Ontology in action
Five sectors. Five critical problems. One intelligence layer.
Each use case reveals the same pattern: data trapped in silos, blind decision-making and money evaporating. Ontology connects everything in under 5 seconds.
Key enterprise ontology use cases
- Logistics: critical order detection and automatic route reassignment in under 4 seconds.
- Retail: connecting stock, online sales and physical store to eliminate stock-outs.
- Construction: integrating BIM, ERP and subcontractors to prevent cost overruns and delays.
- Consulting: unified view of teams, projects and margins to maximise profitability.
- Manufacturing: predictive maintenance and quality control connected to production planning.
The ghost container devouring your margin
3 warehouses · 40 vehicles · 120 employees
El problema de los silos
An urgent order sits in the warehouse for 72 hours because the WMS doesn't know the TMS has an empty truck 15 km away. The operations director finds out on Monday, after the client has 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 the warehouse for 71h, the client just filed their 4th complaint, Truck_C14 has just unloaded 15 km away, and the driver has exactly 54 minutes before mandatory rest — enough to pick up and deliver.
Impacto en el negocio
The store that runs out of stock on its biggest sales day
25 stores · e-commerce · electronics chain
El problema de los silos
The campaign launches, 400 people buy online, only 9 can be fulfilled. 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 → displays_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 an urgent shipment in 3 days.
Impacto en el negocio
The site that bleeds money without anyone noticing
8 simultaneous sites · 180 workers · 35 subcontractors
El problema de los silos
The site is delayed 3 weeks. Penalty: €8,000/day. The subcontractor claims an overrun that no one can verify because the data is in 5 separate systems.
Mapeo ontológico
Site_Valdebebas → requires → Milestone_Structure (deadline: 15/04)
Milestone_Structure → depends_on → Subcontractor_Ferrallux
Subcontractor_Ferrallux → assigned_to → Valdebebas + Alcorcón + Getafe
Material_Steel → deficit → 15t (orders: 65t vs stock: 50t)
Site_Getafe → stoppage → impacts → Ferrallux availability
Decisión y acción de la IA
It's 08:15. Access control logs 12 rebar workers at Valdebebas (20 needed). The AI agent detects Getafe has 8 idle workers, prioritises steel for Valdebebas and documents the subcontractor's SLA 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 delivers 4 weeks late at 8% margin (sold at 35%). Carlos leaves due to burnout (replacement 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 supplier file and the company loses next year's tender (€3.8M).
Mapeo ontológico
Machine_PH07 → produces → Order_Renault_Q2 (154K parts remaining)
Machine_PH07 → health_score: 41 → estimated_failure: 7-10 days
Client_Renault → active_penalty → 0.5%/day on €1.2M
Quality_Rejects → correlation → PH07_Overconsumption (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, 87% failure probability, Renault order at risk, spare part out of 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 + €890K client retention | 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 Ontology doesn't generate new data. It connects what already exists and lets AI agents act with full context in under 5 seconds.
What is an ontology?Recognise any of these problems?
If your company runs on 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.