Tangonet Solutions
Specialised AI, Cloud, and Engineering Teams for Airport Technology, Led by Professionals Who Have Worked in Airport Technology and Operations
Tangonet Solutions builds Artificial Intelligence, Machine Learning, and cloud engineering solutions for airport technology, delivered by teams led by professionals with direct experience in airport technology and operations.
Airport technology projects rarely fail on the model or the code. They fail because the requirement was written by someone who has never worked a live operation. A pilot that performs in a controlled test does not survive real conditions, real staffing, and real weather. The vendor delivers exactly what was specified, and it turns out to be the wrong thing.
Operational experience closes that gap, 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 environments and leadership, and by senior engineers who stand behind every person we place.
Services
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.
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.
Python application development and modernisation
API-first architecture (FastAPI, Flask, Django), integration work across third-party and internal systems, safe refactoring of legacy code, and automated testing and release management.
Edge computing and device integration
Capture, filtering, and processing close to the source, so bandwidth, latency, and cost stay under control when data comes from cameras, sensors, and field devices spread across a site.
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
| 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 |
Every engagement starts with a scoped discovery so the work is understood before anyone commits to it. 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.
Examples of custom-built AI and ML solutions from Tangonet
Both examples below were built for environments where detection had to be accurate, fast, and continuous, and where 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, computer vision, Python, TensorFlow, InfluxDB, PostgreSQL, Grafana, and other open-source components were used. 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 upon detecting airborne object intrusions, enabling prompt action on events 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.
Talk to us at tangonetsolutions.com or call +1 770-864-1200.
