The challenge
The TSA needed multimodal data — video, audio, text, images and 3D scans in the DICOS security-imaging standard — preprocessed, labeled and packaged to develop threat-detection models for aviation screening.
What we built
The MLtwist platform we built already treated every customer as a configurable project, so the TSA work was set up as project configuration: DICOS and multimodal inputs, labeling instructions, reviewers and delivery formats.
Kubernetes-triggered pipelines handled preprocessing and transformation for each data type, feeding human-in-the-loop labeling with automated quality control, and packaged results as JSON or DICOS.
Every dataset stayed versioned and tracked in a secure environment, from raw input to delivered package.
Scope
- Multimodal ingest: video, audio, text, image and 3D DICOS
- Preprocessing and transformation pipelines
- Human-in-the-loop labeling with automated QC
- Versioned, tracked data in a secure environment
- Per-project setup of inputs, instructions, reviewers and formats
- Delivery as JSON or DICOS packages
The result
MLtwist reports that its pilot cut processing time from eight weeks to three. In February 2026 the TSA awarded MLtwist a $590K contract for multimodal AI data labeling and processing.
Axio engineered the platform MLtwist runs on — how we manage projects and people, run labeling, and launch custom pipelines on Kubernetes. It’s the foundation we delivered our TSA and Department of Energy work on. They build like owners, and they ship.
David Smith
Founder & CEO, MLtwist
