I’m Randy Lariar, and I work at Balance Theory, focused on building practical, trustworthy AI products at the intersection of AI, data, and security. I help teams turn AI from “interesting” into useful—shaping strategy, translating messy real-world needs into product, and shipping systems that are safe, usable, and resilient.
- Building AI capabilities that fit how people actually work—from discovery to prototypes to production, with governance and usability baked in.
- Partnering across engineering, product, and stakeholders to reduce risk while increasing adoption (security, privacy, evaluation, and responsible deployment).
- Exploring patterns for personalized, workflow-aware intelligence that stays aligned with user trust and organizational constraints.
- Experimenting across the AI/product stack: Python, Next.js, FastAPI, Azure, Databricks, OpenAI APIs.
- Developing reliable delivery loops for AI products: evaluation, monitoring, iteration, and operational rigor (DevOps + AIOps-minded).
- Writing and thinking publicly on lariar.com.
I enjoy teaming up on:
- AI tools or workflows that improve real-world business processes—especially where trust, governance, or security matter.
- Open-source demos and reference pipelines that show how to go from MVP → measurable value → durable adoption.
- Community-powered learning—working groups, doc collections, or workshops that make applied AI more approachable.
- How organizations and individuals can be part of the small group that gets AI right (not just fast).
- Blending “build fast” AND “build right.”
- Making trust practical: explainability, guardrails, evaluation, and clear UX around uncertainty.
- Packaging AI so it accelerates outcomes—not just showcases tech.