Services

I work with funded biotech, medical-AI, and frontier-AI teams when the problem requires both research judgment and implementation ownership.

The strongest fit is a technical problem where failure is expensive: unreliable imaging accuracy, ambiguous R&D, weak feasibility evidence, poor spatial alignment, or AI claims that need independent review.

Medical Imaging / Computer Vision R&D

Primary focus

For: Biotech and medical-AI teams

Senior technical support for imaging-heavy products involving segmentation, registration, calibration, spatial alignment, microscopy, pathology, or precision image-processing workflows.

Typical duration: 1–3 week sprint or monthly retained R&D

Engagement type: Sprint, advisory, or retained R&D

Typical problems

  • Medical image segmentation or post-processing is unreliable
  • Scans, microscopy images, or spatial data need alignment or calibration
  • Research code needs to become a working technical pipeline
  • AI outputs must connect to physical or lab workflows

Typical outcomes

  • Clear technical risk assessment
  • Prototype or production-oriented imaging pipeline
  • Improved spatial alignment, calibration, or workflow reliability
  • Practical roadmap for implementation or validation

Relevant proof

Medical-imaging AI work involving segmentation, scan interpretation, geometric calibration, spatial alignment, precision output generation, lab validation, and patent filings.

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Research Engineering Sprint

For: AI startups and R&D teams

Focused research-to-product execution for teams that need a prototype, paper implementation, benchmark, feasibility answer, or architecture path through an ambiguous technical problem.

Typical duration: Usually scoped in 1–3 week blocks

Engagement type: Fixed-scope sprint or senior hourly execution

Typical problems

  • A promising AI method needs to be tested quickly
  • A prototype is blocked by research uncertainty
  • A paper or open-source method needs adaptation to real data
  • The team needs senior technical judgment before committing engineering budget

Typical outcomes

  • Feasibility assessment
  • Working prototype or benchmark
  • Technical constraints and failure modes
  • Recommended implementation path

Relevant proof

Experience turning ambiguous AI ideas into working prototypes across Meta Reality Labs, QuantumCyte, and AGAT Software.

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AI / CV Technical Due Diligence

For: Founders, investors, and technical leaders

Independent assessment of whether an AI, computer-vision, medical-imaging, or spatial-AI claim is technically real, scalable, defensible, and execution-ready.

Typical duration: Lightweight review, diligence memo, or deeper technical assessment

Engagement type: Advisory, diligence, or technical review

Typical problems

  • A vendor, startup, or internal team claims strong AI/CV performance
  • Technical risk is unclear before investment or product commitment
  • Model, data, architecture, or deployment claims need expert review
  • Leadership needs a clear go/no-go technical opinion

Typical outcomes

  • Clear technical risk map
  • Feasibility and scalability assessment
  • Architecture and data-quality review
  • Practical recommendations for next steps

Relevant proof

Senior AI R&D background across computer vision, spatial AI, medical imaging, LLM systems, and production-oriented AI workflows.

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How engagements usually start

  1. Technical assessment: Clarify the problem, data, constraints, risk, and likely implementation path.

  2. Sprint or technical plan: Build a prototype, benchmark, feasibility study, or architecture plan.

  3. Retainer or advisory relationship: Continue with reserved senior R&D capacity, implementation ownership, or technical review.

Not a fit

If the problem is difficult, technical, and expensive to get wrong, send a concise technical brief or book a technical-fit call from the contact page.

Available for selected contract, advisory, and retained R&D engagements.

Request a technical assessment