Case notes
What I've actually built and shipped.
Selected proof points, from semiconductor inspection and enterprise computer vision to the agentic systems and audio experiments running today.
Guardian Agents: Founder (Dec 2025-present)
Context. Founded December 2025: a Physical AI / AgeTech product for healthcare operations.
What I did. Founder. Set the product direction across operational visibility and intelligence, quality control, and auditable risk mitigation. I am building it 0-to-1.
Outcome. Early-stage and building. This is the same founder-stage product work I bring to fractional engagements, done firsthand and in parallel.
Applied Materials AERA: closing the capability gap that mattered
Context. AERA was Applied Materials' next-generation aerial-image mask-inspection platform. Competitive research exposed a strategic gap against KLA: phase-shifting chrome-and-glass masks were effectively transparent to our existing inspection path. Separately, polarization control was a prerequisite for Intel acceptance.
What I owned. I carried both capability projects from market and customer evidence through feasibility, management approval, budget, optics, mechanics and opto-mechanics planning, subcontractor selection and coordination, manufacturing, QA, and acceptance. Both workstreams relied on specialist partners in electro-optics, micro-electronic glass etching, and delicate, high-accuracy mechanics.
The hard part. The off-the-shelf wave plates sold as zero order did not behave correctly across AERA's bundle of optical angles. I developed a new method to measure effective retardance, thickness, and polarization purity, escalated the evidence to Thorlabs' CTO, and gave the supplier a repeatable way to produce and verify true zero-order parts.
Outcome. The phase-contrast demonstrator and plug-and-play linear and circular polarization module were delivered on time, on budget, and to specification under an aggressive timeline. Intel accepted the polarization capability and purchased two AERA systems, opening a strategic adoption path for the platform.
BriefCam: Director of Product (2018-2022)
Context. An enterprise video-analytics company running an AI/ML R&D organization of 30+ PhD algorithm developers building computer-vision and deep-learning algorithms.
- Took the license-plate-recognition (LPR) feature from concept to ship, including a camera-agnostic specification and a fuzzy, Levenshtein-distance plate search built for real-world cases like Amber Alerts. Exact-match search misses partial, misread, or transposed plates; fuzzy search finds the vehicle an operator is actually looking for.
- Re-architected the video-analytics pipeline from centralized to distributed processing, deciding per algorithm what ran on the camera versus the central server, including a two-stage face pipeline (detection at the edge, recognition in the core).
- Instituted a QA methodology for AI features: algorithm owners define their own pass/fail criteria and test plans before handoff, because they're the only ones who know where a model's envelope actually breaks.
- Authored the hardware-sizing methodology (GPU/CPU benchmarking, storage and chassis right-sizing) that became the sales engineering standard.
- Led the product organization: three senior PMs, two UX designers, and a technical writer.
Outcome. Enterprise deployments scaled from roughly 40 to 1,200+ cameras per installation, alongside a ~30% reduction in hardware package costs that kept upgrade pricing flat as the algorithms grew more demanding.
Mindful Designs Haven: Founder & Creative Director (Sep 2023-present)
Context. A generative-AI print-on-demand studio.
What I did. End-to-end, solo: AI image-generation for product design, multi-platform storefronts (Amazon Merch by Amazon, Etsy, Redbubble, WooCommerce/Elementor, Shopify), and performance marketing across Meta, Google, and Amazon ads.
Outcome. A live, AI-native commerce operation I've run continuously since 2023: the practical, hands-on counterpart to the enterprise AI work above.
The agentic workspace: personal practice (ongoing)
Context. My own production environment: not a client project, but the infrastructure I use to run everything else.
What I did. Designed, built, and operate a full-stack agentic AI system: 12+ autonomous agents coordinated across research, outreach, business development, product operations, analytics, CRM, and DevOps. Underneath it: a graph-backed knowledge layer, multi-model LLM orchestration across providers, and an access-control architecture where each agent only sees the data its task requires.
Outcome. I'm the sole operator. When an orchestration failure or a data-flow fault happens, I debug it myself, in production. That's the operating discipline behind the consulting work.
Adaptive noise masking - live web experiment
Context. A browser experiment that listens to ambient sound locally, then shapes white or pink noise around the room's spectrum.
What I did. Framed the product question, built the interactive web proof, kept microphone analysis on-device, and made the core comparison testable on a phone.
Try it. The experiment is public now. Headphones are optional; the most useful comparison is in the room you want to mask.