state, backend logic, and database inconsistencies
- Message delivery failures across notification services and third-party providers
- Authentication token issues between services
- Data inconsistencies between microservices caches
Impact: 5x faster issue resolution. Less escalation. Clients back online faster.
"It did a great work in documenting complete flow. Specifically it separated and compared generic and client-specific implementations so well that anyone without KT will be able to understand the complete approach."
How ProdE Works
ProdE indexes the entire codebase into a searchable knowledge base. It analyzes code at multiple levels - from individual functions to complete features - and maps dependencies across services. When someone asks a question, it searches through actual implementation and synthesizes answers grounded in real code.
- Builds a knowledge graph of code symbols and cross-service dependencies
- Maps API calls between microservices to create a dependency graph
- Generates technical summaries (for devs) and product summaries (for PMs) from the same codebase
- Updates as code changes, so answers stay current with production
The Results
| Metric | Result (3 most recent months snapshot) |
|---|---|
| Engineering Time Saved | 2450+ hours |
| Team Adoption | 68/93 active users (>70%), 3 job functions |
| Teams Enabled | Engineering, Product, Support |
| Annual Value (extrapolated) | >$780,000 @ $80/hr |
| Direct ROI | 10x+ |
Why It Stuck
1. It removed waiting, not work. People still confirm with teammates, still review code, still make decisions together. They just don't spend 2-3 days waiting to start.
2. Everyone got value immediately. Not a dev tool that PMs ignore. Not a PM tool that engineers resist. Cross-functional value on day one.
3. Answers grounded in their codebase. Not generic docs. Not ChatGPT guesses. Their actual architecture, their actual patterns, their actual constraints. The index updates daily, so it never goes stale.
The Multiplier Effect
- Deal cycles faster: PMs answered client questions in real-time instead of waiting days
- Feature velocity improved: Engineers stayed focused instead of constant context-switching
- Team scaled efficiently: New hires productive in days, not weeks
- Issues resolved before escalation: 5x faster debugging across services
Security: ProdE doesn't train any AI models on code. It creates an intelligent index that stays in infrastructure. Code never leaves the organization's control. On-prem deployment available.