Caroobi
Enterprise
Automotive
300,000+ customer cases
Automated across the CRM behind a €3bn aftermarket division
1,000+ workshops
- A 56 person team runs the platform on a single account
- The model scaled well past the small pilot it started as
- 300,000+Customer cases handled by AI we built
- 1,000+Workshops running on it
- 40 to 6People after we replaced two consultancies
We find where AI pays off in your business, then take it to production.
Plenty of companies are exploring AI. The harder part is knowing where it creates real business value and getting those ideas live. We do both, on the systems and the data you already have.
Trusted by industrial and enterprise organisations across Europe
What AI actually changed for them
150m+
Parts ordered
300+
Workshops integrated
“GoTeams helped us identify and leverage AI throughout our existing workflows. During the process we also discovered an opportunity for an AI first product, which is now live and growing. Very happy with the work of GoTeams.”
Stephan Otto
Co-Founder & Managing Director at Helloparts
10
Countries live
€1m+ ARR
In under 12 months
“With the support of GoTeams, we were able to build one of Germany's leading cybersecurity platforms within just a few years. Today, we protect the German Mittelstand – thanks to GoTeams' outstanding work. Here's to a continued great partnership.”
Julius Gerhard
Co-Founder & Managing Director at Trustspace
Tell us where you think AI could help.
Three reasons AI stays stuck
AI is a priority and nothing is live
It is in the strategy, it is in the board pack and it has been on the roadmap for a year. Nobody disagrees that it matters. There is still nothing running that a customer or a colleague touches.
Your best people spend the week on repetitive work
A hundred recruiters process CVs, screen candidates, match them, write outreach, summarise interviews and build reports. The same shape shows up in claims, in onboarding and in back office finance.
The pilot worked and then stopped
The proof of concept did what it promised in the demo. Then it met real data, a legacy system and a security review, and there was nobody whose job was to get it through all three.
From the first workflow we look at, to the system your business runs on
The same team finds the opportunity, proves it on your data and takes the ones worth having into production.
Find the opportunities
We start with your workflows, not with the technology
We look at where the volume and the repetition actually sit, size what automating them is worth and put the candidates in order. You leave with a scored shortlist and the business case behind each one, including the ones we tell you not to build.
- Opportunity assessment across the workflows you already run
- Each use case scored on business value and on feasibility
- A written case for the two or three worth funding first
Prove it before you fund it
A proof of concept on your data, with a real go or no go
We build the shortlisted use case against your own data and your own edge cases, because that is where most AI ideas quietly fail. You get something you can try and a straight answer about whether it is worth building properly.
- Built on your data rather than on a clean sample
- Evaluated against how the workflow behaves on a bad day
- A recommendation you can take to the people holding the budget
Take it to production
Into your systems, through your security review, and kept running
This is the part that stops most AI projects. We integrate with the systems you have, including the old ones, we bring data protection and compliance in from the first design decision, and we stay with it once it is carrying real volume.
- Built for your existing systems and your fragmented data
- Security, data protection and compliance designed in, not bolted on
- The team that built it stays with it as the volume grows
Find out which of your use cases would survive production.
We are not an AI strategy consultancy
We do not hand over a roadmap and leave the execution to your team. GoTeams has been building software for six to seven years, led by founders who have been building together for fifteen, and the same group that finds the opportunity is the group that ships it. Our architects have taken AI systems into production, which is a different job from having an opinion about them.
Assessment to production, one team
Opportunity assessment, proof of concept, build, integration and production. No handover in the middle where the momentum dies.
Architects who have shipped AI
The people designing your system have taken AI into production before, on real business workflows carrying real volume.
We work with what you have
Legacy systems and fragmented data are the normal starting point. We do not ask you to fix your data estate first.
The business case comes first
We build around what the work is worth, not around what the technology can do. Some use cases we tell you to drop.
Product and AI engineering together
AI that people actually use needs an interface, a workflow and a rollout. We bring the product side with the models.
German speaking, based in Europe
We are a German company. We already work with organisations across Europe and know how DACH procurement and compliance work.
AI we already run in production
Zennify
Enterprise
Consulting
40 people to 6
Replacing an Accenture and Infosys setup
Higher output
- Six specialists delivered more than the setup they replaced
- Speed went up while the cost of the arrangement came down
helloparts
B2B
Automotive parts
Order entry runs itself
AI document processing, live for workshops across Germany
0 wrong parts in 183 pages
- Purchase orders arrive as PDFs, scans and photos and come out catalogue valid
- Anything the system is unsure about reaches a person before it ships
How an AI initiative reaches production
Four stages. Each one ends with something you can decide on.
- 01
Opportunity assessment
We map the workflows, find where AI would change the economics and score the candidates on value and feasibility.
You get a scored shortlist with the business case behind each use case.
- 02
Proof of concept
We build the leading candidate against your real data and evaluate it on the cases that usually break these systems.
You get a working proof of concept and a go or no go you can defend.
- 03
Build and integrate
We build it properly and connect it to the systems your people already work in, including the legacy ones.
You get the system live inside the tools your team already uses.
- 04
Production and scale
We harden it, monitor it and keep the same team on it as the volume and the number of use cases grow.
You get a supported system in production and the team that keeps it running.
Ready to look at your first use case?
What people ask us first
We do not have a defined AI project yet. Is it too early to talk?
No. That is usually where we start. Most of the value in the first conversation is working out where AI would actually be worth something in your business, which is the part that is hard to do from the inside. You do not need a use case picked, a budget approved or a business case written.
Our data is messy and our systems are old. Do we have to fix that first?
No. Legacy systems and fragmented data are the normal starting point, and we design for them rather than around them. If a use case genuinely depends on data you do not have in a usable state, we will tell you that during the assessment instead of after you have funded it.
How do we know an idea is worth building before we commit?
We validate it first. The proof of concept runs on your own data and is evaluated against the awkward cases, not a clean sample. You get a go or no go recommendation, and we have told clients to drop use cases at this point. That is cheaper for both of us than finding out in production.
Are you going to hand us a strategy and leave?
No. We are not an AI strategy consultancy. We can take an initiative from opportunity assessment through proof of concept, development, integration and into production, with the same team throughout. The reason the handover matters is that it is exactly where most AI programmes stop.
What about security, data protection and GDPR?
They are part of the first design decisions rather than a review at the end. We are a German company working with organisations across Europe and the DACH region, and we understand how procurement, compliance and technology approval actually work here.
We already have an AI team. Where do you fit?
Usually on the parts that are hard to staff for or hard to finish. Some clients bring us in for the opportunity assessment and keep the build in house. Others have a team that is fully committed and needs a use case taken to production alongside them. We can also add AI engineers directly to your team if that is the actual gap.
Will the team speak German?
Yes. We provide German speaking teams for DACH organisations, and they work in your timezone. Assessments and workshops run in German or English, whichever suits the people in the room.
How long before something is actually live?
The assessment is a matter of weeks and the proof of concept follows it. How long production takes depends on what the use case touches, and the honest answer is that integrating with your existing systems usually takes longer than building the AI. We give you that estimate before you commit to the build, not after.
What happens after I book an assessment?
You get thirty minutes with someone who has taken AI systems into production. We look at where AI could make sense in your business, tell you which of those we would look at first and show you a comparable system we already run. You get a written summary either way.
Tell us where you think AI could help.
A short call to look at where AI would be worth something in your business, and to tell you which use case we would start with.
In German or English, whichever suits your team.