AI systems that survive production.
RAG · Agents · Multi-tenant AI · APIs · Integrations
I'm interested in moving beyond demos and building systems that can operate in real workflows.
Technical Program Manager
Paul V Sebastian
Build. Measure. Learn. Repeat. ↻
Technical Program Manager building AI-native products and platforms.
I help teams close the gap between promising POCs and reliable production systems — from discovery and system design through integration, go-live, adoption, and scale.
8 years · Healthcare · Open Commerce · AEC · IoT
The Core Idea
Good systems are built in loops.
Most complex systems don't succeed because someone designed everything perfectly upfront. They improve through a tighter loop:
01
Build
Create something useful.
02
Measure
Understand what actually happens.
03
Learn
Find the gap between assumptions and reality.
04
Repeat ↻
Use what you learned to improve the next version.
The goal isn't just to ship. It's to continuously reduce the gap between what we think will work and what actually works.
This is the core philosophy behind how I approach AI systems, products, and delivery.
What I Focus On
What actually matters.
Quality · Reliability · Latency · Cost · Adoption · Delivery
If you can't observe it, you can't reliably improve it.
What reality teaches us.
Evaluations · Observability · User feedback · Failure analysis · Retrospectives
The most useful insights often appear after the system meets real users and real constraints.
Improve the system — and the way it gets built.
Iteration · Workflow redesign · Process improvement · Scaling · Automation
Selected Work
Evidence, Not Just Projects.
Multi-Tenant AI Assistant Builder
A self-service platform that enables organizations to create, test, and deploy their own AI assistants.
Build
- Multi-tenant architecture
- Tenant-isolated retrieval
- Knowledge base management
- RAG-based grounded responses
- In-app testing
- Controlled publishing
Python · OpenAI · Pinecone · Supabase · Langfuse
Measure
AI systems need evidence before they earn trust. The platform explores:
Evaluation gates · Response quality · Groundedness · Reliability · Latency · Cost
Learn
The hardest problems aren't just model problems. They emerge around:
Data boundaries · Retrieval · Evaluation · Security · Observability
Repeat ↻
Use evaluation and production signals to improve:
Knowledge → Retrieval → Response → Evaluation → Improvement
Agentic PMO
An AI-native workspace designed to augment Project and Delivery Managers — making project knowledge easier to access and act on.
Build
- Understand project context
- Search project artefacts
- Answer delivery questions
- Generate status summaries
- Surface risks and dependencies
Measure
The important question isn't "Can it answer questions?" — it's "Does it improve the way delivery teams work?"
Time saved · Information retrieval quality · Reporting effort · Response usefulness · Adoption
Learn
AI is most useful when it reduces cognitive and coordination overhead, not simply when it generates text.
Repeat ↻
Use real project interactions to improve:
Context → Answer → Feedback → Better Context
From POC to Production
AI Transformation & Delivery Framework
Build
Start with a real problem: Discover → Scope → Design → Build
Avoid starting with "Where can we use AI?" Start with "What problem is worth solving?"
Measure
Define success before scaling.
Business Impact · Quality · Reliability · Cost · Adoption
Learn
Find where assumptions break:
Data quality · Workflow fit · User behaviour · Model limitations · Integration constraints
Repeat ↻
Pilot → Evaluate → Improve → Scale
Enterprise Platform Delivery
Building systems at scale — B2B platforms across Healthcare, Open Commerce, AEC, and IoT.
Build
Owned delivery across:
Discovery → Requirements → Integration → Go-Live → Adoption → Scale
Measure
At scale, delivery isn't just about tracking timelines. You need visibility into:
System Health · Quality · Adoption · Dependencies · Risk
Learn
The hardest problems are rarely isolated technical problems. They sit between:
People × Product × Technology × Process
Repeat ↻
Improve both the product and the system used to deliver it.
How I Work
Closing the Gap Between Assumptions and Reality
01
Build
What's the smallest useful system we can create?
↓
02
Measure
What signals tell us whether it actually works?
↓
03
Learn
What did reality teach us?
↓
04
Repeat ↻
What should change next?
BUILD BETTER. Then repeat.
Current Focus
What I'm Exploring Now
AI Systems
RAG · Agentic Workflows · Evaluations · LLMOps · Observability
AI Reliability
Quality · Groundedness · Reliability · Latency · Cost
AI-Native Organizations
How AI changes the way teams: Think → Decide → Act
Continuous Learning Loops
How tighter Build → Measure → Learn loops change the speed and quality of delivery.
About
Hi, I'm Paul.
I'm a Technical Program Manager with 8 years of experience building, shipping, and scaling B2B platforms — across Healthcare, Open Commerce, AEC, and IoT, owning work from discovery through integration, go-live, adoption, and scale.
Today, I'm focused on the next shift: how do we build AI-native products and organizations that continuously learn from reality?
Build something useful. Measure what happens. Learn from reality. Repeat. ↻