Despite the billions spent annually on airport modernization, most airports still operate without a unifying intelligence layer. Systems exist in silos: baggage, terminal operations, airside management, safety, and energy, each optimized locally but rarely communicating in real-time. The result is a sprawling operation lacking collective awareness. Decisions are delayed, coordination is reactive, and human operators carry the burden of connecting the dots. LISA AI, powered by the Model Context Protocol (MCP), is a category-creating solution designed to change this. Built as a foundational intelligence layer for physical infrastructure, LISA enables airports to function as fully integrated, responsive ecosystems. MCP is the engine behind LISA’s ability to maintain real-time situational context across domains, users, and workflows, making intelligence not only available but also actionable and aligned.
The Strategic Gap in Modern Airports
Airports today face multidimensional challenges:
- Increasing passenger volumes and route complexity
- Tightening SLAs on baggage delivery, turnaround times, and passenger flow
- Labor shortages and the loss of institutional knowledge
- Demand for better sustainability and ESG outcomes
- Heightened safety and security expectations
Yet, the technology stack remains fragmented. FIDS doesn’t talk to baggage. CCTV isn’t fused with access control. Terminal operations don’t have visibility into what’s happening on the apron. In most cases, communication between departments occurs via email, radio, and phone calls. This is not due to a lack of data but a lack of cross-domain intelligence. The infrastructure is digital but not yet intelligent. MCP is the key to LISA’s ability to maintain continuity of context across disparate systems. Instead of fragmented snapshots, MCP enables LISA to integrate operational, spatial, and temporal data into a unified, live understanding of the airport, empowering it with a comprehensive view.
What LISA AI Brings to the Table
LISA AI, a real-time, AI-native platform, serves as the ‘central nervous system’ for the airport, unifying operational awareness across all functions. It interprets signals from hundreds of endpoints in real time and delivers contextual insights to the right teams, enhancing efficiency and coordination. MCP is the key to this continuous interpretation. It carries the operational context forward across time and transactions. This means that LISA is not just reacting to current events but also understanding what has happened, who is involved, and what actions are feasible.
1. Multimodal Data Fusion
LISA connects to operational systems via APIs, middleware, sensors, or OT connectors. It ingests and fuses data across:
- Flight Information Display System (FIDS)
- Baggage handling systems (BHS)
- Security and access control
- CCTV and video analytics
- HVAC and energy management (BMS/SCADA)
- Environmental sensors (e.g., air quality, noise levels)
It doesn’t stop at aggregation; it contextualizes. MCP enriches this fusion by preserving the relationships between data points. For instance, a video feed showing crowding, a delayed arrival in FIDS, and HVAC load spikes is not just three separate signals. They are part of a single, contextual model that MCP allows LISA to maintain, reason over, and update in real-time.
2. Causal and Contextual Reasoning
Unlike rule-based dashboards or threshold alerts, LISA performs causal analysis. It can answer not just what is happening but why it’s happening.
For instance:
“Why is Flight XY105 delayed?” → LISA identifies an upstream baggage belt jam traced to maintenance that caused a cascading ground crew delay.
“Why is crowding high in Terminal B?” → LISA links it to a simultaneous delay on two international arrivals and a broken escalator, as indicated by maintenance logs.
This goes beyond monitoring; it’s decision support. MCP enables this type of reasoning by encoding context models that LISA can apply to ongoing situations, not just historical patterns. MCP gives LISA the ability to remember, relate, and reason – hallmarks of accurate intelligence.
3. Natural Language Interface
Stakeholders, from terminal managers to baggage handlers, can interact with LISA through a conversational user interface. They don’t need to learn a new system or interpret dashboards. They ask questions and receive meaningful, operationally relevant answers. MCP ensures that responses are context-aware. A user asking, “Why is Gate 23 congested?” gets an answer tailored not only to the current state but also to the user’s role, location, and prior queries, because MCP preserves and transmits that operational context across sessions and interactions.
4. Role-Aware Delivery
LISA tailors insights to the user’s role. A baggage manager gets different insights than airside ops or a control room supervisor, ensuring signal relevance while avoiding noise. MCP is the framework that defines these role-based contextual views, ensuring that every insight LISA generates is mapped to the user’s situational need. It enables personalization to scale across complex, multi-role operations.
Economic and Operational Impact
LISA doesn’t replace systems; it enhances their value. It overlays existing investments and makes them interoperable, increasing the return on infrastructure and software without forcing a rip-and-replace approach. MCP maximizes this value by serving as the glue that binds systems and workflows into a shared cognitive model. It allows LISA to function not as a set of use-case-specific features but as a persistent, holistic brain for airport operations.
Key Outcomes for Airports
| Metric |
| Turnaround Time |
| SLA Compliance |
| Operational Efficiency |
| Safety & Risk Management |
| Passenger Experience |
| Impact Area |
| Airside/Ground Ops |
| Baggage, terminal, energy |
| Staff, maintenance, facilities |
| Security, access, incident handling |
| Terminal services, signage, wayfinding |
| Improvement via LISA + MCP |
| Reduced delays, better coordination |
| Increased consistency, root-cause visibility |
| Improved triage, better resource utilization |
| Faster escalation, better audibility |
| Reduced congestion, better flow |
This results in a hard ROI, faster operations, higher throughput, lower downtime, and measurable SLA gains, all without increasing headcount or adding operational complexity.
Why Now: Timing the Inflection Point
The post-pandemic aviation recovery has exposed structural weaknesses. Airports face increased variability, reduced predictability, and fewer experienced staff to manage it all. At the same time, regulators are emphasizing sustainability, safety, and uptime. This is the moment when airports need real-time operational intelligence, not just to survive but to scale. LISA enters the market as this need reaches a peak and MCP enables LISA to meet that demand not with brittle rules but with adaptable, contextual reasoning.
LISA has several structural differentiators:
- AI-Native Platform: Built for reasoning, not just rule execution.
- Plug-and-Play Integrations: Works seamlessly with existing airport systems, eliminating costly CapEx-heavy overhauls.
- Domain Specialization: Pre-trained on airport operations logic: turnarounds, crowding, SLAs, not a general-purpose engine.
- Edge + Cloud Hybrid: Supports regulated environments where specific data must remain on-prem.
- Model Context Protocol (MCP): The heart of LISA’s intelligence layer, enabling persistent, multi-role, cross-domain context modeling to deliver truly situationally aware AI across time, space, and system boundaries.
The Broader Vision: A Foundational Layer for Physical AI
Airports are just the beginning. LISA is part of a broader thesis: as physical infrastructure becomes digitized, it requires a contextual reasoning layer to make sense of data and guide action. The future of AI in physical spaces isn’t about replacing human operators. It’s about augmenting them with the correct information at the right time to make complex environments manageable and intelligent. LISA is that augmentation layer. It’s not an application; it’s a foundation. MCP is what makes that foundation dynamic, resilient, and scalable. It provides LISA with memory, continuity, and understanding, enabling it to operate not as a static system but as a living, operational intelligence.
What Makes LISA + MCP a High-Conviction Bet?
- Massive, Underpenetrated Market: Every airport above a specific scale needs LISA, and few are currently equipped with comparable intelligence platforms.
- Validated Demand: Early deployments are already underway, and operational users are requesting broader rollouts.
- Fast Time to Value: Lightweight, rapid integration model with visible impact in weeks, not years.
- Expandable TAM: Extensible beyond aviation to other verticals, LISA can be the intelligence layer for the entire “smart infrastructure” stack.
- Strategic Differentiation: Unlike analytics or dashboard tools, LISA operates as a real-time operational co-pilot, and MCP serves as its reasoning backbone, maintaining the situational context required to act with intelligence, not just awareness.
LISA AI doesn’t just make airports smarter; it makes them coherent. MCP ensures they stay contextually aligned across people, systems, and time. Together, they bring people, systems, and decisions into a unified operational intelligence layer, enabling resilience, efficiency, and scalable growth. As airports and other infrastructure systems become more complex, this type of intelligence will no longer be optional. It will be foundational. LISA is not just building a product. MCP is not just a protocol. Together, they’re defining a new category.