Tangonet Solutions
Specialised AI deployment and cloud engineering teams for airport technology, led by professionals experienced in airport technology and operations
Tangonet Solutions builds and deploys AI and AI-enabled solutions in airport environments: systems that perceive what is happening across an airport campus, decide what matters, and act within seconds. Teams led by professionals with direct experience in airport technology and operations deliver them.
Airport operations are unique in almost every aspect of business, and the underlying and enabling technologies that support them require not only technical proficiency but, to be truly successful, they also need the operational and commercial understanding of this environment.
AI is the point where artificial intelligence stops producing answers on a screen and starts perceiving, deciding, and acting in the real world. In an airport, that means a camera feed that identifies an airborne intrusion and triggers an alarm within seconds, a vehicle system that detects a violation and starts the process that follows, or a sensor network where the anomaly triggers the alert, then the inspection, rather than the scheduled round. Three things moved this from experimental to practical: simulation that multiplied the available training data, edge compute that lets inference run locally at low power, and models tolerant enough to handle variability without reprogramming for every new situation.
Tangonet Solutions builds the engineering layer that makes those systems work in production. We are not a hardware vendor, and we do not sell robots. We build the AI and AI-enabled solutions, the cloud infrastructure, and the operational monitoring that sit between a device and a decision, which is where most of these projects succeed or fail.
Operational experience closes the gap between a working model and a working system, and it shows up early: in the questions asked during scoping, the risks flagged before the build starts, and how quickly a requirement becomes something an operations team will actually use.
We work with the organisations that already hold the airport relationship: airport technology vendors, systems integrators, and airport technical teams. Our role is to add specialised engineering capacity you do not want to carry permanently, as an extension of your existing delivery process. Engineers are based in Latin America and Europe, backed by professionals experienced in airport operations and leadership, and by senior engineers who stand behind every team we deploy.
Services
Physical AI systems engineering
The software layer between a device and a decision: perception models, edge inference, real-time event processing, and the integration work that connects an event to whatever happens next in your operation. Our delivered work includes advanced media analytics (video, image, sound) for operational and commercial aspects of both landside and airside operations.
AI and Machine Learning engineering
Computer vision, custom algorithms and models, video and image analytics, real-time detection, classification, and alerting. Built with Python, TensorFlow, PyTorch, Keras, OpenCV, and other platforms.
Edge computing and device integration
Capture, filtering, and local inference close to the source, so bandwidth, latency, power, and cost stay under control when data comes from cameras, sensors, and field devices spread across a site.
Cloud and DevOps engineering
AWS and Azure architecture, infrastructure automation, CI/CD pipelines, containerisation and orchestration (Docker, Kubernetes, Helm), and edge deployment for distributed airport environments.
AIOps and observability
Monitoring, alerting, and incident detection for systems where a silent failure is the expensive one. Full-stack visibility across infrastructure, applications, and data pipelines, with dashboards built for engineers and for leadership.
Cloud cost visibility and optimisation
Mapping what is actually running, removing obvious waste, and giving finance a version of the cloud bill they can follow.
Our engineers hold AWS certifications including Solutions Architect (Associate and Professional), DevOps Engineer Professional, Security Specialty, and AI Practitioner.
How we engage
Every engagement starts with a scoped discovery, so we understand the work before anyone commits. On multi-person engagements, you get one accountable lead who runs our team and reports to you, so you are not coordinating five people to get one thing done.
| Model | Use it when |
| Project-based delivery | A defined initiative needs delivering against agreed scope, timeline, and acceptance criteria |
| Fractional teams | You need a pod working alongside your internal team, without permanent headcount |
| Staff augmentation | A specific skill is missing, and a permanent hire is not the answer right now |
| Managed services | You want to stop running a function yourself and hand it over against SLAs |
Solution examples
Examples of custom-built AI and ML solutions from Tangonet
Both examples below are Physical AI systems in production: detection had to be accurate, fast, and continuous, and missing an event had operational consequences rather than theoretical ones.
1. Vehicular Traffic Monitoring, Control, and Violation Detection:
The solution covers an extensive use case, from general vehicular movement (velocity detection, stop light, stop sign, and parking violations) to traffic density analysis, licence plate recognition, and post-processing of fines and KPI analysis using advanced dashboards that increase safety along the roadways. For this use case, we used computer vision, Python, TensorFlow, InfluxDB, PostgreSQL, Grafana, and other open-source components. The capture was done from IP cameras using a practical edge computing platform for data filtering to optimise network traffic.
2. Mobile (Aerial) Object Detection and Tracking (video and image-based)
The solution is based on a use case that incorporated machine learning techniques such as computer vision with convolutional neural networks and RNN-LSTM. Processing ran in real time to monitor and trigger alarms within seconds of detecting airborne object intrusions, enabling prompt action within defined property perimeters in areas such as airports, government locations, and prisons. The solution was built on AWS, using computer vision and Python as the main technologies.
Start with a conversation about where your delivery capacity breaks first, not a capability deck. From proofs of concept and MVPs to fully scoped projects, Tangonet Solutions moves airport technology work forward without diverting your team from core priorities. Send us an enquiry using the contact form below to start that conversation.
