Agentic engineering with Claude – AI that actually takes work off your plate .
Claude doesn't just write text – it works inside your repositories, pipelines and documentation. We deploy AI agents where they measurably save time, with reviews, tests and clear boundaries instead of blind trust.
- 300+
- Data & AI projects
- 1–3
- Months to go live
- 100 %
- Focus on productive use cases
- 2
- Locations in Germany
From chat window to working agent
The leap of recent years is not better answers – it is that models can carry out multi-step tasks on their own. Claude is currently one of the most reliable models for this, especially with code, long contexts and structured work.
Strong at code and data logic
SQL, Python, Spark and infrastructure code: Claude understands existing structures instead of reinventing them.
Long contexts
Entire repositories, specifications and data models fit into a single step – which reduces follow-up questions and errors.
Tools instead of plain text output
Agents read files, run tests and create change proposals – with a documented trail.
Controllable by design
Every change goes through review and tests. The agent speeds up the work but never decides alone.
How we put Claude to work in your development and data teams
Agentic engineering does not mean “AI writes our code”. It means: repetitive work gets automated, people decide.
Agents for data pipelines
Routine data engineering tasks automated instead of repeated by hand.
- Generating transformations from business specifications
- Adapting pipelines when schemas change
- Automated data quality tests
- Root cause analysis for failed runs
Accelerate migrations
The biggest lever: translating, understanding and documenting legacy estates.
- Translating SSIS, stored procedures and legacy SQL
- Analysing and documenting undocumented estates
- Reconciling old and new KPIs
- Step-by-step migration with parallel operation
Documentation & knowledge
Documentation that stays current because nobody maintains it by hand.
- Descriptions of tables, fields and KPIs
- Business data catalogues from existing code
- Change logs for business units
- Onboarding material for new team members
Quality assurance & review
An extra pair of eyes that never gets tired.
- Automated code reviews against your standards
- Checks for security and data protection risks
- Extending test coverage
- Consistency checks across repositories
Agents in business processes
Beyond development: structured work inside business departments.
- Analysing documents and proposals
- Preparing reports and summaries
- Integration with business systems via APIs
- Human in the loop for every decision that needs approval
Safe operations
Agents need boundaries – we set them from day one.
- Hosting via Azure, AWS Bedrock or Anthropic directly
- Permissions, logging and approval levels
- Cost and usage monitoring
- Classification under the EU AI Act and GDPR
Why agentic engineering is in good hands with us
We have been using agents productively in our own projects for months – in migrations, data quality and documentation. What works for us, we bring to you.
- We know the limits: where an agent saves time and where it creates follow-up costs, we say so before the project starts.
- Every change goes through tests, review and version control – no uncontrolled interference with production systems.
- Data engineering experience from 300+ projects goes into how we brief the agents.
- Enablement: your developers end up working with the agents themselves instead of waiting for us.
Typical agentic engineering projects
Legacy migration with agent support
Grown SQL and SSIS estates are analysed, documented and migrated step by step onto the new platform.
Migration effort significantly reduced
Automated data quality
Agents create and maintain quality rules for new tables and flag anomalies to the business team.
Errors surface before the report does
Documentation that stays current
Tables, KPIs and pipelines are described straight from the code and updated with every change.
Onboarding in days instead of weeks
How we introduce agentic engineering at your company
Identify the tasks
We look for the repetitive work that eats the most time and check which parts can sensibly be automated.
Safe environment
Access, permissions, logging and approval levels are set up before the first agent starts working.
First agent in production
A clearly scoped use case runs in daily business with review – including measurement of the time saved.
Scale & enable
More tasks, standards for working with agents and training for your teams.
Need AI for everyone first?
If what you need first is a secure, shared AI interface across the company, OpenWebUI is the better starting point.
Frequently asked questions
Does the agent write code straight into production?+
No. Agents create change proposals that run through tests, review and your existing approval process. The decision stays with your developers.
Does our source code leave the company?+
That's your call. Claude can be run in European cloud regions, and we define exactly which repositories and data an agent is allowed to see.
Is it worth it without a large development team?+
Yes, especially then. Small teams gain the most when documentation, tests and routine changes stop piling up.
How do you measure the benefit?+
Before we start we record how much time each task takes. After the pilot we compare lead times and error rates – no flattering numbers.
45 minutes of straight talk about agentic engineering.
We look at your development and data processes and show which tasks an agent can realistically take over – and which it shouldn't.
- Outcome: concrete areas of use and an effort estimate
- Honest assessment of benefit, risk and operations
- Free of charge and without obligation
Preparing appointment calendar …