AI R&D · Computer Vision · Model Lifecycle Delivery

I fine-tune, validate, and ship Vision AI.

I bring Vision AI from R&D to partner deployments—shaping validation, guiding multinational partner testing, and translating real-world behavior into the next engineering cycle.

Keith Ponce
01

Executive Intent to Model Targets

Translate executive product intent into model behavior, latency targets, validation scenarios, and release criteria.

02

Partner Validation in Real Environments

Work with multinational partner engineering and data teams to evaluate our systems on their hardware, data, and deployment conditions.

03

Field Behavior to Next Iteration

Turn production findings into engineering feedback that improves the next AI release cycle.

Current focus

Practical QA for AI systems in the real world.

I combine six years of QA experience with a growing computer vision skillset to find product risks at the earliest stages of development.

  • Inference Analytics APIs
  • Modular Vision Architecture
  • Metabase Model Reporting
  • n8n Train-to-Publish Pipelines

Experience

Senior QA work across Vision Analytics, model testing, CI pipelines, and product delivery.

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Blog

Field notes on AI quality, model evaluation, Vision systems, and the delivery habits that make releases sharper.

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