Applied AI, doing real work
That means AI running inside your business, on your own data, giving your organization the leverage it needs in our AI future. We scope one workflow, build it into your systems, and run it in production with a small team alongside yours. Which workflow would you start with?
Two decades guiding companies through technology change. Today, that means AI.
How we work with you
Scope
Two or three weeks to work out whether a workflow is worth automating. We look at how often it runs, what it costs you today, whether we can get at the data, and who would own it. You get a written recommendation and a price. Sometimes the recommendation is not to build it.
Build
A team of two or three builds the system into your stack and runs it in your own cloud. We measure quality and cost from the first week, so by the time it is in production you know what it does and what it costs to run.
Meet the teamRun
Once it is live we keep it working as models, data and policies change, report results monthly, and train your people to own it. When it is steady, we scope the next workflow.
Who we work with
We work with organizations that hold a lot of data and do a lot of expensive manual work on it
01
Firms built on their own data
Search firms, funds, research centers and professional services firms whose records are the business.
02
Teams doing research and document work by hand
Candidate research, clinical extraction, contract review, recurring reports. Work that takes senior time and could run at scale.
03
PE operating partners and their portfolio companies
Where one working deployment at one company becomes the template for the next.
What comes with the team
The tools we bring with us
We built these on our own product, TalentJet, and on our own research. They belong to us, not to a previous client, and they mean the first weeks of a build go to your problem rather than to plumbing.
Research agents
Our own fork of the OpenClaw framework, tuned for research on people, companies and documents: finding sources, pulling out facts, and checking them against each other.
Verification and provenance
Every fact traced back to where it came from. When the system is not sure, it says so rather than guessing.
Measurement
Quality and cost measured at every step, so you can see what it would cost to run the whole dataset rather than a sample, and which steps actually need the expensive model.
Selected clients
Trusted with production systems, not pilots
New Haven, Connecticut — home to Yale University.
Selected work
Applied AI in production
Systems we have built and run for clients, some of them for years.
TalentJet: AI Interview Intelligence
Result: recruiters spend 70% less time on candidate assessment.
Technology Stack
Capabilities
Riviera Partners: Production ML in Executive Search
Result: Several years in production at one of the world's top executive search firms.
Technology Stack
Capabilities
Executive Search Platform
Purpose-built ATS with data science at its core for executive search.
Technology Stack
Capabilities
The people who do the work
A small team, working inside your business
A principal who owns the outcome and one or two senior engineers who build. The same people from the first conversation to hand-over, working in your channels, your repos and your cloud.
The same people from scoping to run
No hand-off from the people who advised you to a different team that builds. The engineers you meet in scoping are the ones on the build.
Built in your cloud, on your data
The system runs where your data already lives, integrated with the tools your team uses. Nothing has to move out to make it work.
Yours to own when we leave
You own the result. We keep the tools we bring, and they get better with every engagement, which is why the second build is faster than the first.