Cloud security + startup product + applied AI
I build secure cloud infrastructure at Microsoft, lead product engineering at Battr, and help business owners make better software and AI decisions through BAM Innovations. Brayden legally; Bam everywhere else.
Story
Some of my earliest tech memories are watching my dad on a beige Windows 95/98-era PC, probably with Novell and GroupWise somewhere in the mix. Years later I ended up at Microsoft, which is a pretty funny family plot twist if you remember Novell and Microsoft were not exactly sending each other holiday cards back then. My own version started with the Nintendo 64, then Game Boys, jailbroken iPhones, online game hacks, project cars, and small experiments that started with “I wonder if this can be changed.” That instinct carried through the University of Utah, Microsoft, Battr, and the consulting work I do now.
Current role
At Microsoft, I work on Azure Core Security Services, with a focus on encryption and security infrastructure that customers rely on for sensitive workloads.
It has made me pretty allergic to hand-wavy engineering. If a system is important, someone has to own it, run it, debug it, and make it boring enough to trust.
More Microsoft work
Azure Confidential Ledger
Created a Merkle-tree validation tool that supported confidence in Azure Confidential Ledger as it moved toward general availability.
Microsoft Garage
Built the Unity and VR portion of a Garage project that explored how attack graphs could be visualized and demonstrated in AR/VR.
Startup work
Battr is a real estate accountability platform for teams whose databases are full of leads that drift, stall, or get ignored. It checks the database daily, finds standards gaps, and helps move opportunities to agents who are ready to work them.
I lead the technical side: product architecture, full-stack code, CI/CD, preview environments, and the AI-assisted dev workflow that keeps a tiny team moving.
Work with me
BAM Innovations is where I do consulting outside Microsoft and Battr. The usual question is: should we fund this app, fix it, rebuild it, or use AI somewhere else entirely? I usually start small on purpose, so you get a plain-English read before spending serious money.
For trades, sales, service businesses, and other non-software teams that want to know where AI can help without making work weirder.
Read service pageSoftware product assessmentFor prototypes, internal tools, inherited builds, and AI-coded apps that need an honest read before more money goes in.
Read service page