01
Computer Vision
Object detection and tracking, image and video analytics, and visual inspection prototypes.
Independent AI engineering studio
AI systems built for real-world problems.
Infera Studio builds practical computer vision and machine learning systems, from early prototypes to production-ready integrations.
VISION / 01LIVE INPUT
OBJECT / 010.94
OBJECT / 020.87
X / 1280Y / 720FRAME 01042● ANALYSING
Capabilities
Engineering across the AI stack.
From model development to integration, we focus on making AI useful in the context where it needs to work.
02
Machine Learning
Custom model development, careful evaluation, and performance optimization.
03
RAG & Intelligent Search
Document ingestion and retrieval, grounded generation, and answer-quality evaluation.
04
AI Integration & Deployment
Inference APIs, existing-product integrations, and deployment and monitoring foundations.
Selected explorations
Built to be tested, not just presented.
Early technical demonstrations designed to make assumptions and failure modes visible.
Prototype
DEMO 01
Football Video Analytics
An experimental computer vision pipeline for detecting and tracking players and other objects in football footage.
RETRIEVED CONTEXT010203
In developmentDEMO 02
Document Intelligence with RAG
A retrieval-augmented generation prototype for searching documents and generating answers grounded in retrieved context.
These are internal prototypes, not client deployments. Project links, results, and live demonstrations can be added as they are verified.
Approach
Start with the problem. Engineer the solution.
- 01
Understand
Define the business problem, constraints, available data, and success criteria.
- 02
Validate
Establish a baseline and test whether AI is an appropriate solution.
- 03
Build
Develop the model, pipeline, and application integration.
- 04
Evaluate
Measure performance, investigate failure cases, and prepare the next deployment step.
Principles
Practical engineering over AI theatre.
Measure what matters.
Evaluate systems against meaningful tasks rather than relying on impressive-looking demos.
Build for constraints.
Account for data quality, latency, cost, reliability, and integration requirements.
Be honest about limitations.
Make failure modes visible and validate assumptions before scaling.
Start a conversation
Have an AI problem worth solving?
Tell us what you're building, what you're trying to improve, and where you're getting stuck.