Ontology in action
Five sectors. Five critical problems. A single intelligence layer.
Each use case shows the same pattern: data trapped in silos, decisions made blindly and money evaporating. Ontology connects everything in less than 5 seconds.
Main enterprise ontology use cases
- Logistics: critical order detection and automatic route reassignment in less than 4 seconds.
- Retail: connecting stock, online sales and physical store to eliminate inventory breaks.
- Construction: BIM, ERP and subcontractor integration to prevent overruns and delays.
- Consulting: unified view of teams, projects and margins to maximize profitability.
- Manufacturing: predictive maintenance and quality control connected to production planning.
The phantom container devouring your margin
3 warehouses · 40 vehicles · 120 employees
El problema de los silos
An urgent order is stuck for 72 hours in the warehouse because the WMS doesn't know the TMS has an empty truck 15 km away. The operations director finds out on Monday, when the client has already called 4 times.
Mapeo ontológico
Order_4782 → stored_in → Warehouse_Getafe
Order_4782 → belongs_to → Client_Seur (health_score: 34)
Truck_C14 → operated_by → Driver_Pedro
Route_Madrid_South → passes_near → Warehouse_Getafe (15 km)
Decisión y acción de la IA
It's 13:36. The AI agent detects that Order_4782 has been in warehouse for 71h, the client has opened their 4th complaint, Truck_C14 just unloaded 15 km away and the driver has 54 minutes before mandatory rest — just enough to pick up and deliver.
Impacto en el negocio
The store that runs out of stock on its busiest day
25 stores · e-commerce · electronics chain
El problema de los silos
The campaign launches, 400 people buy online, only 9 units exist. 391 cancellations, 200 negative Google reviews, customer service collapses for 3 days.
Mapeo ontológico
Product_AirPods → real_total_stock → 9 (discrepancy with ERP: -14)
Campaign_Email_2803 → promotes → Product_AirPods
Campaign_Email_2803 → estimated_demand → 420 units
Online_Channel → shows_available → 23 (FALSE data)
Decisión y acción de la IA
It's 18:45 the day before the campaign. The AI agent detects real stock is 9 (not 23), the campaign will generate demand for 420 units and the supplier can do urgent shipment in 3 days.
Impacto en el negocio
The project that bleeds money without anyone noticing
8 simultaneous projects · 180 workers · 35 subcontractors
El problema de los silos
The project is delayed 3 weeks. Penalty: €8,000/day. The subcontractor claims an overrun that nobody can verify because the data is in 5 different systems.
Mapeo ontológico
Project_Valdebebas → requires → Milestone_Structure (deadline: 04/15)
Milestone_Structure → depends_on → Subcontractor_Ferrallux
Subcontractor_Ferrallux → assigned_to → Valdebebas + Alcorcón + Getafe
Material_Steel → deficit → 15 tons (orders: 65 tons vs stock: 50 tons)
Project_Getafe → shutdown → impacts → Ferrallux availability
Decisión y acción de la IA
It's 08:15. Access control registers 12 steelworkers at Valdebebas (20 needed). The AI agent detects Getafe has 8 idle steelworkers, prioritizes steel for Valdebebas and documents the subcontractor breach.
Impacto en el negocio
The invisible consultant billing zero
60 people · 15 active projects · €4.8M/year
El problema de los silos
The project is delivered 4 weeks late with 8% margin (sold at 35%). Carlos leaves due to burnout (cost: €45,000). Telefónica doesn't renew the €180K annual contract.
Mapeo ontológico
Project_Telefonica → assigned_to → Consultant_Carlos (load: 145%)
Project_Telefonica → belongs_to → Client_Telefonica (health_score: 38)
Final_Deliverable → requires → 160h in 3 weeks (1 resource, capacity: 80h)
Renewal_Pipeline → depends_on → Client satisfaction (health_score: 38)
Decisión y acción de la IA
It's 09:00 Monday. Carlos logs 11h from Friday. The AI agent connects: health_score 38, 80h capacity deficit, Carlos at 145% load, client unresponsive for 8 days and 2 junior consultants available at 40%.
Impacto en el negocio
The machine that warns before it breaks
3 production lines · 200 employees · 45 clients
El problema de los silos
PH-07 breaks down. Part takes 12 days. Line 2 stops for 14 days. Penalty: €84,000. Renault opens a file and the company loses next year's tender (€3.8M).
Mapeo ontológico
Machine_PH07 → produces → Order_Renault_Q2 (154K pieces remaining)
Machine_PH07 → health_score: 41 → estimated_failure: 7-10 days
Client_Renault → penalty → 0.5%/day on €1.2M
Quality_Rejects → correlation → Overconsumption_PH07 (R²: 0.94)
Decisión y acción de la IA
It's 06:12. PH-07 registers a vibration spike. The AI agent detects: health_score 41, failure probability 87%, Renault order at risk, spare not in stock but available urgently in 4 days.
Impacto en el negocio
The pattern is always the same
The data is there. The value is there. Trapped between silos that don't talk to each other.
| Sector | ROI | Tiempo | Silos |
|---|---|---|---|
|
Logistics
|
€372K/year + client retention €890K | 3.3 seconds | 4 |
|
Retail
|
€79K sales + €85K reputation | 4.4 seconds | 5 |
|
Construction
|
€112K penalty + €60K contractual | 4.4 seconds | 5 |
|
Consulting
|
€180K renewal + €45K talent | 4.7 seconds | 5 |
|
Industrial Manufacturing
|
€84K penalty + €3.8M tender | 4.7 seconds | 6 |
Intellico's Ontology doesn't generate new data. It connects the data that already exists and allows AI agents to act with total context in less than 5 seconds.
What is an ontology?Do you recognize any of these problems?
If your company operates with more than 3 systems that don't talk to each other, you have a silo problem. We can diagnose it in a 30-minute session.