Approach
SCOPE: how we transform a scientific workflow
Five stages, one workflow at a time: from watching how your team works today to a redesigned workflow they actually use.
Study
We start by observing your workflow exactly as it runs today — who’s involved, which systems hold the data, how approvals move, and what the team is actually trying to achieve. No template process imposed from outside.
Characterize
We quantify the bottlenecks: hours spent, where delays creep in, quality risk, repetitive work, and where institutional knowledge lives in one person’s head instead of a system.
Optimize
We redesign the workflow assuming AI exists from day one. This isn’t bolting AI onto your current process — it’s asking what the process should look like if you were building it today, simplifying first and automating second.
Prototype
We build fast, validate with the people who’ll actually use it doing real work, and iterate before anything is called done.
Embed
We train the team, track whether the new workflow is actually being adopted (not just installed), and hand over a documented workflow your team can run and extend.
Deployment model
You control where this runs
Your data stays with your team by default. Biotech’s compliance bar calls for it, and it’s the fastest way to a yes from your QA and IT teams.
Default
In your environment
Runs inside your own cloud or on-prem environment. Data never leaves your systems. Fastest to trust, lowest compliance overhead — the right fit for regulated biotech teams.
On request
Managed cloud
We host and maintain it; your team uses it through the web. Less for your IT team to run, with updates handled for you.
For large rollouts
Hybrid
Coordination runs in our hosted service; the steps that touch sensitive data run in your environment. Built for enterprise-scale adoption.
See how this plays out on a real engagement.
View case studies