Deploy Generative AI & LLMs Securely – Flexible, Independent, and Tailored to Your Use Case
You decide where your AI runs. We support you in selecting, operating, and managing the right models.
LLM-agnostic. Continuously evaluated. Fully managed on request.
IntraFind is LLM-agnostic. You can run suitable models on-premises, use models hosted by IntraFind, or integrate external cloud LLMs.
We don’t just help with the initial selection: we continuously evaluate new generative LLMs and embedding models, assess relevant model releases, and support the entire model lifecycle on request.
The right LLM for your use case
In our view, the question is not:
“Which is the best LLM?” But rather:
“Which model works best for your specific use case?”
There is no single best LLM for every company. What matters is which model is suitable for your specific use case, your data, your infrastructure, and your security requirements.
In the IntraFind LLM Evaluation Framework, we continuously evaluate generative LLMs and embedding models under reproducible conditions. Among other things, we use a defined golden-standard document set and consider criteria such as quality, performance, resource requirements, context processing, and suitability for enterprise RAG applications.
From these evaluations, we derive concrete model recommendations and, on request, support the entire lifecycle – from selection and GPU sizing through integration and operation to a later model change.
Depending on the model and application, we evaluate, among other things:
- Performance, latency, and throughput
- Context length and context processing
- required GPU and infrastructure resources
- Retrieval and RAG quality
- Quality of embeddings and semantic search
- Suitability for different document types and languages
- Operability in on-premises environments
This results in more than just a general model comparison. We assess which model is suitable for which use case.
Choosing the right LLM – let us advise you
Evaluate → Recommend → Size → Integrate → Operate → Update → Switch
On request, we support you throughout the entire model lifecycle – from selection and GPU sizing to installation and integration, through to updates and a later model switch.
LLM-agnostic instead of locked into a single provider
That's why the IntraFind Enterprise AI Platform is fundamentally designed to be LLM-agnostic.
Generative models and embedding models can be selected according to your requirements and replaced as needed.
This allows you to take advantage of technological developments without having to redevelop your entire AI application.
Three ways to operate LLMs in your enterprise
IntraFind supports different operating models.
1. Operating LLMs on-premises
For organizations with particularly high requirements for data protection, data sovereignty, and control, suitable generative and embedding models can be operated entirely within your own infrastructure.
Together with you, we select suitable models and help size the required GPU infrastructure.
On request, we then take care of installation, configuration, and updates.
Your advantages:
• Processing within your own infrastructure
• Maximum control over data and systems
• No need to transfer content to external LLM providers
• Free choice of suitable models
• Ability to switch to new model generations
• Advice on GPU selection and sizing
• Support with installation and updates
• Generative LLMs and embedding models handled from a single source
Ideal for: organizations with sensitive data, high security requirements, or clear data residency requirements.
2. LLMs hosted by IntraFind – Managed Service
If you don't want to build your own GPU infrastructure right away, you can use LLMs operated by IntraFind. Operation can take place on correspondingly agreed infrastructure hosted in Germany.
In doing so, it can be ensured that:
• Your company content is not used to train future model generations
• No content-based tracking of your queries takes place for our own purposes
• The processed content is not permanently stored for our own purposes
• Processing takes place on agreed infrastructure in Germany
• Models are selected according to your requirements
• IntraFind handles the technical operation and model management
This allows you to use generative AI without first having to build and operate the entire AI infrastructure required for it yourself.
3. External cloud LLMs and LLM APIs
If your organizational, regulatory, and data protection requirements allow it, external cloud LLMs and provider APIs can also be integrated.
Depending on the use case, commercial or freely available models from various providers can be used, for example.
Your requirements are decisive here:
• Where may data be processed?
• What data residency requirements apply?
• Which data may leave your own infrastructure?
• Which providers are approved within your organization?
• What contractual requirements exist?
• What security and data protection requirements apply?
• Which generative functions should be used via the cloud?
Based on this, we work with you to determine which services and models are suitable for your use case.
Hybrid scenarios are possible too
Particularly sensitive applications, for example, can be operated with an on-premises model, while an approved cloud model is used for other tasks. Different models can also be used in parallel for different use cases.
Good enterprise AI needs more than just a powerful LLM
Embedding models map content and search queries mathematically, thereby influencing which information is found as relevant context for a generated answer in the first place.
That's why IntraFind looks at the interplay of:
Enterprise Search + Retrieval + Embeddings + generative LLM
iFinder makes enterprise information accessible, takes existing access permissions into account, and provides generative AI with relevant context from connected information sources. Sources and hits can be made traceable in the process.
💡FAQ: What are LLMs, generative AI, and RAG?
Large Language Models – LLMs for short – are large AI language models based on machine learning that can process and generate language.
They can, for example:
- Answer questions
- Summarize texts
- Explain content
- Translate texts
- Structure information
- Formulate new texts
- Analyze content
- Conduct natural-language dialogues
In an enterprise context, LLMs can offer particularly great added value when they are specifically combined with relevant and trustworthy information from your own organization.
Generative AI or GenAI refers to AI systems that can generate new content based on training data and a user input.
This includes, for example:
- Text
- Summaries
- Answers
- Images
- Software code
- Structured information
In the context of enterprise search, generative AI is used, among other things, to generate natural-language answers or summaries from relevant search and research results.
Retrieval-Augmented Generation – RAG for short – combines information retrieval with generative AI.
Instead of relying solely on the language model's knowledge for an answer, relevant information is first retrieved from defined data sources.
This information is made available to the LLM as context.
In simplified terms:
Question → Search for relevant information → Permission check → Handover of the relevant context to the LLM → Generated answer
This allows the language model to formulate answers based on your own, up-to-date company information.
For enterprise applications, the performance of the LLM alone is therefore not the decisive factor.
Equally important are:
- Quality of information indexing
- Quality of search
- Embedding models
- Retrieval quality
- Permission checking
- Context selection
- Source information
This is exactly where IntraFind's many years of enterprise search expertise meet modern generative AI.
Case study: Generative AI at a German federal agency
In a further project, generative AI was used to answer employees' organizational questions in natural language via an intranet chatbot.
Read more >>
The answers should likewise be given in natural language.
The decisive factor:
The answers should be based on the organization's approved internal information.
If a question cannot be answered based on the available information, this should be communicated transparently, rather than generating a seemingly plausible answer.
The system is designed for several thousand queries per day.
The result is an AI assistant that combines the technological strength of generative AI with the organization's controlled information repositories and permission structures.
Such application scenarios can be realized not only in public administration, but also, for example, in industrial companies, financial organizations, or other knowledge-intensive organizations.
The use case presented can of course be applied to any industry — whether manufacturing, financial services, or public administration.
💡 Frequently Asked Questions about LLMs in the Enterprise
No. The IntraFind Enterprise AI Platform is fundamentally designed to be LLM-agnostic.
Depending on the area of application, infrastructure, and organizational requirements, different generative LLMs and embedding models can be used.
IntraFind makes recommendations based on its own evaluations – the decision about which model to use is yours. If your organization already specifies certain models, manufacturers, or cloud providers, we take these requirements into account.
In our view, there is no such thing as a universally "best LLM."
The suitability of a model depends, among other things, on the specific use case, the language, the required quality, the available hardware, the needed throughput, and the security requirements.
That's why we compare models not only using general benchmarks, but with a view to specific application scenarios.
IntraFind continuously evaluates relevant models within a systematic evaluation framework.
To do this, we use, among other things, defined gold-standard documents and compare qualitative results as well as technical performance KPIs.
From this, we derive concrete model recommendations for different application scenarios.
On request, you don't have to continuously track new models and model versions yourself:
IntraFind takes care of continuously monitoring and technically assessing relevant developments and can proactively inform you when a new model or model version becomes relevant for your application.
No.
A newer model is not automatically the better model for your specific use case.
We first assess whether a switch offers an actual advantage, for example in terms of quality, performance, resource requirements, or functionality.
A model switch is carried out in coordination with your operating and service model.
Yes.
That is precisely why we rely on an LLM-agnostic architecture.
New models can be integrated and existing models replaced without having to fundamentally redevelop the entire AI application.
Yes.
Suitable generative and embedding models can be operated on your own GPU infrastructure.
IntraFind supports you in selecting suitable models, sizing the infrastructure, and, on request, with installation, configuration, and updates.
In this case, a model hosted by IntraFind as a Managed Service can be a suitable solution.
This can be used permanently or as a transitional phase while you, for example, build your own on-premises infrastructure.
Operation can take place on correspondingly agreed infrastructure hosted in Germany. This is particularly relevant for organizations with specific requirements regarding data residency and data processing.
With the IntraFind Managed Service, processed company content is not used to train future model generations. Likewise, there is no content-based tracking of prompts or queries for our own purposes.
Processed documents, prompts, and content are also not permanently stored for our own purposes. The processing serves to provide the agreed functionality.
The specific technical and organizational framework conditions depend on the respective agreed operating and service model.
Yes.
If this fits your data protection, security, compliance, and data residency requirements, external LLM APIs and cloud models can also be integrated.
Yes.
Hybrid architectures can even make sense.
Particularly sensitive applications, for example, can be processed with an on-premises model, while approved cloud models are used for other tasks.
No.
We also evaluate and manage embedding models.
These play an important role in semantic search, retrieval, and RAG, and can have a significant impact on the overall quality of an AI application.
Both perform different tasks.
The embedding model influences, among other things, which information is recognized as semantically relevant.
The generative LLM then processes the provided context and uses it to formulate, for example, an answer or summary.
For good enterprise RAG applications, we therefore look at the interplay of the entire system.
Would you like to use generative AI for your own data?
Together, we will look at:
Discuss your use case with us
Whether on-premises, IntraFind Managed Service, external cloud, or a hybrid combination:
You decide where your generative AI will be operated.
- We continuously evaluate suitable generative LLMs and embedding models, recommend models tailored to your use cases, and, on request, accompany the entire lifecycle – from the initial selection through sizing and operation to the next sensible model switch.
- Your contacts:
Franz Kögl, CEO Breno Faria, Product Lead AI
