Discuss your project

Individual AI and LLM models for internal processes

Reiz Consulting develops AI and LLM solutions that make knowledge, documents and specialist processes usable. Depending on the requirements, these solutions can be cloud-based, hybrid or partially operated locally.

Use AI where it delivers clear, traceable value.

Individual AI and LLM systems make sense when general chatbots are not sufficient, internal knowledge needs to remain protected or specialist processes need specific rules, approvals and data sources.

Individual AI solutionsLocal LLM optionsKnowledge basesRAG and searchAssistance systems

AI and LLM development at a glance

Reiz Consulting supports the development of individual AI components, LLM-supported assistance systems and local or partially local model setups. The focus is on specific tasks, controlled data access and a solution that can be reliably used in everyday life.

AI assistants

Assistance systems for search, summary, draft, review and internal knowledge work.

Local LLMs

Testing and building local or hybrid model setups when data storage and control are particularly important.

RAG & knowledge

Make documents, guidelines and internal information usable via Retrieval-Augmented Generation.

Integration

Embed AI building blocks in web apps, workflows, CRM/ERP processes and interfaces.

Custom solutions instead of a generic chatbot

Many companies want to use AI without giving sensitive information to external tools in an uncontrolled manner or receiving answers without a technical context. Reiz Consulting therefore first clarifies which task AI should solve, which data sources can be used and where human control remains necessary.

This results in individual AI solutions that, for example, search internal documents, summarize content, prepare tests, suggest answers or support teams with recurring decisions.

Local and hybrid models with data control

Not every AI solution needs to run completely locally, but some requirements favor local or hybrid architecture: confidential data, limited data sharing, traceable processing or better control over model, prompting and knowledge base.

Reiz Consulting will work with you to check whether a local LLM, a hybrid approach, an API solution or a combination of models, vector database and internal web app makes sense. Performance, maintenance, costs, privacy and user expectations are realistically weighed up.

From data sources to usable applications

An AI solution only becomes valuable when it is embedded in the work process. Therefore, document structure, authorizations, logging, feedback, quality assurance and clear user interfaces are part of the implementation.

  • Analysis of tasks, data sources and protection needs
  • RAG systems for internal documents and knowledge bases
  • Check and implement local or hybrid LLM architectures
  • Integration into web apps, workflows and existing systems
  • Consider logging, feedback and technical approval

Which AI support is suitable?

The selection depends on the data situation, protection needs, desired benefits and the question of how much control over the model, data and operation is required.

If internal knowledge should be discoverable

RAG system

Suitable for documents, policies, project knowledge, search and context-based answers.

When data should remain local

Local LLM

Suitable for sensitive knowledge assets, limited data sharing and controlled processing.

When teams need to be supported

AI assistant

Suitable for summaries, drafts, exams, research and structured proposals.

When AI has to be included in processes

Integration

Suitable for web apps, workflows, interfaces, CRM/ERP processes and automation.

Support for each project phase

AI projects become more reliable when benefits, data, model selection, interface and operation are thought of together right from the start.

Analysis

Clarify use case and data situation

Task, target group, data sources, protection needs and limits are made visible.

Architecture

Select model and operation

Cloud, local, hybrid, RAG, interfaces and authorizations are planned to match the target state.

Implementation

Turn the prototype into a usable solution

Assistance functions, search, prompting, data connection and web interface are implemented in a testable way.

Operation

Maintain quality and feedback loops

Answer quality, protocols, user feedback and further development are tracked in a structured manner.

How good AI and LLM solutions can be identified

An AI solution is only reliable if it provides helpful answers, respects data, makes boundaries visible and fits into real workflows.

01

Clear task

The use case is clear and is not diluted by general AI promises.

02

Data control

Data sources, permissions and storage locations are deliberately determined.

03

Traceability

Sources, answers, error cases and approvals remain verifiable.

04

Operation

The model, knowledge base and user feedback can be maintained and improved.

AI for knowledge, documents and mature processes

Reiz Consulting provides particular support where AI should not be used as a gimmick, but rather as a controlled component in the system landscape, web app or work process.

  • Internal knowledge databases and document stocks
  • Local and hybrid LLM setups
  • RAG, vector search and source reference
  • Integration into web apps, workflows and interfaces

Our approach

First the concrete benefit, then the choice of model, then the integration.

  1. 01

    Clarify use case

    We determine the task, users, data sources, risks and limits.

  2. 02

    Choose architecture

    We examine cloud, local, hybrid, RAG, model requirements and interfaces.

  3. 03

    Build prototype

    We implement a usable first version with sources, interface and feedback path.

  4. 04

    Stabilize

    We improve quality, documentation, operations and extensibility.

Briefly assess the AI or LLM project

A little information is enough for an initial assessment. From this it can be deduced whether local models, RAG, web app, interface or process automation make sense.

  1. 01task

    What work should AI specifically support or facilitate?

  2. 02Data

    Which documents, systems or knowledge sources may be used?

  3. 03Operation

    Does the solution need to be local, hybrid or cloud-based?

Get in touch

Planning an individual AI or LLM solution?

We talk about the use case, data situation, protection needs, local model options and the question of which AI solution really helps your process.

  • Describe the use case and target group
  • Name data sources, protection requirements and operation
  • Assess cloud, local, hybrid or RAG

Frequently asked questions

Answers to typical questions about individual AI and LLM solutions.

Does Reiz Consulting develop individual AI or LLM solutions?

Yes. Reiz Consulting develops AI modules, LLM-supported assistance systems, RAG solutions and integrations for specific internal processes and knowledge bases.

Can LLM models be operated locally?

Depending on the requirements, local or hybrid operation is possible. The amount of data, hardware, quality, maintenance, costs and privacy are realistically checked in advance.

What does RAG mean?

RAG stands for Retrieval Augmented Generation. Internal documents or sources of knowledge are searched for and used as context so that answers become more comprehensible and relevant to the subject.

When does an individual AI solution make sense?

It makes sense when general tools do not provide enough context, sensitive data needs to be protected, or AI needs to be integrated directly into web apps, workflows and internal systems.