AI consulting · RAG & AI agents

From their knowledge identifiable Answers

RAG systems and AI agents make your internal knowledge usable in seconds – precise, GDPR-compliant and completely on-premise on request.

Book a consultationView practical examples
GDPR compliantOn-Premises PossibleWith references
ALGEBRA Knowledge Assistant
What maintenance does pump P-23 need before winter?

Before winter there are Sealing change and an antifreeze check recommended; the flange screws with 45 Nm follow suit.

SourcesMaintenance manual S. 42Ticket #1182

Your knowledge is there – but untraceable

Professionals spend several hours a week searching for information alone. The knowledge is in manuals, tickets, protocols and e-mails – but not where and when it is needed. A professional AI consultancy starts here and makes this knowledge usable.

  • Answers are hidden in hundreds of pages of documentation.
  • Classic keyword search only finds what is exactly named.
  • Generic AI chatbots invent facts and don’t know your company.
  • Sensitive data must not leave the house.

Retrieval-Augmented Generation (RAG)

RAG combines the speed of semantic search with the language competence of generative AI – and delivers fact-based, comprehensible answers from your own knowledge, with references and without hallucinations.

Your questionin natural language
Semantic searchVector DB · Embeddings
PDFConfluenceTicketsDB
Relevant passagesTop hits + sources
LAMformulates the answer
Substantiated responsewith references

Generic AI vs. RAG from your knowledge

The same use case, two worlds: A generic AI advises – an RAG system responds from your data, with sources and without invention. The difference is immediately visible.

“What maintenance does pump P-23 need before winter?”
Generic AI & Web Searchwithout RAG
‘Pumps should generally be maintained regularly’; The exact steps depend on the model …“
No source invented
  • Does not know your company and documents
  • Invents plausible but false facts
  • No traceability, no sources
  • Data flows to external providers
VS
ALGEBRA RAG systemwith RAG
Before winter: Sealing change + antifreeze check; Flange screws with: 45 Nm follow suit.
Manual, p. 42Ticket #1182
With sources · comprehensible
  • Responds from your own knowledge
  • Fact-based, without hallucination
  • With references & audit trail
  • 100% on-premise possible – GDPR compliant

This is how we work with you

From the first idea to the productive operation – structured, transparent and with a fixed contact.

1. Initial meeting and goals

We clarify use cases, data and feasibility – and define a clear, measurable goal.

2. PoC

A clearly defined proof-of-concept on the knowledge process with the highest leverage.

3. Iterative rollout

Gradually productive – incl. Evaluation (Precision/Recall/F1) before rolling out.

4. Operation and further development

Integration (also via MCP), monitoring and continuous improvement.

Measurable benefits

95%
less search time
(45 min.)
80%
faster research
in practice
70%
less analysis time
per use case
100%
internal data
0 byte drain

Key figures from real ALGEBRA projects and studies. Concrete results depend on the use case.

What distinguishes our AI solutions

  • Semantic search with vector databases and modern embeddings
  • LLMs of choice: open source (on-premise) or API
  • DeepResearch loop for deep, iterative analysis
  • MCP connection to your existing systems
  • 100% on-premise / private deployment – full data sovereignty
  • GDPR-compliant, with source information and audit trail

AI – also in our own work

AI is not just a consulting topic for us, but a daily tool: we use AI-supported tools in analysis, conception and development. This accelerates your project and also makes smaller projects economical – with full technical control.

Tools for our consultants
AI accelerates research, analysis and conception – more time for your expertise.
Resources for our developers
AI takes over boilerplate, testing and refactoring – we focus on logic and quality.
Your advantage
Less effort means lower costs and shorter time-to-value – even smaller projects pay off.
up to ≈40% faster delivery30–50% less routine effort100% human tested

Industry-standard benchmarks for AI-supported development – concrete values vary depending on the project. What remains decisive is that every AI output is audited and accounted for by our experts.

Application areas from practice

Industry and maintenance

RAG reduces expensive downtime – technicians find answers in seconds instead of minutes.
Reading a practical example

Legal and Compliance

AI-supported research relieves lawyers and provides proven answers with sources.
Reading a practical example

Internal analyses

With RAG & DeepResearch on reliable decisions – only on the basis of internal data.
Reading a practical example

Frequent questions about AI consulting

Retrieval-Augmented Generation combines a semantic search in your own data with generative AI. The model responds based on facts and names the sources – instead of “hallucinating”.

Yes. On request, we operate the entire solution on-premise or in your private cloud – with 0 bytes of data drain and full data sovereignty.

A first productive RAG assistant can often be reached in a few weeks. We deliberately start with a clearly defined use case as PoC.

Not mandatory. Depending on requirements, we use open source models on-premise or powerful APIs – we choose the right, economical variant.

Before production, we evaluate the system measurably (Precision, Recall, F1) and iteratively optimize it – so that you can rely on the answers.

The PoC entry is plannable and manageable; a volatile solution depends on the extent and depth of integration. In the free initial consultation we assess this together.

Talk directly to our AI team

No account manager, no waiting loop – You talk directly to the people who build your solution. Let’s talk about your use case.

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