Knowledge Assistant
Employees ask in natural language — the AI searches manuals, policies, and process documents and delivers the relevant passage along with its source.
Most Common Starting Point
ConsultingServices.aiKI-Consulting für KMUSolution in Detail
Corporate LLM: Unlocking Internal Company Knowledge via AI.
An internal AI assistant that understands manuals, process documents, and guidelines — and delivers the right answer to your employees in seconds. No more hours of searching in SharePoint folders.
Target Audience
Who is this for?
A good fit if...
- Knowledge is scattered across dozens of PDFs, Wiki pages, SharePoint folders, and email threads
- New employees take weeks to find relevant processes
- The same questions are constantly routed to the exact same people
- Internal guidelines exist, but nobody knows exactly where
- You have 15+ employees who need access to shared knowledge
Less suitable if...
- Your company has no documented processes yet
- There are fewer than 50 documents acting as a knowledge base
- Knowledge is passed down purely verbally and cannot be put into writing
Application Areas
Where an Internal LLM Provides the Most Leverage
Three common scenarios where a Corporate LLM immediately delivers noticeable value.
Onboarding AI
New hires ask questions about the company, workflows, and tools — receiving instant context-aware answers instead of having to ask colleagues.
Saves 40% Onboarding TimeCompliance Assistant
Checks texts, proposals, or contracts against internal guidelines and highlights potential deviations. Not a replacement for legal counsel — but a strong first filter.
For Regulated IndustriesAdvantages
What Concretely Changes
Instead of 15 minutes in a Wiki or SharePoint: precise answers in under 10 seconds.
New employees find their way around faster — without constantly asking colleagues.
Every answer shows exactly which document it originated from — fully verifiable.
Data never leaves your infrastructure. On-Premise or EU-Cloud deployment possible.
Calculations based on typical SME scenarios. Individual results may vary.
Approach
How Your Corporate LLM is Built
Map Knowledge Sources
Together we identify all relevant sources: Documents, Wikis, Email archives, Databases. We clarify access rights and data formats.
→ Source Map + Permissions MatrixIndexing & Preparation
Documents are parsed, broken down into meaningful segments, converted into vectors, and indexed. OCR for scanned PDFs is included.
→ Indexed Knowledge Base + Quality Report (Coverage, Gaps)Pilot & Fine-tuning
The assistant is tested in a small working group. Answers are evaluated, thresholds adjusted, and escalation paths defined.
→ Pilot System with Access for Test Group + Evaluation ReportRollout & Knowledge Maintenance
Expansion to all employees, integration into existing tools (Teams, Slack, Intranet). Regular updates to the knowledge base.
→ Production System + Maintenance Plan + Usage Statistics after 4 WeeksArchitecture Decision
Open Source vs. Proprietary AI Models
There is no single "best model". The choice depends on your data, budget, and security requirements. That's why we work vendor-independently.
Proprietary Models
(e.g., GPT-4o, Claude 3.5, Gemini)
The standard for fast results.
- Highest performance & precision usable immediately
- Simple cloud connection without needing your own servers
- Dependency on provider and ongoing API costs
Open Source Models
(e.g., Llama 3, Mixtral)
Maximum control for sensitive company data.
- Full data control: On-Premise or Edge execution
- No ongoing "Pay-per-Token" API costs
- Requires own hardware (GPUs) and setup expertise
How we stop Data Hallucination: Whether Open Source or GPT-4 – through our methodology ("Retrieval-Augmented Generation"), we forbid the models from guessing. They cite only from your uploaded documents in a strictly verifiable manner.
Under the Hood
Technical Setup
This is how the architecture is built — transparent instead of a black box.
Document Ingestion Pipeline
PDFs, DOCX, HTML, Confluence pages, and Emails are parsed automatically. OCR processes scanned documents. Metadata (author, date, department) flows into the system.
Chunking & Embedding
Documents are semantically broken down into segments (not by character count, but by meaningful units). Each chunk is saved as a vector — enabling search by meaning, not just keywords.
Retrieval-Augmented Generation
During a query, the most relevant document chunks are retrieved and passed to the LLM as context. The model generates the answer based solely on these sources — zero hallucinations.
Access Control (RBAC)
Not everyone is allowed to see everything. Role-based access rights ensure that the assistant only returns documents that the asking user is permitted to view.
Guardrails & Prompt Hardening
System prompts are hardened against injection attacks. Output filters prevent the transmission of confidential data outside the permitted context. Answer during uncertainty: "I don't know".
Audit Log & Monitoring
Every request is logged: Who asked what and when? Which sources were cited? A dashboard provides usage statistics and an unanswered-questions feed.
Typical Stack
The stack is tailored to your privacy and integration requirements. Completely On-Premise is possible with Open Source models (Llama 3, Mistral). Azure, AWS, or your own servers — you decide.
Data Protection & Compliance
GDPR Compliance is not a secondary feature
Your documents do not leave your infrastructure. On-Premise deployment or EU Cloud (Azure/AWS Frankfurt) — you choose.
Your company data does not feed into the training of external models. API calls are contractually excluded from training.
Complete audit log of all requests and answers. Deletion concepts and retention periods can be configured according to your Data Protection Officer.
The assistant supports decisions — but doesn't make them automated. Human oversight is always maintained.
Frequently Asked Questions
Corporate LLM — Concrete Answers
Do I have to prepare all documents beforehand?
No. The ingestion pipeline processes PDFs, Word files, HTML, and scanned documents automatically. What I need: Access to the sources and a brief overview of which areas should be covered.
Can employees have different access rights?
Yes. The access control aligns with your existing roles (e.g., Azure AD / Entra ID). The assistant only displays answers based on documents the user is permitted to see.
Does the system run in the cloud or locally?
Both are possible. Cloud: Azure or AWS (EU data centers). On-Premise: Own server with Open Source models (Llama 3, Mistral). Hybrid forms too — e.g., Vector DB locally, LLM via Azure API.
How current are the answers?
The knowledge base is updated regularly — automatically upon changes in connected sources or manually via re-index. New documents are available within minutes depending on the setup.
How much does a Corporate LLM cost?
Typically starts in the Professional Package from €6,900. On-Premise setups with hardware consulting belong in the Enterprise Package. Ongoing costs: €50–300/month for hosting and API, depending on usage volume.
Corporate LLM starts with the Professional Package — with fixed deliverables.
Voice AgentsAutomate phone accessibility.
ChatbotsIntercept standard questions on your website and email.
Next Step