Business Value Through Technology
The model is rarely the problem. It's everything around it: how your team is structured, how you test and deploy, how you operate at scale, and how you sustain it past day one. That's where we work.
Start a conversationWho we are
We help you decide what to build, what to kill, and what to test first, before you waste time on the wrong thing.
We don't hand you a roadmap and leave. We're in the weeds with you: architecture, deployment, day-2 ops, the hard tradeoffs.
We'll push back on your assumptions, stress-test your approach, and tell you when something won't work. That's what a real partner does.
Not POC specialists. We've built, shipped, and operated AI systems at scale, and we stay until yours runs the same way.
How we work
We assess the real problem: root cause, dependencies, team readiness, buy vs. build. Then you get an honest go/no-go with a clear path forward. Billed time & material, capped. You know the ceiling before we start.
We embed alongside your team or own the full build with our own engineers. Full velocity to ship, then we stay at whatever pace your business needs, through the next initiative and the one after that.
Experience
Logistics, insurance, financial services. Turning unstructured documents into accurate, automated workflows. 30%+ accuracy gains and 45% cost reduction in production.
From healthcare efficiency to customer support and post-call analytics. End-to-end agent design, deployment, and operationalization.
Helping organizations identify where AI creates real value, sequence initiatives, and build a roadmap that actually gets executed.
Legacy code and infrastructure transformation on AWS, from assessment through delivery, including GenAI-assisted migration tooling.
Real-time fraud detection systems for payments and fintech. Built and deployed in production.
Connecting fragmented data sources into a coherent, queryable foundation. It's the prerequisite most AI projects skip, then regret.
Reference architecture and spec-driven approach for teams building AI products. How you build matters as much as what you build.
Who's behind this
I've spent 13+ years in ML and AI. I started in 2012 building predictive analytics products for retailers as part of a startup that was later acquired. I grew from engineer to leading the data and ML team, building and operating production systems at scale.
I spent years at AWS as a Solutions Architect, working alongside enterprise customers on their AI and cloud initiatives. I've seen what works, what fails, and what the market sells that rarely delivers.
I've built and operated my own AI product from concept to market, including the mistakes that come with scaling too early. I advise from that experience, not from the sidelines.
I started JibeTack because the gap between strategy and production is where most AI initiatives die, and I've spent 13 years on both sides of it.
LinkedIn →Let's talk
Whether you have a clear vision or just a problem worth solving, we'll have an honest conversation about what makes sense, and whether we're the right fit.
Start a conversation