v9LabsEngineering AI for serious work.
We develop AI for the demanding tasks in your core business. We start with one clearly scoped process, connect domain knowledge, data and systems, and build from there.
How AI takes root in your company.
Most companies start AI in one of two ways. Wide: one tool for everyone that changes little. Or scattered: pilots and concepts in five departments that never connect. Both use up budget and patience before anything takes hold.Most companies start AI wide, with one tool for everyone, or scattered, with pilots that never connect. Both use up budget before anything takes hold.
We engineer AI into your systems, your data and the way your teams actually work. One process first, built so the next use case grows out of it.We engineer AI into your systems and data. One process first, built so the next use case grows out of it.
You talk to the buildersWe're two founders. We plan the first process with you and build it ourselves.
Built to growThe data, access rights and tests of the first tool carry the next one.
Spread by your teamYour people build along and ask for the next step themselves.
Customers and partners
Before v9LabsThe founders have already shipped production systems here.
Customers and partners
Before v9LabsThe founders have already shipped production systems here.
For companies that want more from AI than a chatbot: AI built into your knowledge, systems and teams, where it pays off.
Not sure which one fits?
Three projects: the problem, the build and where it stands.
Employees have personal questions about benefits, taxes and leave. The answer depends on their own contract and on data in SAP.
Douglas led technical delivery in a three-person team, built with HR on one real use case in short loops. Answers draw on each contract and live SAP data.
10,000+ employees use it. Business units now build their own assistants on it, and it runs HR processes across several systems.
A small SAP team, a large body of custom ABAP code and no automated tests. AI help meant copying code into a browser chat and back.
An agent connected to their SAP development system. Tests prove it writes only inside a fenced package and cannot release transports. Every action is logged.
Began with an on-site workshop. We supplied the technical input for the works agreement. Now the whole team works real tickets with the agent.
Chat, model access and SharePoint search were in place. The question that mattered: which of our references fits this tender?
A knowledge layer where every fact points to its source document, page and version. CVs are anonymized before any model sees them, as agreed with the data protection officer.
Began with a workshop. Now rolling out for proposal preparation. The company’s existing AI tools reach the same knowledge through one MCP server.
What we commit to on every engagement.
01
We don’t stop
Others stop at the strategy, the demo or the handover. We cover architecture, infrastructure, deployment and operations. We stop when your people use it every day.We cover architecture, infrastructure, deployment and operations. We stop when your people use it every day.
02
No spec needed
Hand us a rough problem. Get back a working tool. No management overhead either. Your team stays in the loop, sees what’s happening and learns enough not to depend on us.Hand us a rough problem. Get back a working tool.
03
Quick wins that compound
Start with a focused sprint that shows value early. Each project builds the infrastructure and trust for something more ambitious. The second tool starts where the first one left off.Start with a focused sprint that shows value early.
No commitment. You talk to the people who build.
Tell us what you’re dealing with. We’ll help you think through where AI fits and what a realistic first step looks like. You leave with a clearer plan, whether or not we end up building it.Tell us what you’re dealing with. Together we’ll look at where AI can help.