AI Strategy Consulting
An honest, technically grounded assessment of where AI can — and can’t — help your team, before you commit budget to a build.
TYPICAL CLIENT
A founder or R&D leader deciding whether AI is worth building at all
Usually a founder, CTO or VP of R&D without an in-house ML function, weighing whether to invest in AI and needing a technically credible, domain-aware answer before committing budget or headcount.
Six concrete deliverables, not a slide deck.
This is exactly what transfers to you at the end of this engagement.
Feasibility assessment report
A clear, written verdict on whether your data and goals actually support the AI approach being considered.
Data readiness audit
An honest inventory of what your current data can and can’t support, and what would need to change.
Build vs. buy vs. partner recommendation
A specific recommendation on whether to build in-house, buy a vendor solution, or partner — with reasoning.
Prioritized roadmap with cost & effort estimates
A sequenced plan of what to do first, second and later, scoped against realistic effort and cost.
Risk & regulatory considerations memo
The regulatory, data-privacy and operational risks specific to your use case, flagged before you build.
Executive-ready presentation deck
A deck built to present the findings and recommendation to your board, investors or leadership team.
What this service includes
Feasibility & data readiness assessment
A grounded evaluation of whether your data and goals can actually support the AI approach you have in mind.
Build vs buy analysis
A clear-eyed comparison of building in-house against buying or partnering, with total cost and risk factored in.
Roadmap & prioritization
A sequenced, realistic plan for what to invest in first and why.
Executive workshops & advisory
Working sessions with your leadership team to pressure-test the plan before it’s finalized.
What this looks like in a real engagement
Three concrete examples of the kind of work this service covers.
Feasibility study before an in-house diagnostics model
An assessment of whether existing lab data could support a proposed diagnostic classifier, before any engineering budget was committed.
Build-vs-buy recommendation against a vendor platform
A comparison of building a custom model in-house against licensing an off-the-shelf vendor tool, with total cost of ownership modeled out.
12-month AI roadmap presented ahead of a funding round
A prioritized roadmap and risk memo prepared for a biotech board, used directly in investor conversations.
Why generic “just add AI” advice backfires in life sciences.
Most AI strategy advice is written for marketing and clickstream data, where the cost of a wrong bet is a wasted sprint. In life sciences, a wrong AI bet can mean months of engineering time spent on a model your data was never going to support.
We give the assessment we’d want if we were spending our own budget — including telling you clearly when the answer is that you’re not ready yet, and what would need to change first.
How an engagement runs
Four stages, from initial scope to delivery.
Understand current state
We review your data, goals and existing team capacity before forming any opinion.
Assess feasibility & gaps
Data readiness and technical feasibility are evaluated honestly against the proposed use case.
Build the roadmap
A build-vs-buy recommendation and prioritized roadmap are drafted with real cost and effort estimates.
Present to stakeholders
Findings are presented in a working session with your leadership team, board or investors.
How we work
Half of good AI strategy is telling a client honestly that they’re not ready yet.
Before you reach out
Both, if you want. Many clients move directly from the roadmap into an Applied AI engagement with us, but the assessment stands on its own if you’d rather build elsewhere.
Most feasibility and roadmap engagements run two to four weeks, depending on how much data review and stakeholder input is involved.
Then that’s exactly what we’ll tell you, along with what would need to change — in data, team or scope — for that to become the right call later.
Explore related work
Not sure if AI is the right investment yet?
Send us your data and your goal — we’ll give you an honest read before you spend a single engineering sprint on it.