Solutions
What we help scientific teams solve
Three problems keep surfacing across scientific operations: deciding what's worth automating, building systems that actually hold up, and getting a team fluent enough to run them without us.
Scientific AI Transformation
The problem
Most teams know something in their workflow is worth automating — they just can’t say which step, why it’s actually slow, or whether AI is even the right lever.
What we do about it
We map the workflow as it really runs, quantify where time and quality are actually being lost, and tell you honestly where AI helps and where it won’t — before anyone commits to building anything.
Solved by Assessment →AI Architecture Implementation
The problem
Even once you know what to automate, most attempts stall as prototypes that never survive contact with a real, regulated workflow — because they were designed like software demos, not like a scientific operation.
What we do about it
We design and build systems around how your team actually works — automating the repeatable parts while keeping scientific judgment and sign-off exactly where they belong. What ships is something your team runs, not a demo.
Solved by Build →AI Enablement & Adoption
The problem
AI tools are cheap to buy and expensive to actually use well — most teams end up with a subscription nobody opened twice.
What we do about it
We set up the tools, train your team against your own data, and hand over the documentation so the capability stays with your team — not locked up with us.
Solved by Enablement →How we typically engage
Three stages, not three products
Most engagements move through these in order — Assessment tells you what’s worth doing, Build does it, Enablement makes sure your team can run it without us. You don’t need to start at the top; if you already know what to build, we can start there.
Assessment
Find out which processes are worth automating, which to do first, and how much it would actually save.
2 weeks, mostly remote
Includes
- Process audit report — current-state mapping, automation candidates flagged, ROI estimate per candidate
- Implementation roadmap — priority order, timeline, resourcing needs
- One 60-minute readout with your leadership team
Build
We build the working AI automation system — from requirements to a system your team actually runs.
2–12 weeks depending on scope
Includes
- Design document — architecture, what’s automated vs. kept human, data flow
- A working system — connected to your data, with AI drafting, checking, sorting, matching and more
- Team training (1–2 sessions) + an operating manual and troubleshooting guide
- 2 weeks of free bug-fix support after delivery
Enablement
Get your team actually using AI — from setup to training — so they can run it themselves.
1–2 weeks, plus optional monthly support
Includes
- Setup — access to the AI tools, connections to your systems, security and permissions
- Team training — an intro session plus a hands-on workshop using your own data
- Usage docs — quickstart guide, templates, best practices
- Optional monthly support
Who does what
AI doesn’t replace scientific judgment
Every engagement starts by drawing this line explicitly, so your team knows exactly where AI is doing the work and where a person always stays in charge.
Purely human
Strategic decisions, scientific judgment, risk ownership, partner and regulator relationships.
Human-led, AI-assisted
Experiment design, scientific interpretation, data review, technical writing.
AI-led, human-reviewed
Report drafts, meeting prep, document summarization, knowledge retrieval.
Fully automated
Notifications, task creation, file archiving, status reporting.
Not sure where to start?
Discuss a workflow