We learn how your business works before we build anything. Then we build the data science and stay until it's a tool your people use, not a proof of concept that dies in a folder.
Vercertus is founder-led by design. The three of us scope, build, and hand over every engagement ourselves: data scientists with master's degrees from Virginia Tech, working across defense, higher education, and business advisory. Discovery is free because the first thing you should evaluate is our thinking on your problem.
Three stages, one continuous engagement. Most consultancies stop at the model; we're built for the part after.
We start in your world, not your data. A free session with your leadership to find the problem worth solving, then a short engagement of about two weeks mapping exactly how data science changes the process.
What we do →Forecasting, segmentation, anomaly detection, LLM systems: the right technique for the problem, never the trendiest one. If the data doesn't exist yet, we help you start collecting it.
How we work →A model isn't a solution until someone uses it. We turn the work into tools that fit how your team already operates, then train your people and hand over.
See the work →We run every engagement on CRISP-DM, the Cross-Industry Standard Process for Data Mining, the default framework for data science projects since 1999. Following it closely is how a project reaches production.
Success gets defined in your terms before we touch a dataset.
ii.What exists, what's missing, what can we trust. If there's none yet, we help you collect it.
iii.The unglamorous seventy percent, done properly so everything after holds.
iv.A forecast, a clustering, an anomaly detector, an LLM system: whatever the problem calls for.
v.Measured against the business goal from step one, not just an accuracy score.
vi.Delivered as something your team uses, with training and a real handover.
Graduate and applied projects, each taken from raw data to a working result. We'll walk you through any of them in detail.
Topic modeling and LLM analysis across customer-agent conversations, built into an end-to-end view of how every agent performs.
Autoencoder models that let satellites not built for the job detect space weather anomalies.
Forecasting component failures and scheduling maintenance before downtime hits, instead of after.
We don't arrive with a template. Every engagement starts with discovery, and what we build is shaped around how your business runs.
Automation should enhance your people, not replace them. Overstretched teams want back the hours they lose to work a machine should be doing.
If a result won't change a decision or a process, it isn't worth building. We measure ourselves against what changes in your business.
Tell us what's slowing you down. The first session is free.