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Go from prototype to production without the 3-month rebuild

Let agents handle monitoring, orchestration, and business translation.

3mo→3dTo production
AutoDrift detection
ZeroGlue code

Trusted by data users and teams at

Airbus
BBC
Canal+
Canary
Cox Communications
Deel
Dun & Bradstreet
Airbus
BBC
Canal+
Canary
Cox Communications
Deel
Dun & Bradstreet
Lineage
Kerno
ITV
IBM
HPE
Flye Fit
Dunn Humby
Lineage
Kerno
ITV
IBM
HPE
Flye Fit
Dunn Humby
Linesight
NASA
QVC
Sky
S&P Global
Sun King
Tesco
Linesight
NASA
QVC
Sky
S&P Global
Sun King
Tesco

The model works. Getting it into production? That's a different story.

1

Experiments that take weeks instead of hours to iterate

2

Production handoffs that stall for months

3

No visibility into when models drift

4

Business teams that can't interpret your outputs

5

Weeks of engineering to deploy something simple

Zerve bridges the gap between experimentation and production — without adding engineering overhead.

Prototype to production, no rewrite required

Notebook to structured workflow to repeatable run.

Deploy in days, not quarters

Agents that watch your models

Detect data shift, threshold crossed, performance drop.

Catch drift before it costs you

Model outputs the business can actually use

Impact, risk, change from prior version.

Bridge the gap between science and strategy

Less glue, more modeling

Agents handle the orchestration layer.

Spend time on models, not plumbing
User Voices

Why data scientists choose Zerve

Real stories from teams building with Zerve every day

Zerve feels different because it was built for data scientists from the ground up. It understands context, adapts as you work, and lets you iterate without crashes or lost work.

Eduardo Ordax

Gen AI Lead at AWS

Zerve finally feels like the cursor for data scientists. It understands that schemas live in your files, not in your head.

Lior Alexander

Founder/CEO at AlphaSignal

I gave it a churn analysis task and it pulled data, handled missing values, ran segmentation, and scheduled the workflow automatically using context from my past work.

Eric Vyacheslav

AI Engineer at Stealth

We were working with a 4.3 billion-row dataset, and a high-end notebook instance could only handle 18 million-rows at a time without going out of memory due to the nature of preprocessing to be done on the data. That meant running the same call 24 times in a row. With Zerve, we could run all 24 in parallel. That shift alone saved us a huge amount of time and let us move faster on model development.

Arindam Bose

Head of Data Science at Sun King

The Zerve agent has transformed our data science workflow. Backlogs are vanishing and we're experimenting faster than ever.

Mike Smith

Founder/CEO at Ascendr.org

As we advance our AI and Data standardization initiatives, Zerve provides the robust foundation we need to scale with confidence and be a force multiplier for meaningful outcomes.

Richard Springer

Director of Data at Cubic

The Cursor for Data Science is here.

Sandipan Bhaumik

Data & AI Lead at DataBricks

Zerve represents a step-change for data science development.

Rob Hickey

Former EVP of Engineering at DataRobot

Zerve's Agentic code assistance is easily among the best I've seen

Conor Nolan

Technologist at HPE

I used Zerve AI, and it honestly felt like a cheat code for data science.

I use other AI when the answer already exists and I need to surface it. I use Zerve when the answer doesn't yet exist so I need to build it.

Warren Paull

Applied AI Consultant at Commercial Outcomes

How It Works
1
Experiment
2
Operationalize
3
Monitor

Ship models, not glue code.

See how Zerve helps data scientists operationalize faster.

Decision-grade data work

Explore, analyze and deploy your first project in minutes
Zerve for Data Scientists | Prototype to Production in Days