From documents to code to internal assistants: privacy-compliant and high-volume.
On-premise AI is used wherever sensitive data, high usage, and predictable costs matter.
Typical use cases
Document analysis Contract review, summaries, version comparisons (e.g. law firms, fiduciaries, compliance)
Code assistance & agentic coding Code generation, reviews, bug fixes, documentation (very high consumption, fast amortization)
Internal chatbots & knowledge search Chat across contracts, policies, internal documents (RAG)
Invoice & financial analysis Invoice processing, reports, SQL queries (financial and business data stays internal)
Email & customer communication Summaries, reply suggestions, prioritization (high volume, sensitive content)
HR & recruiting CV screening, skill extraction, matching (personal data, DPA/GDPR-relevant)
Why on-premise for SMEs?
- Sensitive data stays internal
- No token or per-user costs
- Scales better under heavy usage
- Cloud gets expensive fast for code, documents, and analysis
In short
Everything that is regular, data-intensive, and confidential is a great fit for on-premise AI, from SME to enterprise.
For an individual assessment or cost estimate: get in touch.
Next steps
- Open the cost calculator and determine the demand for your use cases
- Contact us for individual use case advice
Sources and further information:
- How much does it cost to run AI yourself? (costs and scenarios)
- Connecting ERP, CRM, and document data (integration)
- Does on-premise AI make sense for SMEs? (compliance and data protection)
- On-premise AI for SMEs (use cases and application examples)