Inference architecture
Model routing, prompt/runtime constraints, and fallback paths tuned for latency and reliability.
Parallax Beacon
Architecture and execution systems for serious AI products.
I help founders and technical leaders move from exploratory builds to dependable AI systems with strong runtime behavior, clear ownership, and measurable delivery.
Austin-based, remote-first, engagements across US time zones.
What I Build
Four practical build surfaces where architecture decisions directly affect launch quality and operating cost.
Evaluation harnesses
Task-grounded quality checks with clear pass/fail criteria before features reach production.
Delivery pipelines
Release workflows that connect experimentation, telemetry, and rollback into one operating rhythm.
Team operating systems
Decision logs, ownership models, and runbooks that prevent architecture drift as teams scale.
Who I Work With
Teams that need execution clarity, not theory decks.
Founder-led companies
- Shipping v1 with constrained engineering bandwidth
- Need architecture choices that survive customer growth
- Want direct principal-level partnership
Product and engineering leaders
- Consolidating fragmented AI experiments
- Balancing roadmap speed with quality controls
- Aligning teams around measurable system outcomes
Investment and diligence partners
- Technical diligence on AI feasibility and delivery risk
- Architecture reviews before major build commitments
- Independent perspective for high-stakes decisions
Engagement Model
Three ways to engage based on urgency, scope, and team maturity.
01 | Architecture Sprint
2 weeks
Focused system diagnosis, target-state architecture, and an execution brief your team can implement immediately.
02 | Build Partner
6 to 10 weeks
Embedded support for design decisions, milestone reviews, and delivery stabilization during critical launches.
03 | Fractional Principal
Ongoing advisory
Recurring architecture oversight, evaluation governance, and leadership coaching for in-house teams.
Outcomes
The engagement is successful when these are true.
- Roadmap decisions are tied to explicit model/system constraints.
- Quality gates are defined before launch, not after incidents.
- Telemetry and evaluation connect directly to business metrics.
- Team ownership and escalation paths are documented and operational.
- Launches become predictable instead of hero-driven.