# MeteFlow > Infrastructure for airport data-driven operations, planning, and decision-making. > MeteFlow organizes passenger flows, infrastructure, operational constraints, and > airport activity into a shared operational model for planning, simulation, > forecasting, and operational decision support. - Site: https://www.meteaviation.com - App: https://app.meteaviation.com (operator portal) - Contact: demo@meteaviation.com - Built for: airport operators, hub operations teams, capacity planners, infrastructure programs - Key people: Gian Luca Littarru --- ## Identity - **Company:** Mete Srl (legal name: METE S.R.L.) — an Italian limited liability company registered in Rome, Italy. REA 1655515. VAT IT16417041007. - **Platform:** MeteFlow — operational infrastructure for airports, developed and operated by Mete Srl. - **Domains and brand assets** (meteaviation.com, app.meteaviation.com, the MeteFlow word- and figure-marks, the visual system): owned and operated by Mete Srl. --- ## Who We Are We build the infrastructure for airport data-driven operations and decision-making. Our products connect airport ecosystem data, analytics capabilities, and operational planning and execution. Our applications present the right data to the people who need it, when they need it — allowing them to take data-driven decisions, perform analysis, and refine planning and operations through feedback. --- ## Positioning Most airports run on a patchwork of siloed planning models, static assumptions, and reactive coordination. Small disruptions propagate invisibly across stands, gates, queues, and revenue — long before anyone has the data to act. MeteFlow models the airport as a tightly coupled system. Schedules, infrastructure, resource state, telemetry, and planning assumptions are organized into a single shared operational model, and operational changes are traced as they propagate across coupled subsystems. **Differentiation:** unlike dashboards, isolated planners, or retail BI tools, MeteFlow focuses on operational causality — modeling *why* outcomes happen, not just that they happened. --- ## Architecture One platform → one operational model → multiple operational applications. ### Platform Foundation Reusable, airport-agnostic computational substrate. Includes simulation engines, propagation logic, forecasting framework, ontology framework, scenario engine, validation engine, capacity methodologies, and operational intelligence infrastructure. ### Airport Operational Model The airport-specific configuration that sits on top of the foundation. Terminal topology, stand inventory, gate systems, processing systems, operational areas, airline allocation, routing assumptions, capacity assumptions, operational constraints, and resource models. The platform stays reusable; the operational model changes per airport (RUH ≠ FCO ≠ DXB ≠ KSIA). ### Operational Applications Surfaces built on the shared operational model. Each application is a different reading of the same operational state. They compose freely because they share the same underlying ontology. --- ## The Airport Operational Model MeteFlow models the airport as a network of stateful operational entities and the dependencies between them. - **Entities** — flights, passenger cohorts, aircraft rotations, queues, stands, gates, processing resources, infrastructure systems. - **State** — occupancy, allocation, dwell, queue saturation, congestion, utilization, disruption. - **Constraints** — capacity limits, staffing constraints, compatibility rules, infrastructure constraints. - **Dependencies** — upstream/downstream coupling between systems, passenger dependencies, operational coupling between airside and landside. - **Temporal structure** — wave structures, peaks, persistence, recovery periods, state evolution over time. The same substrate that describes stand assignment also describes passenger dwell, queue saturation, and commercial flow. There is no separate retail engine, no parallel analytical stack. --- ## Core Concepts ### Propagation Events do not occur in isolation. A change — disruption, decision, or assumption — propagates through coupled subsystems. MeteFlow traces the full chain of effect as a causal graph. Example: *inbound delay → stand occupancy shift → gate conflict → passenger redistribution → queue pressure → recovery impact*. ### Planning ↔ Operations Convergence Most platforms split strategic planning from operational management. MeteFlow unifies them: planning assumptions evolve continuously into operational state, and operational reality continuously refines planning inputs. No quarterly batch boundaries. ### Explainable Intelligence Every input, rule, and constraint is inspectable. Predictions, forecasts, and recommendations come with the operational context that produced them. No black-box outputs. ### Commercial Coupling Dwell, passenger exposure, flow density, checkout saturation, and queue abandonment are operational quantities first. Commercial impact is a downstream consequence of modeling the airport correctly — not a separate product. ### Cohort Intelligence Passenger cohorts (transfer, premium long-haul, low-cost short-haul, delayed, early-arrival) have distinct dwell distributions, conversion behavior, and sensitivity to disruption. Airline allocation, gate allocation, and disruption reshape that distribution; the operational model carries that resolution. --- ## Operational Applications Manifestations of the shared operational model. Composed freely because they share the same ontology and state. - **Infrastructure Planning** — capacity planning, operational simulation, infrastructure stress analysis. - **Operational Replay** — reconstruction of operational state and disruption days. - **Disruption Analysis** — causal modeling of disruptions and downstream effects. - **Capacity Intelligence** — infrastructure pressure across operational regimes. - **Passenger Flow Intelligence** — circulation, dwell, exposure surfaces. - **Commercial Flow Intelligence** — commercial impact derived from operational state. - **Resource Planning** — staffing, gates, and resource coordination against the operational model. - **Decision Support** — quantified options under live and projected state. --- ## Commercial Flow Intelligence (example surface) Operational–commercial chains traced through the model: - *Security pressure → revenue impact*: security queue increase → reduced airside dwell → reduced passenger exposure → revenue impact (Δ −2.4%). - *Gate reassignment → commercial performance*: gate reassignment → passenger flow redistribution → retail zone exposure shift → commercial performance impact (Δ +1.1% concourse B). - *Wave compression → lost sales*: passenger wave compression → checkout saturation → skipped purchases → lost sales (Δ −€4,200 / wave). --- ## Why MeteFlow - **Operational realism** — models match how airports actually run; brownfield by default. - **Explainable assumptions** — every input, rule, and constraint is inspectable. - **Brownfield complexity** — built for legacy systems, mixed data quality, and partial telemetry. - **O&D operational intensity** — designed for the propagation dynamics of high-intensity origin-and-destination operations. - **Seasonality** — operational state is seasonal, weekly, and hourly; the model carries that structure end to end. - **Integrated substrate** — passenger, flight, infrastructure, staffing, and commercial surfaces in one coupled model. - **Infrastructure-scale simulation** — engineered for the full operational graph, not a representative subset. --- ## Engineering Principles How MeteFlow builds operational systems. 1. **Operational Realism** — airports are tightly coupled systems running under real infrastructure constraints, finite resources, and uncertainty. Software must reflect that, not abstract workflows. 2. **Model the System, Not the Symptoms** — delays and congestion are downstream effects. Focus on state and propagation, not isolated KPIs. 3. **Planning and Operations Are Connected** — both live within the same operational framework, sharing one ontology and constraint set. 4. **Continuous Reforecasting** — dynamic reforecasting using live signals, not a daily snapshot. 5. **Explainable Outputs** — predictions, forecasts, and recommendations must be interpretable; every output carries its operational context. 6. **Infrastructure-Grade Systems** — resilient, observable, deterministic where possible, designed for operational continuity. 7. **Propagation Matters** — understanding how a small deviation spreads is often more important than measuring isolated metrics. 8. **Human-Centered Decision Support** — the goal is to improve operational awareness, coordination, and decision-making — not to replace operations teams. --- ## Forward Trajectory - **Near-term:** operational system model, scenario simulation, disruption analysis, capacity intelligence, continuous reforecasting. - **Long-term:** predictive recovery planning, real-time state assimilation, cohort-resolved demand, and resilient airport orchestration. Long-term vision: to augment airports’ ability to analyze, operate, and plan — through a unified, data-driven view connecting operations, infrastructure, passenger experience, and operational economics. --- ## Pages - [/](https://www.meteaviation.com/) — homepage: hero, problem, operational model, platform architecture, capabilities, propagation, scenario, commercial flow, cohorts. - [/about](https://www.meteaviation.com/about) — who we are, philosophy, operational causality, long-term vision. - [/engineering-principles](https://www.meteaviation.com/engineering-principles) — the eight principles above, in full. - [/security](https://www.meteaviation.com/security) — Security Operations, the flagship operational application surface. - [/trust/security](https://www.meteaviation.com/trust/security) — security and governance posture. - [/trust/privacy](https://www.meteaviation.com/trust/privacy) — privacy posture. - [/trust/terms](https://www.meteaviation.com/trust/terms) — website terms of use. - [/trust/responsible-disclosure](https://www.meteaviation.com/trust/responsible-disclosure) — coordinated disclosure policy. --- ## Origin MeteFlow was shaped through direct experience in airport operations, planning, and large-scale hub expansion environments — with a focus on operational realism, infrastructure constraints, and the coordination between planning assumptions and real-world airport operations.