All company knowledge becomes
a personal assistant for every employee
LINE ASSIST is a RAG-based AI assistant connected to your policies, accounts, HR and finance data. It answers only within each employee’s role permissions, cites sources on every answer — and runs fully on-premise, so company data never leaves.
Auth & permission gateway
SSO login with role/department (RBAC) filters applied
Hybrid search + GraphRAG
Vector + keyword fusion, expanded through relationship graphs
Grounded LLM generation
Answers generated only from retrieved documents
Cited answers
Source links on every answer · full audit logging
Find it, ground it, and answer only within permission
Enterprise assistants succeed or fail on retrieval and control, not model size. LINE ASSIST retrieves precisely with a modern RAG stack, answers only what the user is allowed to see, and leaves evidence on every response.
Authenticate & filter
After AD/SSO login, role and department permissions constrain the search space itself — out-of-permission documents are never even retrieved
Hybrid retrieval
Semantic vector search fused (RRF) with keyword search that nails exact identifiers — meaning and precision together
GraphRAG & decomposition
Expansion along account–contact–contract graphs; compound questions decomposed into sub-queries (agentic)
Grounded generation
Answers generated strictly from retrieved documents with mandatory source links; no evidence, no answer
* RAG instead of fine-tuning: sensitive data is never absorbed into model weights — deletion requests take effect instantly by removing the index.
Exact names + semantics
Vector search alone misses identifiers like "Contract T-2024-088". Keyword fusion (RRF) captures exact matches and meaning at the same time.
Answers connected by relationships
Accounts, contacts, contracts and receivables link as a graph — one question about an account assembles scattered facts into a single answer.
Compound-question decomposition
"Who is on leave next week, and what is Account A’s receivable?" — mixed asks are split into sub-queries, retrieved separately, then synthesized.
Triple-layer security
Encryption at rest (AES-256) · in transit (TLS 1.3) · permission isolation (RBAC) — plus an on-premise LLM that eliminates external transfer entirely.
Use Case Analysis
Different questions from every team — one assistant, answering within each person’s permissions.
A 360° account view in one question
Account data lived across ERP, spreadsheets and email; preparing for a meeting took each rep 30+ minutes.
ERP records, contracts and contacts were linked as a graph, so one account name returns contracts, contacts and receivables — with sources.
Meeting prep collapsed to a single question, and account history survives handovers and absences intact.
Policy questions become self-service
Repetitive questions on leave carryover, travel expenses and benefits consumed HR hours — with inconsistent answers.
Company policies and notices were indexed, with clause-level source links mandatory on every answer, creating a self-service desk.
Repetitive inquiries dropped sharply and answers unified at clause level; most onboarding questions are now handled by the assistant.
Meeting decisions, retrievable again
Decisions and owners lived in meeting minutes, but finding "what did we decide back then?" weeks later took ages.
Minutes and quality documents were indexed for decision/owner/due-date-centric queries.
Past decisions surface in seconds with their source documents — meetings shifted from re-debating to progress checks.
* Figures and scenarios reflect representative deployments and may vary by process and data conditions.
Specs & Integration
Frequently Asked Questions
Does company data go to an external AI?
What if the AI makes things up (hallucinates)?
What if someone asks about data beyond their permission?
How long does deployment take?
See a demo on your own company documents
Tell us about your process and data environment — we will come back with a feasibility summary and expected impact.