I've owned ByteNite's product end-to-end: from the first customer interview to production, through a full market pivot, and across a cross-functional team. My product practice is grounded in direct user research, rigorous prioritization, and a bias toward shipping and measuring over speculating.
I owned every phase of ByteNite's product from day one. Discovery started with structured interviews with AI developers and infrastructure engineers to identify where existing tools fell short. I translated those insights into technical specifications, scoped them into sprint-ready tickets, shipped iteratively, and tracked outcomes against real signals from users.
Visit ByteNiteIn 2023, customer feedback and market signals made it clear our video encoding SaaS was hitting a ceiling: a narrow segment, high competition, and limited willingness to pay. Rather than optimizing a product that wasn't working, I ran a structured discovery sprint and identified a broader, underserved need: general-purpose serverless compute for AI and data workloads. I built the case for the pivot off of customer insight calls, restructured the roadmap, and led the transition from a vertical SaaS to a horizontal Platform as a Service. The pivot expanded our addressable market and directly shaped our successful Techstars application.
Maintained a living product roadmap fed by three inputs: direct customer feedback, competitive market analysis, and infrastructure capacity constraints. Ran structured 6-week planning cycles using effort/impact scoring to surface the highest-leverage tickets. Tracked the developer platform landscape actively, including positioning against Modal, RunPod, and emerging AI compute providers, to keep our differentiation sharp. Every major roadmap decision was backed by a written rationale tied to specific user problems and business outcomes.
Redesigned the developer onboarding path by translating complex container orchestration into JSON-based templates, intuitive step-by-step guides, and clonable repositories. The result: a 60% reduction in onboarding time as measured by time-to-first-successful-deploy with our users. The insight behind it: when competition is harsh, get a user to working prototype as fast as possible to lock in their initial awe and turn it into commitment.
View Onboarding GuidesAs product owner in a small, high-velocity team, I coordinated engineering, go-to-market, and QA from a single source of truth: the product spec. I wrote requirements that engineers could build, defined quality criteria that QA could test, and shaped the value narrative the GTM team could sell. I ran weekly reviews and facilitated retrospectives, maintaining shared context across a distributed team. When technical trade-offs created friction, I anchored resolution to product goals rather than individual preferences.
Built and maintained Metabase dashboards over production data to surface hidden system inefficiencies and real usage patterns. Wrote SQL-based analyses that revealed bottlenecks invisible to the engineering team's intuition, then drove the prioritization decisions that led to a 50% increase in computing throughput. Good product analytics is not a reporting function: it's how you find the decisions worth making.
View QA DiscussionBuilt comprehensive cloud cost-forecast models by synthesizing multi-vendor pricing data across AWS and GCP. Implemented standardized provisioning rules and spending guardrails that reduced compute expenses by 30% without degrading performance SLAs. Treating infrastructure cost as a product constraint rather than a pure engineering concern enabled sustainable unit economics ahead of our next fundraise.