Custom Engineering · Platform and Payload Customization

Data Interface Adaptation

Data interface work is mostly about agreeing semantics. Both ends can speak the same protocol and still mean different things.

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Overview

Write down the message set, the units, the timestamp convention and the coordinate frame before implementation.
Agree the behaviour for missing, delayed and out-of-order data — this is where most integration defects occur.
Verify with recorded data from a real sortie, not with a bench simulation alone.
Plan version negotiation so that one side can be updated without breaking the other.

Data adaptation is where working payloads produce unusable output

A payload can be mounted, powered, controlled and functioning perfectly, and still fail the programme because its output cannot be consumed. That failure is common, it is discovered late, and it is almost always avoidable — by establishing the output format and the receiving system's expectations before the payload is selected rather than after.

The questions are concrete and unglamorous. What does the payload produce, in what format, at what rate and in what coordinate or time reference? What system has to consume it? Does that system expect a different format, a different projection, a different metadata set or a different naming convention? And who performs the conversion, with what tooling and how often?

None of these are hard, and all of them are expensive when discovered after acquisition. The adaptation is usually a conversion step plus a validation step, and it is cheaper to design it once than to improvise it per delivery.

The three levels of adaptation

The first level is format and container: file type, compression, naming, directory structure and metadata. This is the easiest and the most frequently needed. A receiving system that expects GeoTIFF and gets JPEG, or expects a specific filename pattern and gets timestamps, will not consume the data regardless of its quality.

The second is geometry and reference: coordinate system, datum, projection, units and the georeferencing model. This is where survey and mapping data most often fails to integrate, and it is a substantive question rather than a cosmetic one — a dataset in the wrong datum is not approximately right, it is wrong.

The third is semantics: what the fields mean, how severity or class is encoded, and how records relate to the receiving system's own identifiers. This is the level that determines whether findings can be acted on rather than merely stored, and it is the level that usually requires the most conversation with the client.

Where the adaptation lives

The conversion can run on the ground station, on a server, or inside the client's own infrastructure. The choice depends on data volume, on latency requirements and on where the data is allowed to be. Where volume is large and latency irrelevant, post-processing on a server is simplest. Where data cannot leave the client's environment, it runs on-premise.

Our on-premise deployment and data management work covers the case where data must stay within client infrastructure, and server deployment assessment covers the hosted case. The decision is usually driven by the client's data governance rather than by technology.

Where the receiving system is a GIS, our GIS platform integration work covers that specific case; where it is a business system, API integration does. Both are more about the receiving system's expectations than about the aircraft.

Validation and acceptance

A data adaptation is accepted by demonstrating that the output loads and is usable in the receiving system, with the client's own data, end to end. Not by demonstrating that a conversion script runs, and not by demonstrating that a sample file opens in a viewer.

That acceptance should include the failure cases: what happens when a sortie is partial, when metadata is incomplete, or when the data does not meet the accuracy tolerance. A conversion that silently produces a plausible but unflagged bad output is worse than one that fails loudly.

Acceptance criteria for the analysis side, where included, are covered by AI performance acceptance, and the review steps around the data by human review and reporting workflow.

Commissioning a data adaptation

Send the payload, its native output, the receiving system and an example of data that system currently accepts — that last item is the most useful thing you can give us and it is often available immediately.

We will tell you what conversion is required, where it should run, and what validation we would want before the first operational delivery.

Contact our engineering team, and see custom data acquisition standards for the acquisition-side specification that usually accompanies this work.