From data access and model choice to quality, security and operations.
Once AI accesses company data or influences processes, a good prompt is no longer enough. The foundation is 25+ years in distributed systems: explicit boundaries, replaceable components and operations where quality and cost remain transparent.
Before model selection
The quality of an AI solution is not decided in a single demo. What matters is whether the complete path from data and permissions to a reviewable result remains under control.
Value and boundaries first — which outcome should improve and which decisions deliberately remain with people?
Control access — models receive only the data and tools required for the specific task.
Measure quality — domain correctness, latency, cost and failure modes are evaluated using realistic examples.
Keep components replaceable — business logic stays independent of models, providers and short-lived AI hype.
Technical coverage
RAGA method that supplies AI with relevant company information before it generates an answer. connect language models with approved knowledge. Ingestion, chunking, hybrid search and source references are designed around the data set and quality target.
APIA clearly defined interface through which systems exchange data and securely call functions. and MCP servers give AI systems narrowly scoped functions instead of blanket system access. Dedicated identities, least privilege and logged calls keep integration manageable.
AI tools handle clearly bounded steps, work with approved tools and hand over to people or conventional software at defined boundaries.
OpenAI, Anthropic, Azure OpenAI and local models sit behind a stable application layer. Selection, fallbacks and switching follow quality, privacy, latency and cost.
Evaluation scenarios, tracingFollowing one request across multiple technical components to locate errors and delays precisely. and operational metrics show whether answers hold up, where failures occur and how token cost and latency evolve.
Containers, KubernetesA platform that automatically distributes, monitors and stabilises applications as usage grows or failures occur. and GitOps make AI components reproducible to ship. Observability, secrets and controlled network paths are part of production, not a later work package.
Under real conditions
Replaceable model, retrieval and tool components run on dedicated KubernetesA platform that automatically distributes, monitors and stabilises applications as usage grows or failures occur. infrastructure. Dify, Weaviate and custom integrations provide a real environment for permissions, model changes, updates and operational failures rather than prepared demos only.
MCPAn open standard that lets AI tools access approved systems, data and functions in a controlled way. access, an AI-supported playground and targeted skills help consuming teams understand DataHub functions faster and adapt their integrations with generative AI. Domain APIs and permission boundaries remain the controlling layer.
Custom AI tools handle clearly bounded development tasks through tickets. Automated checks and human reviews in GitLab, GitHub and Azure DevOps keep responsibility and quality inside the existing engineering process.
Accountability and boundaries
Technical expertise also means leaving AI out deliberately. When a simpler solution is more reliable and economical, it takes priority.
Explicit rules are implemented conventionally. That is cheaper, faster and easier to explain in an audit. AI can prepare, review or flag anomalies without taking over the binding rule-based decision.
If the data or process foundation is not sound, that foundation comes first. AI does not hide unresolved domain logic.
If result quality cannot be evaluated against realistic examples, production use is not responsible.
For irreversible or safety-critical decisions, AI assists while a person retains approval and accountability.
From expertise to a project
This page shows the technical depth behind my AI solutions. If you first want to identify where automation, conventional software or AI creates concrete value in your business, the service page leads from the business process to the right solution.
AI & Automation for businessesAssess the use case
Briefly describe the data, systems and decisions involved. I will give you an honest view of whether AI fits and which next step makes sense.