We build AI agents into your processes and run them in production, in your own cloud.
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The agent prepares the invoice, the contract check or the answer. Your people only review it, so each case costs less.
Open positions stay open for months. Agents take over the routine part, so the team you have can handle more.
Requests and calls get an answer right away, not when someone in the team is free again.
Competitors are building agents now. A process that already runs with agents is hard to catch up with a year later.
TodayThe current version of a policy sits somewhere in SharePoint, a wiki or an old ticket, and new colleagues ask the same questions every week.
With an agentDocuments, contracts, tickets and wikis become one place to ask. Each answer names the document it comes from, so you can check it.
AI knowledge managementTodayLegal and procurement read every supplier contract line by line, although only a few clauses deviate from the standard.
With an agentIt reads the incoming contract, compares it with your templates and drafts the reply. A lawyer or a buyer decides what goes out.
AI agent developmentTodayTeams already use different AI tools, and nobody can say which of them processes which data.
With an agentA reference architecture, guardrails and a playbook, so the second and the third business unit do not start from zero again.
EU AI Act and governanceTodaySomeone copies numbers from several exports into the monthly report and looks for the evidence only when the auditor asks.
With an agentRecurring reports drafted from your own sources, with a record of who changed what. That is what auditors ask for anyway.
AI process automationTodayThe hotline answers the same questions about status and appointments, and callers wait in the queue.
With an agentThe agent asks back when something is unclear, confirms the request and hands over to a person when it should.
AI voice agentsTodayThe use case is clear, but the data is too sensitive to go into a public chatbot.
With an agentLanguage models in your data center or your cloud tenant, with access tied to the permissions you already have.
Private AI for companiesIn our experience, the model is rarely the reason. Nobody looked at the process behind the use case, so the agent had nothing real to plug into.
We agree what the engagement has to be worth before we scope it.
We follow the actual work, not the process diagram of it.
We build on a foundation that already stands, so nothing starts at zero.
What is live keeps moving, on your evidence, not on our roadmap.
APG, a pension provider in the Netherlands, asked on the ekipa innovation platform how generative AI could make pension information clear and personal. JadenX answered the challenge and then worked with APG to integrate the AI pension guide.
Read the case study on ekipa.deOne person decides, and a team of agents researches, checks, drafts and reports. Most of what we recommend to clients, we tried on our own work first.
About 1 Human, 100 AgentsBefore we scope anything, we agree what the agent has to save, with stop criteria for the pilot.
The agents run in your own cloud or on your servers, under GDPR and with the permissions you already have.
We connect the agent to your existing systems from the start, so the pilot already runs where the work happens.
Your people decide the exceptions and we train them, so the agent becomes part of their day.
Build, run and supervise AI agents in a dedicated instance inside your own infrastructure.
Signals, scoring, pipelines, inbox and sequences. Every capability works in the interface, over MCP and through the API. On-premise or cloud.
Using Claude? Claude CRM CRM MCP server
Nothing starts at zero. We build for you on a foundation that already stands, and if a piece is missing we build that too.
We build the agents, connect them to your systems and keep them running in production.
Talk about your processYou get a plan and the guardrails before anyone writes code.
Plan the first use caseRun your own work with a team of AI agents, the way we run JadenX.
JadenX is an AI agent company based in Germany. We build AI agents for companies and run them in production: for knowledge management, contracts and procurement, reporting and audit, voice agents and private AI. We also advise on the EU AI Act and train teams.
An AI agent is software built on a language model that works through a task in several steps on its own. It reads documents, emails or calls, looks up data in your systems, prepares a result and hands it to a person for the decision. Compared with a chatbot, an agent can also take steps in your systems, such as creating a ticket or filling in a form.
Processes that repeat often, follow rules most of the time and run on documents, emails or calls. Typical examples are answering internal questions from policies and wikis, checking supplier contracts against your templates, drafting recurring reports and handling standard calls in a hotline. The exceptions stay with your people.
That depends on the process, the systems the agent has to connect to and where it runs. Before we build anything, we agree what the agent has to save, so you can compare cost and benefit before the project starts. The first 30-minute call is free.
They can be. We run the agents in your own cloud or on your servers, so the data stays where it already is, and every access follows the permissions you already have. For the EU AI Act we set up the rules and the documentation that new use cases need.
In our projects the agent takes over the routine part of a process, and the people in the team review the results and decide the exceptions. Companies use this to handle more work with the team they have, for example when positions stay open.
With a 30-minute call about one process. After that we name the process where an agent saves the most, define the goal and analyze the process. Then we build the agent and connect it to your systems. Once it runs, we keep improving it.
Tell us in the call. We will say honestly whether an agent is the right answer, and if it is not, we will tell you that too.
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