Biometrics and identity / United States
Embedded AI engineers who moved with the technology, from computer vision into LLMs and agentic AI
We provided AI engineering resource to MiniAi Live over three years. The work began in computer vision and shifted towards LLM and agentic AI as the technology moved, with the same engineers carrying the context across.
- Client
- MiniAi Live
- Location
- United States
- Industry
- Biometrics and identity
- Engagement
- Embedded AI engineers
- Period
- 3 years
What we delivered
- Embedded AI engineers
- Computer vision
- LLM applications
- Agentic AI
The problem
MiniAi Live needed AI engineering capacity that could grow with their roadmap rather than a fixed statement of work.
Over three years the technology itself moved. Vision work gave way to language models and agents, and a supplier who could only do one of those would have had to be replaced.
How we approached it
We embedded AI engineers into their team rather than delivering against a specification from outside, so priorities could shift without renegotiating scope.
As the work shifted from computer vision towards LLMs and agentic AI, the same engineers carried the product context with them instead of it being re-learned.
Where it landed
Three years of continuous AI engineering resource across two quite different technology generations.
Highlights
- Three years of embedded AI engineering resource
- Started in vision, moved into LLM and agentic AI as the field shifted
- Same engineers retained context across the transition
Services involved
- Computer Vision Systems
- LLM Applications & RAG
- AI Agents & Process Automation
Stack
- PyTorch
- OpenCV
- ONNX Runtime
- Python
- LLM APIs
Working on something similar?
We'll tell you what we'd do differently and what we'd reuse.