Services Overview
From strategy to implementation to operations — we help applied ML teams close the gap between where they are and where their AI investments need to take them.
01 of 04
"We have a mandate to leverage AI — we just don't know how to get there."
We build the plan. Starting from your organization's goals, we cut through competing priorities, internal noise, and vendor hype to define a principled AI roadmap — one your leadership can actually get behind.
Identifying where ML creates real, measurable value — and where it won't, so you don't waste resources chasing the wrong things.
A practical, sequenced plan that translates corporate goals into technical priorities, with a case leadership can act on.
An honest assessment of where your team, data, and infrastructure stand today — and what needs to be true before the next step is worth taking.
Clear-eyed recommendations on when to build custom AI solutions and when proven commercial tools are the smarter path.
02 of 04
"We tried it with one engineer and Claude. It's clearly too hard."
We do the actual build. From early-stage research through production-ready systems, we turn research-grade ideas into scalable tools your scientists and engineers will actually adopt.
Rigorous, reproducible research designed to move from hypothesis to validated finding — with the scientific discipline real-world problems demand.
Computer vision, robotic systems, custom models — architected to scale beyond the proof of concept and survive contact with reality.
Tools only get adopted when they fit how scientists actually work. We design for the end user, not just the technical spec.
Oversubscribed internal teams get a senior partner, not another bottleneck. We move fast and hand off clean.
03 of 04
"Everyone knows we need to do this. No one has budget for it."
The unglamorous work that makes everything else possible. We clean up data infrastructure, establish reproducibility, reduce technical debt, and make your team faster at doing high-quality ML work.
Getting the data warehouse clean and setting up systems that make results verifiable, repeatable, and trustworthy.
Making existing tools easier to work with — so your team spends less time fighting infrastructure and more time doing research.
Evaluating internal teams and workflows to find where high-quality work is getting slowed down, and what it would take to unblock it.
High-quality ML research requires optimizations not widely understood in Pharma and biotech. We close that gap.
04 of 04
"I don't even know what kind of ML person we need."
We help organizations hire and develop the right ML talent. After years of building and evaluating teams across research and industry, we can see what you need — and what a resume won't tell you.
Rigorous, technical assessment distinguishing researchers from practitioners, and academic skill from production readiness.
Before you can hire well, you need legibility into what you actually need. We help define roles in terms of the work, not just the title.
Training and upskilling the team you already have — so they can maintain and extend what Hop Labs builds, without creating dependency.
Building internal ML capability that lasts — including the culture, processes, and standards that retain good people.
Let's Work Together
Get in touch
hello@hoplabs.com