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Computational & AI Strategy Review

A four-to-six-week review of where computational biology and AI can improve your programs and where the investment is unlikely to pay off.

Who this is for

You need to decide what computational capability to build, buy, or skip — whether that's hiring your first data scientist, evaluating an AI vendor, adopting a foundation model for target identification, or assessing whether a particular computational workflow is worth the investment.

This engagement is most useful when the question isn't just technical but strategic: you need to understand the realistic upside and realistic risk of different paths and potential alternatives, and you need to be able to explain that reasoning to outside parties.

This is also appropriate for investors who need an independent technical read on a portfolio company's computational claims or platform as part of due diligence.

What's included

  • Review of current data assets, workflows, and analytical gaps
  • Assessment of which AI and computational methods fit your data and goals, and which don't
  • Build / buy / partner recommendations, with vendor evaluation criteria and shortlist where applicable
  • Honest assessment of the systems and expertise already in place, compared with what your programs require
  • Hiring plan and role definitions if building in-house
  • Written recommendations document, structured for a board or investor presentation
  • Working session to present findings, discuss recommendations, and address questions or objections

How it runs

Four to six weeks, depending on scope and how many programs or data types are in scope.

Weeks 1–2 Scoping and discovery. Interviews with relevant team members. Review of existing data assets, vendor contracts, and any prior assessments. Definition of the key questions the review will address.
Weeks 2–4 Technical assessment. Evaluation of specific methods, tools, or vendors in scope. Where relevant, review of data quality, analytical reproducibility, and platform fit.
Weeks 4–6 Written recommendations. Working session with leadership to present findings, discuss tradeoffs, and vet the recommended path.

What you get

  • A written recommendations document structured for a board or investor audience
  • Build/buy/partner decision framework with specific vendor shortlist or hiring profile where applicable
  • Assessment of AI and computational methods relevant to your programs — with realistic success criteria
  • A working session with your leadership team
  • Reference materials and technical appendix (on request)

Price

From $20,000, depending on scope.

Single-program reviews will fall at the low end, while multi-program or diligence-grade work requiring deeper technical evaluation will be higher. Scope and price are agreed on before work begins.

Get in touch

An email and short call about the decisions you're facing is the right starting point. Together, we'll design a project scope that works for you.

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