Services
Focused engineering services for medical imaging, computer vision, spatial AI, and technical diligence.
AI / CV Technical Due Diligence
For: Investors, acquisition teams, and internal product owners
Evaluate AI and computer vision technology, architecture, and deployment readiness.
Engagement type: Assessment or advisory retainer
Typical start: 1–2 week turnaround from documentation review
Typical duration: 1–3 week assessment or ongoing advisory capacity
Best for:
- Investment or M&A teams reviewing AI assets
- Product owners validating technical claims
- Teams preparing acquisitions or strategic partnerships
Typical problems:
- Unverified AI performance claims
- Unclear architecture or data lineage
- Unknown production readiness
Typical outcomes:
- Technical due diligence report
- Architecture and risk assessment
- Practical recommendations for next steps
How engagements start:
Review available technical documentation and run a short assessment.
Not a fit:
- Strategic market research
- Full software implementation
- High-level business planning without technical work
Example:
Assessed computer vision readiness for a product acquisition.
Contact for details →
Medical Imaging / Computer Vision R&D
For: Biotech and medical-AI teams
Work on imaging pipelines, model development, and validation for diagnostic and vision systems.
Engagement type: Contract, advisory, or retained R&D
Typical start: After technical scope and dataset review
Typical duration: Monthly reserved capacity or project-based engagements
Best for:
- Medical imaging or vision teams with complex data
- Research programs that require NDA-sensitive handling
- Projects needing technical leadership on prototype development
Typical problems:
- Noisy or inconsistent imaging inputs
- Limited labeled data for model training
- Unclear path from research to deployment
Typical outcomes:
- Validated prototype model and technical review
- Roadmap for product integration
- Risk profile for model behavior and performance
How engagements start:
Begin with a technical scope review and dataset assessment.
Not a fit:
- Basic data-entry automation
- Prebuilt model selection without research work
- Pure marketing or design-only work
Example:
Defined a vision pipeline for a medical imaging workflow.
Contact for details →
Research Engineering Sprint
For: Product teams and innovation labs
Short-term engineering sprints to test feasibility, validate algorithms, and clarify technical risk.
Engagement type: Focused sprint or rapid assessment
Typical start: Within 1–2 weeks of initial planning session
Typical duration: 1–4 week sprints depending on scope
Best for:
- Teams that need rapid technical proof points
- Research concepts with uncertain feasibility
- Projects that need a focused, senior engineer-led sprint
Typical problems:
- Ambiguous product requirements
- Unproven model or sensor approach
- High technical risk in early design
Typical outcomes:
- Working proof-of-concept code
- Measurable validation criteria
- Clear next-step recommendations
How engagements start:
Start with a one-day technical planning session.
Not a fit:
- Long-term feature development
- Pure UX/product design engagements
- Commodity IT work
Example:
Delivered a validated prototype in a focused research sprint.
Contact for details →
Examples
Explore applied research examples in the case studies and reach out via contact for confidential details.