Back home
CASE FILE / HM-AI-002

Enterprise self-hosted AI

Open-source models on your own servers, trained or fine-tuned on internal data; documents indexed as a RAG knowledge base and systems wired up as MCP tools — data never leaves your network.

Type · Private LLM + model training + RAG + MCP
For · Internal teams and knowledge management
Status · Live
01 / PROBLEMChallenge

Staff want AI; IT cannot approve public clouds

Public AI services raise data-residency and confidentiality concerns. Internal knowledge is scattered across documents, manuals, and systems — one question means searching several places, and answers are not always trustworthy.

02 / SOLUTIONSolution

Models in-house, answers with sources

A private LLM built on open-source models runs on company servers, and we train or fine-tune it on company data when needed. Internal documents are indexed so questions are answered by retrieval with citations, and internal systems connect as MCP tools the assistant can query directly.

03 / FEATURESFeatures

Asked in-house, answered with evidence

F-01 Private model deploy & training
F-02 RAG knowledge base
F-03 Custom MCP tools
F-04 Permissions and audit
Internal AI assistant ● LIVE · ON-PREM

How many annual leave days?

Per the Staff Handbook §3.2, annual leave is 12 days, plus up to 3 days by seniority.

Staff Handbook §3.2 HR Policy 2026

Internal docs → indexAsk → retrieve + MCP → cited answer

F-01

Private model deploy & training

On-prem LLM + fine-tune

Open-source models run on company servers and can be trained or fine-tuned on internal data; conversations and data stay inside the network.

F-02

RAG knowledge base

Document index + retrieval

Manuals, policies, and internal docs are indexed; answers cite their sources and link back.

F-03

Custom MCP tools

Internal systems as tools

ERP, ticketing, and CRM systems connect as tools the assistant can query and act on.

F-04

Permissions and audit

Roles + audit trail

Visibility is scoped by role; conversations and tool calls are logged for audit.

04 / OUTCOMEOutcome

Qualitative outcomes

  • O-01
    Ask in-house, answers with sources

    Staff get grounded answers directly instead of hunting through systems.

  • O-02
    Data never leaves the network

    Models, indexes, and conversations all live on company servers.

  • O-03
    Internal systems become AI tools

    With MCP wiring, the assistant checks stock and tickets on your behalf.

Next: start with your system.

If this is close to the problem you need solved, get in touch.

Go to contact