Chatbots → RAG → agents → skills → self-learning systems in production. I've shipped at every layer of the GenAI stack as it evolved, across 80+ tools and every serious framework that's shown up. You don't need to test all of it. I already did.
Most people don't need more ideas about AI. They need someone who's already shipped the version that works. Here's how we get there.
I embed with your team and deliver a working system end-to-end. Discovery, build, launch, handover. You get the thing running, not a deck about the thing.
Build with me →My current obsession: turning a company's operations into a living command center — dashboards, workflows, and GenAI agents wired into a single nervous system that senses, decides, and acts. Strategy becomes execution because the org itself is the execution layer.
In progress · case studies soonBring the tool, the workflow, or the roadmap you're unsure about. I'll tell you what I'd pick, what I'd skip, and what I've already watched fail — based on what I've actually run.
Work with me →1:1 mentorship for founders and operators figuring out where AI actually fits in their business. Also available for keynotes, workshops, and off-the-record rooms of leaders trying to make sense of the curve.
Book a session →Strategy only matters when it turns into execution. And execution is where real companies actually break — defining the real problem, getting customers, building the product, managing money, managing people, running operations.
The biggest reason teams don't win isn't a shortage of strategy. It's the gap between strategy and what actually happens on a Monday morning.
That gap grows with every layer a company adds: handoffs, misinterpretations, delayed decisions, lost context. The plan stays perfect in the deck and messy in reality.
Executional Intelligence is a holistic way of solving core business problems with the best suitable technology — whether that's GenAI, ML, a dashboard, a workflow, an automation, or just a basic form with the right owner behind it.
Plenty of execution problems don't need AI at all. Some do. The job is knowing which is which — and then wiring the whole thing together so the company itself becomes the operating system.
GenAI is what makes it tractable now. But the goal isn't AI. The goal is execution that doesn't leak.
I run companies. I ship AI into operations that have payroll, customers, and things that break at 2 a.m. Every opinion on this page has survived that. If I haven't tested it, I won't tell you to buy it.
100+ AI systems shipped across eight industries. I've been the one answering for it when it breaks at 2 a.m. — not the person with the slide deck the morning after.
Every tool on this site, I opened an account for, wired into a real workflow, and pushed until it broke. What you see here is a map of what I actually sat with — not a roundup pulled off Twitter.
The gap where most engagements die — between "great plan" and "running in production" — is the only part I'm actually interested in.
The field didn't arrive all at once — it mutated. Chatbots became retrieval systems, became tool-using agents, became skill-composable runtimes, became production systems with memory that learn from their own history. I've built on every one of those layers as it came out of the oven.
Raw model work: prompt engineering, context control, embeddings, RAG. Learned where LLMs lie, where they snap, and where they're a cheat code.
When coding itself became a GenAI surface. Shipped full products in days with pair-programming agents and generative IDEs.
Multi-agent graphs, tool use, planning, delegation. Built working agent systems on every serious framework — and learned which ones survive a real workload.
The shift from monolithic agents to skill-libraries and open protocols. Wired real systems across MCP and ACP so agents plug into the rest of the stack like services.
Workflow engines doing real work: inbound, outbound, ops, approvals, handoffs. Not demos — the boring stuff that moves revenue.
Systems that remember what happened yesterday and adjust today. Long-horizon memory, evaluator loops, retrieval over their own traces. This is the layer most teams aren't ready for — and the one I spend most of my time in.
Every tool below is something I've actually run in a real project — not a link from a newsletter. Grouped by what they're for, not by who's hottest this week.
Recognised as an AI Trailblazer by AI Kiran, an initiative by Verix and INK Women, supported by the Office of the Principal Scientific Adviser to the Government of India.
Things I keep posting about and rebuilding for different teams. I'm turning the best of them into shippable products and plug-and-play workflows you can run yourself. Drop by the Substack to get them when they go live.
An opinionated template for wiring dashboards, workflows, and GenAI agents into a single operating surface for any SMB or startup.
A lightweight system for founders to close the gap between what gets decided in a room and what actually ships. Notion + automations + agents.
Production-tested n8n and LangGraph flows for sales, ops, content, and research — the ones I keep rebuilding from scratch for different clients.
Notes, teardowns, and playbooks from inside live AI builds — the parts that don't make it into Twitter threads. No fluff, no recycled news.
30 minutes, founder-to-founder. Bring the problem, the tool you're unsure about, or the team you're trying to unstick — I'll tell you what I'd do in your seat, based on what I've already run.
Schedule a call →