How do you build a platform that processes large volumes of aerial or sensor data?

The first design decision is to get processing out of the request path entirely. Ingest accepts the data and returns immediately; the actual analysis runs as background jobs with visible state, retries and failure handling. Here that is Celery over a Python pipeline. The second decision is to make every processed observation permanently traceable to its source capture, because unattributed findings cannot be trusted or re-checked later.

How do you connect field data to physical infrastructure assets?

By treating the asset as the centre of the data model rather than the capture. Each observation is attached to a specific asset and to the flight or survey that produced it, so an asset accumulates a history rather than existing as a series of disconnected reports. This is what makes questions like whether a condition is new or has been slowly worsening answerable, which is usually the actual operational question.

Why use mapping libraries rather than tables for utility data?

Because a utility network is spatial before it is anything else, and its operators navigate it geographically. Leaflet.js gives the interface a map-first structure where a user can move from a whole region to one asset's inspection history in a few steps. A table can express the same data, but it forces the user to translate between how the system presents the network and how they actually hold it in their head.

How long does a platform like this take to build?

It depends on how much of the processing pipeline already exists and how clean the asset data is going in. In our experience the interface is rarely the long pole; reconciling asset records and defining what counts as a reliable observation usually takes longer than expected. We scope that discovery explicitly rather than assuming the existing asset register is accurate, because it very often is not.

HomeEnergy & climateCase study · P-019

Energy & climate · Aerial inspection & grid analytics

Aerolytics: aerial data analysis for utility networks.

A platform for a North American utility-sector operator that collects data over network assets from the air and turns it into something an engineering team can act on. Built with Python, Django, Django REST Framework, Celery, Angular, Angular Material and Leaflet.js, it moves inspection work from photographs on a laptop to a queryable, mapped record of the network.

The problem

The challenge.

Aerial inspection produces very large volumes of imagery and sensor data per flight — orders of magnitude more than anyone will review by hand.
The value is not in the capture but in the association: every observation has to be tied to the specific asset it belongs to before it means anything operationally.
Processing is slow and bursty by nature, so it could not sit in the request path without making the application feel broken.
Field and inspection staff needed to work with results geographically rather than as rows in a table, because a utility network is a spatial object before it is a database.

The plan

Our approach.

Built the ingestion and processing pipeline in Python, with Celery carrying the heavy per-flight work so that uploads and analysis run as tracked background jobs rather than blocking anyone.
Modelled assets, inspections and observations in Django as first-class entities, so an observation is permanently attached to the asset and the flight that produced it.
Exposed everything through Django REST Framework, keeping the processing pipeline and the interface cleanly separated so either can change without breaking the other.
Built the operator interface in Angular with Angular Material, designed for reviewing long queues of findings without fatigue rather than for demo screenshots.
Put Leaflet.js at the centre of the experience so the network is explored on a map, letting a user move from a region to a single asset's inspection history in a few interactions.

The result

The outcome.

Inspection output becomes a queryable record of the network rather than a folder of imagery.
Heavy processing runs as visible background work, so the application stays responsive during large ingests.
Findings are explored geographically, which matches how the people using it already think about the network.
Each asset carries its inspection history, so change over time is visible instead of being reconstructed from memory.

Trusted by our clients

What partners
tell us.

Most clients renew for a second engagement. The ones who don't usually hire someone from our team to run the project in-house.

We are a mental health charity and not tech-minded. We needed to improve our patient registration system but wanted a company that we can relate to. Hassan took the time to meet with me and patiently went through our process to find a personalised solution for us. Hassan's team did a great job streamlining our process and connected our registration system to our patient management software. Whenever we have a glitch, they resolve this quickly. Enigmatix Global provides a personal and efficient service and we have adopted them to be our IT provider.

Tien Kuei

CEO · Power to Live Foundation

I am writing to wholeheartedly recommend Enigmatix Global as a premier bespoke software development agency. Our journey with them has been nothing short of exemplary, far exceeding our initial expectations. While many agencies can develop based on strict specifications, Enigmatix Global demonstrated an incredible commitment to our project that was truly unparalleled.

Robert McPherson

Managing Director · RBM Global Ltd

Dragonfly has been working with the team at Enigmatix Global for several years now on building out our web-based platform and APIs. One of their strengths is quickly building out a multi-disciplinary team to work on the development and delivery of features with different technical requirements. Their support has allowed us to accelerate our development work without building an internal team. They have also worked seamlessly with other third parties both during the acquisition of Dragonfly and development of our mobile app. Enigmatix Global has been instrumental in bringing our ideas to life and providing guidance to help us make the best technical decisions. I would recommend getting in touch with them if you're looking for support.

Rebecca Palser

COO · Dragonfly

Hassan and the Enigmatix Global team have been indispensable partners as we rebuilt our client-facing applications into a single platform. As well as being exceptional technically, they consistently invest the time to understand the product and suggest how it can be enhanced and improved.

David Claridge

CEO · Dragonfly

Enigmatix Global did some great work for Sainsbury's, bridging complex interactions between some older less flexible systems, some rigid vendor solutions and the new systems we were building. The software Enigmatix Global developers wrote stood up well, long beyond the interim scope of it needing to work for 12 to 18 months, and was still in active use 5 years after its creation, since it was part of mission-critical systems.

Fraser Pearce

Senior Engineering Manager · Sainsbury's

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