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

When a RAG system is worthwhile – and when not

Not every search task needs a RAG system. Before we talk about technology, we check with you whether the desired use case meets four conditions – if it meets them, the project usually bears:

  • The question is repeated. If the same information is sought several times a week by different people, a measurable benefit arises. One-time special searches do not justify a system.
  • The answer is in documents. RAG finds what was written. Knowledge that lies exclusively in the minds of experienced colleagues cannot lift a retrieval – that would be a documentation project, not an AI project.
  • An error is apparent. Those who receive the answer must be able to judge whether it is true. This is why assistance systems work much better for professionals than information systems for laypeople.
  • There is one responsible. Someone from the department has to decide which documents apply and which ones are obsolete. Without this role, the knowledge base becomes obsolete faster than the system creates benefits.

Typical candidates who meet all four criteria: service and maintenance documentation, quote and contract templates, internal policies and work instructions, technical standards and testing regulations.

When to discourage: If the document stock is small and well structured, if users are looking for unique identifiers anyway, or if a clean full text search with well-maintained metadata solves the problem. Then a RAG system is additional operating expense without value – and we tell you that in the first meeting, not after the offer.

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. Are you sitting in the region? Find out more about our AI consulting in Stuttgart – with workshops on site.

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.

Your authorization concept remains valid

The question, which is too rarely asked before the introduction, is not where the data is located – but who gets which documents via the system. If a stock is indexed without regard to access rights, each user can then access content via a cleverly formulated question that he should never have opened in the file system: salary lists, dismissal drafts, offer calculations.

We therefore extend the existing permissions into the search results, instead of filtering in the interface. The system only shows each user passages from documents that he could have read without AI – Your existing concept of rights remains the authoritative authority and is not replaced by a second, parallel logic.

Equally important is the opposite: If a document is deleted because a retention period expires or someone asserts his right to deletion, the vectors generated from it must also be deleted. This does not happen by itself. An embedding is not an anonymous series of numbers, but a condensed coding of the source text – whoever keeps it keeps the content. We therefore plan the erasure concept from the beginning, instead of retrofitting it.

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. Instead of answering from training knowledge, the system first searches for the relevant passages from your documents and only presents them to the language model as context. The answer is thus fact-based and names their sources – and the model does not have to be trained on company data, which avoids effort, costs and the most sensitive data protection questions from the outset.

Yes. On request, we operate the entire solution on-premise or in your private cloud – without any data drain to the outside. What is important here is that The vector index is also below the same level of protection as the source documents, because content can be reconstructed from embeddings to a considerable extent. We shall treat it accordingly.

Your existing authorization concept remains relevant. We pass the access rights into the search results, so that each user only receives passages from documents that he could have read without the system. A pure filtering in the surface is not enough for this – the restriction must take effect in the retrieval itself.

A first productive assistant is often available in a few weeks. We deliberately start with a narrowly defined use case as proof of concept that answers a single question: Are the answers good enough that people use the system voluntarily? Only then will further sources, authorization logic and operational processes be connected. The schedule rarely determines the technology, but how quickly the department clarifies which documents apply.

The PoC entry is plannable and manageable; a volatile solution depends on the extent and depth of integration. Reliable numbers are created as soon as three things have been determined: status and quantity of documents, number of source systems to be connected and whether to operate on-premise or in a managed environment. Whoever calls a lump sum before this clarification, calculates the uncertainty – at your expense. This is exactly what the PoC is for: to make estimates into figures. The initial interview is free of charge.

Not mandatory. Depending on requirements, we use open source models on-premise or powerful APIs and select the economically suitable variant. For many medium-sized applications, existing server hardware is sufficient, because the elaborate part is the retrieval and not the model. Where GPUs are needed, we say that before the project, not in the middle of it.

We measure retrieval and response quality separately. If the search finds the wrong passages, even the best language model cannot build a correct answer from them – an overall judgment would conceal this cause. Before production, we evaluate the system on the basis of defined key figures against a set of real questions from your company and optimize iteratively.

The vectors generated therefrom are also erased. This is not a matter of course, but must be planned from the beginning – otherwise the system keeps content that officially no longer exists. This applies to expiring storage periods as well as deletion requests for GDPR. We define a concept for this before the first stock is indexed.

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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