Corporate AI (internal LLM assistant): when you need your own “neural network” and what the best options look like
Queries like “corporate AI assistant”, “internal chatbot for company”, or “enterprise LLM” usually appear when:
- knowledge exists but is hard to use (docs spread across Confluence/Docs/email)
- privacy and compliance matter more than public tools
- you need AI on your data, integrated into workflows (CRM, support, BI)
Important: most companies do not need to train a model from scratch. In most cases you need an internal assistant built on a strong model + RAG (retrieval over your documents) + access control.
1) When you truly need an internal corporate AI setup
- data cannot leave your perimeter (NDA, PII, compliance)
- role-based access and audit logging are required
- deep integration into processes is needed (support, sales, internal policies, engineering)
2) The common best architecture: LLM + RAG (not training from scratch)
RAG (Retrieval-Augmented Generation) means the model answers using retrieved chunks of your internal documents.
Pros:
- faster than fine-tuning
- knowledge updates are easy (re-index)
- lower hallucination risk with good design
Cons:
- document quality and structure matter a lot
- permissions and data segmentation must be done right
3) “Top solutions” today: think in categories
3.1. Enterprise cloud LLM platforms
Best when cloud is acceptable and you need:
- security policies
- privacy controls
- SLA stability
Examples (as market references):
- managed enterprise LLM offerings in major clouds (Azure/AWS/GCP class)
- enterprise plans from leading LLM vendors (privacy policies, SLA, audit features)
3.2. Self-hosted / on-prem deployments
Best when:
- data must stay inside
- you want full control and isolation
- you have infra to run it
Common self-host model families people evaluate (depending on requirements and licenses):
- Llama family
- Mistral family
- Qwen family
3.3. The RAG and knowledge layer
The core capabilities:
- document ingestion and indexing
- embeddings + vector search
- re-ranking and filtering
- source citations
3.4. Integrations and automation
Value grows when connected to:
- ticketing/service desk
- CRM
- BI dashboards
- internal APIs
4) Security checklist (must-have)
- RBAC and data segmentation
- DLP and output filtering
- logging and audit trails
- retention policies
5) Cost: why “cheap pilot” can be misleading
Total cost includes:
- model usage (tokens)
- infrastructure (if self-hosted)
- data preparation and governance
- integrations and access management
“Chat with documents” is easy. “Trusted corporate assistant” is permissions, data quality, and observability.
FAQ
Do we need to train our own model from scratch?
Almost never. RAG + access control + integrations is usually the best ROI.
What matters more: model choice or data?
In corporate settings, data quality and permissions often matter more.
If you want, I can help you design an internal AI assistant: RAG, access control, audit logs, cloud vs on‑prem.