I build AI systems.
I've been at this long enough to know where most of them fail.
I'm Rupreet Gujral - an AI and Systems Architect with 25 years spanning enterprise tech, global consulting, and the builder trenches. I've founded startups, shipped patents, and worked inside organisations large enough to know why most AI projects stall. Today I architect agentic systems and LLM infrastructure - and I write about what I learn, without the hype.
Not a ladder. A loop.
"Most careers in tech are a ladder. Mine has been a loop - and it's given me something a straight line never could."
Corporate to startup, practitioner to strategist, founder to architect - and back again. 25 years across enterprise tech, consulting, and building my own ventures has given me a vantage point that's hard to get from one track alone.
I've sat in rooms where AI projects die slow deaths - not because the technology failed, but because the architecture was wrong from day one. I've also been on the other side - founding startups, shipping under pressure, learning what actually works when the demo is over and the budget is real.
Today I architect agentic systems: multi-agent pipelines, LLM infrastructure, cost governance, observability. I write about what I'm building and learning - in real time, from first principles, not from a vendor whitepaper.
From the blog
Practitioner notes on agentic systems, LLM infrastructure, and what I learn building AI in the real world - not the demo version.
Neuro-Symbolic AI: When the LLM Doesn't Get the Final Say
What it is, why it matters, and a working example you can run in five minutes. 1. What it actually is Neuro-symbolic AI is just two things working together, with a clear line between them. * The neural part. This is your LLM, or any model that reads messy human input, like a sentence, a request, a photo, and turns it into something structured. It's good at understanding language. It is not good at guarantees. * The symbolic part. This is plain old code: if-this-then-that rules, written b
Everyone's Using AI. Nobody's Measuring Whether It's Working.
Everyone has AI now. The engineering team has code assistants. The support team has chatbots. The sales team has AI-powered CRM workflows. The marketing team has content generators. Every team lead, every VP, every board deck has a slide that says "AI-integrated" with a green checkmark next to it. And almost nobody, genuinely, almost nobody, is measuring whether any of it is actually making a difference. I don't mean measuring adoption. Teams are great at measuring adoption. "85% of our devel
Production AI Is an Engineering Discipline, Not a Demo
Over 25 years in enterprise tech, and more intensely in the last couple of years building AI products and advising teams on agentic systems, I've watched the same pattern repeat itself so many times that I can predict it almost word for word. Someone gets pressure from the top to "do something with AI." The conversation starts with the wrong question: which model should we use, GPT or Claude? A model gets picked, a few features get bolted on, it gets tested against a clean, predictable dataset,
Where I go deep
Agentic Systems
Multi-agent orchestration, supervisor patterns, memory systems, tool routing. Designing autonomous loops that do real work - not demos.
LLM Infrastructure & Cost Governance
Semantic routing, SLM/LLM hybrid stacks, observability pipelines, token cost reduction. Making AI deployable at scale without the LLM Tax eating your margins.
AI Product Engineering
Spec-driven development, eval frameworks, RAG pipelines, production deployment patterns. The full system - not just the model layer.
Enterprise AI Adoption
Architecture reviews, build-vs-buy frameworks, AI governance, team structure. The decisions that determine whether an AI investment succeeds or stalls.
Tools of the trade
What I'm Thinking About
Working through what "memory" actually means for a long-running agent. Episodic? Semantic? Neither pattern from human cognition maps cleanly.
Enterprise teams are spending 4–6× what they should on inference. The answer isn't a cheaper model - it's a smarter router.
How do you debug an agent that's three hops deep in a tool-use loop? The tracing primitives don't exist yet.
Pick my brain
I do a limited number of 1:1 sessions - on AI architecture, building defensible AI products, technical strategy for non-tech founders, and career decisions in tech. 25 years of context, no slides, no fluff.
Fractional CTO & Tech Advisor
Expert tech review for non-tech founders. Save dev cost before you spend it.
Build an AI Moat, Not a Wrapper
Turn your thin wrapper into a defensible AI asset.
Career Mentorship
Career clarity through honest conversation. 25 years of pattern-matching.