Core Service / Enterprise AI Knowledge Assistant

Enterprise AI Knowledge Assistant: keep company data private and searchable in LINE

LINE101Chat does not let AI answer from nowhere. It first retrieves relevant content from your documents, then generates answers based on those materials. Organizations can plan cloud, local, or private-cloud deployment by data sensitivity while teammates search SOPs, product manuals, internal policies, and support knowledge through familiar LINE conversations.

Consultant Note

Define data boundaries before sizing the rollout

AI assistant success usually depends on whether documents are official, current, clearly structured, and appropriate for the PoC. Cloud or local deployment is assessed alongside sensitivity.

Why Retrieval-Based AI

Why document-retrieval AI fits business workflows better than a generic chatbot

SMEs and schools usually care less about impressive language and more about whether answers match official documents, cite sources, stay within controlled data boundaries, and can be maintained over time.

It searches your official documents before answering instead of relying only on model memory.

It can attach source sections or document names for easier traceability.

Users can ask questions in LINE without knowing which folder holds the document.

Cloud, local, or private-cloud deployment can be planned by document sensitivity.

When documents change, updating the knowledge base improves answers.

It fits admissions rules, SOPs, internal policies, and support knowledge bases better than a generic chatbot.

Use Cases

Common Use Cases

If you have official documents, repeated questions, and clear users, a focused PoC is a strong first step.

Admissions FAQ assistant

Manufacturing SOP assistant

Internal HR / IT Q&A

Product support knowledge base

DevSecOps / SonarQube report assistant

Regulation and form lookup assistant

Deployment Architecture

Choose cloud hosting or local deployment based on data sensitivity

The price and timeline of an enterprise AI assistant are affected by deployment model. Most SMEs can validate ROI quickly in the cloud; sensitive data, internal rules, or R&D documents may require local or private-cloud deployment.

Cloud-hosted RAG

Build NT$120,000-260,000; maintenance from NT$12,000-35,000 / month

Production launch in about 4-6 weeks

Pros

Fast launch
No GPU or server ownership required
Lower operations burden
Good for validating ROI first

Constraints

Sensitive documents need careful review
Large long-term usage creates cloud costs
Data processing and access boundaries must be planned

Local / Private RAG

Build from NT$350,000; maintenance from NT$35,000-90,000 / month

About 6-10 weeks, depending on hardware and permission integration

Pros

Data stays in the customer environment
Stronger permissions and audit controls
Suitable for internal rules, contracts, or R&D data
Local LLM / Ollama can be assessed

Constraints

Higher initial cost
Requires IT operations and hardware planning
Model updates and performance tuning need ongoing management

Document Preparation

How to Prepare Documents

For the PoC stage, prepare 20-40 clean, official, current pages. Clearer data makes it easier to evaluate the real impact of the AI assistant.

Word

PDF

Markdown

TXT

Excel / CSV

Website pages

For the PoC stage, prepare 20-40 clean, official, current pages.

Data Readiness

Data Readiness Score

These five checks quickly show whether your documents are ready for a PoC. If the score is low, cleaning documents first is more effective than jumping into implementation.

Are documents current?

Remove outdated policies, expired forms, and processes no longer in use.

Is the structure clear?

Clear headings, paragraphs, tables, and categories make answer quality more stable.

Is the text selectable?

Scanned images need OCR first; otherwise AI cannot reliably read the content.

Are duplicates and outdated content removed?

Repeated or conflicting content makes answers unstable, so clean it before the PoC.

Can you provide real questions?

Testing with real user questions reveals the actual value after rollout.

Rollout Pace

An AI assistant rollout timeline for SMEs

Validate LINE search experience, source citations, and data boundaries in a controlled scope before spending budget on production rollout and maintenance.

Phase 0

Free needs assessment

30 minutes

Confirm pain points, document status, users, and whether cloud or local deployment is needed.

Phase 1

AI Assistant Starter PoC

2-3 weeks

Use 20-40 document pages and 30-50 real questions to validate answer quality, source citations, data boundaries, and LINE / web experience.

Phase 2

SME production rollout

4-6 weeks

Expand document categories, admin update flows, usage records, and maintenance mechanisms so the team can use it daily.

Phase 3

Production rollout and maintenance

Scope-based

Plan cloud, local, or private-cloud deployment by data sensitivity, then establish update, correction, permission, and maintenance workflows.