Product overview
LLM AI chatbot
Let an AI consultant handle your inquiries!
Reduce complexity.
Improve operational results.
The core of an enterprise AI chatbot
A work interface that combines a fluent language model with the knowledge to retrieve, permissions to respect and verifiable evidence.
LLM
Understand question intent and generate summaries, explanations, reasoning and drafts.
RAG
Find relevant company documents and inject current evidence into the answer context.
TRUST
Apply user permissions and control answers through sources and audit records.
What is an LLM, and
why is RAG needed?
An LLM is a probabilistic generative model trained on language patterns. Plausibility does not necessarily mean factual accuracy or compliance with internal standards.
Knowledge cutoff
Unaware of current regulations and project status after training
Enterprise context
Insufficient knowledge of internal terminology, product codes and business procedures
Hallucinations
Can generate fluent but incorrect answers even without evidence
Missing sources
Difficult to verify which document and clause an answer came from
Permission risks
Sensitive information may be exposed regardless of user permissions
Update cost
Retraining a model with every knowledge change is slow and inefficient
Solution approach · RAG is a practical solution that retrieves the enterprise knowledge needed at question time and provides it as context, rather than retraining the model.
From asking a question to checking evidence
First retrieve relevant company documents, then augment the LLM with the evidence found to generate answers with sources.
Question understandingIntent · Keywords02Evidence retrievalVector DB · Filters03
Context assemblyOrganize top evidence04
Answer generationRespond with sources
Routine inspections take place once a month, with an additional inspection required after 500 operating hours. Check safety sensors every week.
How enterprise knowledge becomes searchable
Collect documents and business data and turn them into searchable knowledge while preserving context and permissions.
CollectionDocuments · Databases · Business systems02
ParsingBody text · Tables · Metadata03ChunkingSplit into semantic units04EmbeddingTurn meaning into numerical vectors05
StorageVector DB + Original source linksParsing quality
Minimize loss of tables, drawings, headers and OCR content
Chunk strategy
Preserve paragraph, clause and page context
Metadata
Link department, security level, version and effective date
Synchronization
Reflect changes, deletions and expirations in the index
A retrieval pipeline that finds valid evidence for a question
Retrieval means selecting the most valid evidence needed for an answer, not simply the most similar document.
Query refinementExpand terminology and extract filters02Candidate retrievalVector + Keyword03
Permission filterUser · Organization · Security level04
RerankingReassess relevance05Top evidenceRemove duplicates and ensure diversityFind documents with similar meanings even when phrased differently.
Find exact strings such as product codes and regulation numbers.
How evidence becomes an answer
When an answer cannot be produced, the system should say it does not know and let users check the original source themselves.
Context assembly
- Structure the question and evidence
- Prioritize key content within the token limit
- Resolve conflicting and duplicate documents
Answer generation
- Reason according to business rules
- Disclose limitations when uncertain
- Follow format, tone and language requirements
Show sources
- Document name · Page · Clause
- Original source link · Revision date
- Link answer sentences to evidence
Guardrails throughout · Prompt policies · Personal data masking · Permission checks · Harmful-content and confidentiality checks · Audit logs
Turn your website into a consultation and conversion interface
Visitors ask in natural language and immediately receive grounded answers instead of navigating long menus and documents themselves.
Greater likelihood of leaving · Waiting for a staff response
Resolve immediately · Connect to consultation · Navigate to products and content
Conversion rate
Connect product and service selection to consultation CTAs
Customer support
Automated answers to recurring inquiries, 24 hours a day
Content utilization
Reuse overlooked documents, FAQs and examples
Customer insights
Discover interests, frustrations and demand through question logs
Design security and permissions first.
Apply user, organization and document permissions before retrieval, rather than generating candidate results and then hiding them.
ON-PREMISE
Highest level of control and network isolationOperational complexity and infrastructure costs
PRIVATE CLOUD
Dedicated network · Managed scalingReview cloud policies and regions
HYBRID
Keep sensitive data internal; use external models selectivelyDesign data boundaries and routing
Shared controls · Encryption in transit and at rest · Key and secret management · DLP and personal data masking · Input/output policies · Auditing and anomaly detection
Use cases by business function
Start where documents are ready and recurring questions are common, then expand from the scope that has been validated.
Manufacturing sites
Equipment · Quality · Safety- Instant questions about SOPs and manuals
- Find fault-code causes and remedies
- Find similar quality-issue cases
Project
Contracts · Design · Schedules- Summarize meeting minutes and design changes
- Track risk and decision history
- Search contract clauses and deliverables
Business support
HR · Finance · Purchasing- Guidance on policies, benefits and expense settlement
- Ask about purchasing criteria and approval procedures
- Draft reports and market materials
An enterprise CHATBOT completed by RAG
This screen retrieves internal company documents using RAG and answers based on verified evidence. Visitors can ask questions in natural language and receive fast, consistent guidance within the information approved by the company.
Analyze the question's intent, retrieve and rerank relevant documents, then have CHATBOT answer using the evidence assembled by RAG. This provides an evidence-based consultation experience centered on enterprise data rather than general-purpose generative AI.
Turn expected benefits into measurable outcomes
We evaluate answer quality, retrieval quality, user experience, operational health and safety together.
Search time
Time and clicks needed to find an answer
Response consistency
Accuracy · Evidence coverage
Work speed
Processing and drafting time
Knowledge reuse
Document utilization · Repeat inquiries
Start small, validate and expand
We establish data readiness and evaluation before focusing on model performance, and expand only where quality, security and cost criteria are met.
DISCOVER · Assessment
Assess priority workflows and users, documents, permissions and quality
PILOT · Pilot
Set up RAG with representative documents, an evaluation set and a baseline
HARDEN · Security and operationalization
Connect SSO, ACL, auditing and monitoring
SCALE · Rollout
Expand workflows and data sources; optimize costs
Recommended pilot · 1 representative workflow · 100–500 core documents · 50 evaluation questions · 4–8 weeks
Trusted AI that connects enterprise knowledge to action
More than simply generating answers, the chatbot connects enterprise knowledge to the next actions of customers and employees.
Accurate answers
Internal documents and current evidence
Safe use
User permissions and data boundaries
Connect to website outcomes
Consultation, purchasing and content actions
Continuous improvement
Questions · Evaluation · Operational data
Go beyond a website that makes customers search: offer an AI experience that understands their questions and builds confidence through evidence.
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