Move old platforms to new ones, with AI doing the repetitive work.
AI takes on the repetitive part of platform migrations and model delivery, and engineers stay in control of every change.
- 01
ETL migration with AI
Move old data pipelines to a modern stack, with AI doing the repetitive rewrites.
- 02
MLOps platform
Ship models to production on one shared backbone.
AI in production, story by story.
Pick one to see the result, the client behind it and the full story.
Use Case · ETL Migration with AI300Talend jobs processedAn enterprise client cuts ETL migration time by an estimated 30%An AI-assisted migration tool to move large-scale Talend pipelines into a modern Python and Spark stackUndisclosed ClientFull success story 
Use Case · MLOps Platform-75%time-to-market decreased byOne MLOps platform cuts time to market by around 75%A unified MLOps platform to streamline data workflows and accelerate time-to-marketUndisclosed ClientFull success story 
Questions
Talk to usDo our engineers review what AI changes?
Yes. AI does the repetitive rewrites, and your engineers review and approve every result before it ships.
What happens after go-live?
The team that builds your solution runs it. We monitor quality and adoption, fix what drifts and improve the solution as your business changes.
Where does our data live?
You decide. We deploy in our cloud, in your own cloud account or on your servers, and your data stays inside that perimeter under your rules.
Do we need an AI strategy before we start?
No. If you already have a use case with an owner, workable data and internal agreement, we can build it. If not, the AI Assessment finds and ranks the opportunities first.
Can you work with our own data and IT teams?
Yes. We adapt to your stack and work side by side with your engineers. We can also plan the move from a DareData team to your own team over time.
Which AI models do you use?
We are not tied to one provider. GenOS works with models such as Claude, GPT, Gemini, Mistral and Llama, and we pick per task for accuracy, cost and where the data may go.
Who owns what we build together?
Ownership is agreed in the contract before work starts. Ask us for our standard terms on the first call.
How is a project priced?
It depends on scope, length and the team it needs. After a first conversation we send a proposal fitted to your case, not a price list.
How do you keep our data secure?
Every solution runs on GenOS, with guardrails, access rules and a log of each answer, action and model call. Your security and risk owners review the design before anything goes live.
How soon do we see results?
The AI Assessment takes two working weeks and ends with a ranked plan. Most of our builds reach production in two to six months, depending on scope and data.
Other teams
Bring us your hardest AI problem.
Large enterprises trust us to carry AI all the way into production. We shape the use case, build it into your systems and run it with the governance your business needs.
- 01Use cases chosen for value, not hype
- 02Systems built into your stack and data
- 03Run, monitored and improved after go-live