The challenge
MLtwist turns raw, messy data into model-ready datasets for government, research and enterprise customers. Every customer brings different data types, labeling tools and delivery formats, so the business needed one platform to run projects, people and pipelines — without custom engineering for every engagement.
What we built
We designed the platform around the unit MLtwist actually sells: a customer project. Each project carries its data sources, labeling instructions, workforce assignments, quality rules and delivery format, so onboarding a new customer is configuration rather than a new codebase.
Processing runs as containerized workloads on Kubernetes. When a project needs data pulled, transformed, labeled or packaged, the platform triggers the right custom pipeline as jobs that scale up for large batches and back down when idle.
Around that core we built workforce management for labelers and reviewers, labeling project setup and assignment, automated quality control, and versioned, tracked data in a secure environment.
Scope
- Project and customer management
- Workforce management for labelers and reviewers
- Labeling project setup, assignment and QC
- Custom pipelines triggered per project on Kubernetes
- Containerized processing workloads that scale per job
- Versioned, tracked data in a secure environment
The result
The platform is the operating system for MLtwist’s delivery — the foundation for its U.S. TSA multimodal labeling contract and its U.S. Department of Energy materials-data work.
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
