Before01
Company profile
- Vast, complex MES data
- 5-10 min per lookup
- No IT support at night
AI
LLM Assistant
The smartest work assistant — one that talks with your in-house data
OVERVIEW
Instead of turning questions into SQL,asking is enough to get an answer
Company data and documents move into a workplace where one line of plain language is enough to reach them.
Sample conversation
MES AI AssistantLLM
Ask about production, equipment or quality
KEY FEATURES
Four capabilities that make up the LLM assistant
From real-time data intelligence through the model hub and visualization to the user-based agent.
Capability Index04
Assistant Capability
01 / 04
01
Real-time data intelligence
Unified AI model hub
Visualizing complex data
User-based AI agent
Without any complex query language such as SQL, internal databases and documents are analyzed instantly from a natural-language question, so business teams can derive real-time insights without a data specialist and dramatically improve decision-making speed.
Management Scope
- Natural-language query
- Instant insight
- Conversational interface
- Business intelligence
- Intelligent search/discovery
- Data precision
Natural-language question and answer
Ask about field data or documents in everyday language and the AI answers accurately, straight away
WORKFLOW
The workflow behind each capability
For each capability, the order it runs in and the check it passes before moving on.
Turning documents and business systems into searchable knowledge
- 01
Source linking
MES · ERP · documents
- 02
Document cleanup
OCR · splitting · metadata
- 03
Search preparation
indexing · embedding · versioning
- 04
Permission mapping
role/access scope
Recorded alongside
01
Document source/
02
DB field/
03
User/
Operating check
Are the latest documents and read permissions confirmed?
YES
Allowed material → searchable
NO
Outdated/no permission → exclude · clean up
An example workflow · the detailed links and criteria are set to suit your floor.
SYSTEM ARCHITECTURE
What it connects,and the work it leads to
From the data that goes in to the results on the floor.
01 / INPUT
01Business dataMES · ERP · production · stock
02Company knowledgeSOPs · manuals · maintenance history
03User contextQuestion · role · access rights
CONNECTED OPERATIONS
LLM Assistant
Check intent and permissions
Search data and documents together
Grounded answers · visualization
03 / OUTPUT
✓Q&AAnswers from business data and documents
✓Visual reportsTables · charts · query basis
✓Task guidanceRelated procedures · follow-up questions
01 / INPUT
01Business dataMES · ERP · production · stock
02Company knowledgeSOPs · manuals · maintenance history
03User contextQuestion · role · access rights
CONNECTED OPERATIONS
LLM Assistant
Check intent and permissions
Search data and documents together
Grounded answers · visualization
CONNECTED OPERATIONS
LLM Assistant
Check intent and permissions
Search data and documents together
Grounded answers · visualization
03 / OUTPUT
✓Q&AAnswers from business data and documents
✓Visual reportsTables · charts · query basis
✓Task guidanceRelated procedures · follow-up questions
Linked by common identifiers, master data and historyResults checked → fed into the next run
Who uses itSite managersQuality and maintenance staffManagement and support
METHOD
How it is built
Plain language is interpreted, the answer is found in company data and returned.
Collection01 →
Data Ingestion
MES / ERP link
REST API / DB connector
Company documents
SOPs / manuals / drawings
Prompt input
Natural language
Work orders / processEquipment knowledge baseQuality standardsEquipment status
Document preprocessingOCR / DLA
Layout analysis · chunking · metadata tagging
Embedding / indexing
Text vectorization · versioning · access control
Target System
Vector DB
Document lake
Conversation history
Modeling & analysis02 →
AI/ML Modeling
Analysis & prediction
RAG
Vector similarity search · LLM answers in context
AI agent
Calls MES/ERP APIs automatically
Domain fine-tuning
Learns plant terms and process knowledge
Multimodal answers
Answers grounded in visuals
Feedback & refinement
Model evaluation
Action history
MLOps retraining
Service03
Intelligent services
Chat bot
Document Q&A
Retrieval-augmented answers
Agent automation
Learning performance
Feedback loop
Automatic reports
Learning vast enterprise data intelligently to support business decisions.
Customer service
Manufacturing/supply chain
Human resources (HR)
Sales/marketing
Banking/finance
Education
Customer service
Manufacturing/supply chain
Human resources (HR)
Sales/marketing
Banking/finance
Education
The AI answers from the data and the manuals, with evidence.
- Production and quality data
- Equipment state
- Manuals and work standards (SOP)
- Maintenance and fault history
- Questions from the floor
LLM Assistant
- Intent interpretation
- Search across data and documents
- Answer and action with its evidence
- Decision time
- Time spent searching
- First-response accuracy
- Data utilization
EFFECT
Business impact
What it leaves behind — in decision time, search time, first-response accuracy and data usage.
- 01
Shorter decisions
Scattered indicators and history are gathered into one answer with its evidence, so a call that used to need a meeting is made on the spot.
Decision timeevidencereporting steps - 02
Less time searching
Standards, manuals and past records are searched in plain language and answered with the passage itself, not the file it lives in.
Search timecoverageprecision - 03
More accurate first response
The standard procedure and what was done in the same situation before are offered together, so the first move holds up whoever is on shift.
First responseprocedurerepeat queries - 04
Higher data usage
Documents and logs that were only stored now answer questions, widening the range of data the floor actually consults.
Coveragequeriesdepartments
CASE STUDY
Case study
Natural-language queries cut production lookups from an average of seven minutes to under thirty seconds.
Customer industry
Manufacturer running one MES across three sites
After02
What was built
- Chat AI wired to MES
- Questions become SQL
- Charts, tables, voice
Results
Build an LLM assistant that queries the MES in one plain sentence
- Production info look-up time
- Before 7 min avg., After Under 30 s
- MES adoption among site managers
- Before 65%, After 94%
- Custom report requests to IT (per month)
- Before 40, After 8
- Training time for new managers
- Before 16 h, After 2 h
Figures from company B's deployment.