Our AI automation handles recurring steps across your systems: classify requests, transfer data and prepare replies. We define what starts the workflow and what it delivers. Your team handles exceptions and approves important actions.
Discuss your workflowOne possible workflow: a customer request arrives. AI identifies the enquiry, the system adds customer details and prepares a reply. If the delivery date is missing, the responsible person takes over.
Match incoming requests. The request starts the workflow. The system adds the relevant customer details.
Interpret the message. AI identifies the enquiry. Fixed rules determine the next steps.
Hand over exceptions. Missing details go to the responsible person. Sending waits for approval.
I did not want a tool that dictates how I work. Today the system maps my workflow, and the AI moves within it inside clear limits.
Built in the project
Enrico BaronettiIndependent regional manager (HGB)Postbank Finanzberatung AGQuotes translated from German.Today I can describe what the site should do myself, and it gets built. Before, I would have needed someone every single time and paid a lot of money.
Built in the project
Niruchan ThanabalasingamFounder & Managing DirectorMatheCodeQuotes translated from German.Customer records, documents and internal work: start where your team spends time finding or transferring information.
Your systems work together. Fixed rules handle defined steps. AI helps classify and prepare information. Your team reviews the exceptions.
Four places to start in your business.
Bring customer information together automatically.
Find and reconcile duplicate customer records.
Use an AI knowledge base for recurring questions.
Connect tasks across your existing systems.
First the workflow. Then the connections.
Then less manual work.
01
We define the trigger, steps and result. Existing cases show how often it runs, how long it takes and common follow-up questions. That gives us a basis to compare which manual steps disappear and where your team still needs to intervene.
02
Which application supplies the data, and which receives the result? We check possible connections and define where AI interprets text. Rules control the remaining steps. Missing details and approvals have a named owner.
Keep the software that already does its job.
03
We connect the applications and build the required AI steps. We test the complete workflow, including duplicate requests, missing data and interrupted connections. Your team checks the results against typical cases.
Get one bounded workflow working before adding more.
04
We launch the workflow and show your team how to identify unfinished cases and errors. Together we compare handling time and manual steps against the starting point. Ownership and ongoing support are agreed before handover.
A fixed price for each phase, agreed before it starts.
The effort depends on your systems, data and required checks. We define the first workflow and quote before you commit.
30 minutes
Tell us which task keeps repeating. We assess whether automation makes sense and which foundations it needs.
Discuss your workflowAI automation combines defined process steps with tasks that use AI to interpret context. A system might classify a request, add information and prepare a draft. Rules determine what happens automatically and when your team reviews the result.
Recurring workflows with a clear trigger and result are a useful starting point. For example, a request arrives by email. The system classifies it, adds customer details and prepares a draft for review. An AI chatbot can also collect the required details in a conversation. Frequency, data access and exceptions determine whether the work is worthwhile.
Fixed rules suit predictable steps, such as transferring an approved record. AI can interpret inconsistent text or documents. The workflow around that AI step can still be predefined. An agent is useful when intermediate results should guide its choice of next steps and tools. Having several steps does not itself require an agent.
Not necessarily. We assess which connections your existing systems support and add missing capabilities where needed. If access or data is unavailable, preparing it becomes part of the project scope. A complete system replacement is not a prerequisite.
We design checks, approvals and exception handling into the workflow. Uncertain or incomplete results need to reach the responsible people. At MatheCode, a second agent reviews changes before publication. We agree the checks your workflow needs before rollout.
The scope determines the cost: connected systems, data quality and required checks. Each phase has a fixed price before you commit. A first usable version typically takes six to ten weeks. Model usage and running costs are considered separately.
Yes. If the right starting point is unclear, we begin with AI consulting. We consider the current workload, available data and potential value. Development starts once we agree the scope.
We define which data an agent can read or change. This includes suitable credentials, permissions and checks before consequential actions. Model providers, processing locations and contractual requirements are agreed for the project.
Next step
Let us find out together where AI creates the biggest leverage in your company. From Meerbusch near Düsseldorf, for businesses across Germany and beyond.
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