Technology
AI and ML models, robotics systems, computer vision pipelines, data infrastructure, scalability and defensibility.
Advisory | Investors, acquirers & strategy teams
AI & robotics due diligence.
Assess the technical substance, deployment readiness and commercial assumptions behind an opportunity.
Assessment areas
What needs to be true?
Commercial context
Product-market assumptions, competitive positioning, go-to-market plans and the route from research to revenue.
Team & research
Technical capabilities, execution dependencies, research quality and the evidence behind claims. Review patent portfolios for technical context.
Decision brief
A concise assessment of evidence, material risks, open questions and recommended validation steps relevant to your decision.
Typical outputs
Evidence you can use in the decision.
Technical assessment
Capabilities, deployment constraints, evidence gaps and technical risks.
Management questions
A focused set of questions and validation activities for the company and its technical team.
Decision brief
Findings connected to commercial assumptions and the next steps in your assessment.
Technical coverage
AI, robotics and their real-world use.
- AI & machine learning
- Robotics & automation
- Computer vision & perception
- Healthcare technology
- Industrial automation
- Autonomous systems
- Human-robot interaction
- Deep tech hardware & software
Scope the assessment around your decision.
Agree the depth, access to evidence, timetable and deliverables before work begins. Technical review of IP does not replace a legal opinion; specialist reviews can be defined as part of the broader process.
For venture investors
Match the diligence to the company's stage.
Early stage
Understand the technical thesis, the quality of the evidence and the work needed to reach the next milestone.
- Reproducible demo and evaluation method
- Data access, dependencies and IP provenance
- Team capability and critical hiring gaps
- Validation plan and commercial assumptions
Scale-up stage
Assess repeatable delivery, deployment economics and the constraints that emerge as the business grows.
- Performance across real customer environments
- Integration effort and customer-specific work
- Support, reliability and operating costs
- Technical roadmap and scaling bottlenecks
Use the checklist to define scope and evidence requests. Online answers stay in your browser. A public checklist is not an assessment of a specific company.
Put the context to work
What do you need to understand before committing?
Share the opportunity, decision timeline and the technical questions you want to resolve.