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How does on-demand collaboration work?
We start with intake and a high-level estimate, then deliver iteratively with clear reviews and acceptance checkpoints.
How fast can an MVP or prototype be delivered?
It depends on scope. A focused prototype is often possible within a few weeks when data access and decision-makers are available.
How do you estimate effort and cost?
We estimate transparently by work packages and uncertainty. Billing is hourly based on actual demand.
What does tailored really mean?
No template logic: We model data, workflows and UI around your real operating process.
What does local AI mean and where is data stored?
By default, solutions run in controlled infrastructure in Germany/EU. Alternative models require explicit alignment.
How does internal knowledge search (RAG) work in simple terms?
Relevant internal sources are indexed. The AI answers based on your content rather than generic, uncontrolled output.
Document analysis: what can be classified or extracted?
Examples include document type, deadlines, amounts, contacts or case numbers. Scope is defined per use case.
How do you choose the right technology?
We pick the stack that fits the problem — whether .NET, Go, Rust, Python or TypeScript. What matters is long-term maintainability, performance and your team's ability to evolve the solution.
Integrations and APIs: how do you connect existing systems?
Through stable interfaces with clear contracts, authentication, monitoring and proper error handling.
Quality: reviews, tests, monitoring - what is your standard?
We use code reviews, automated testing and traceable logging. Quality is part of delivery, not an afterthought.
How do operations and ongoing support work after go-live?
We prioritize jointly in a backlog, iterate transparently and keep documentation current for long-term maintainability.
Data protection and compliance: what is your pragmatic approach?
We make data flows, access and deletion policies transparent. Legal assessment remains with your legal counsel.
