Machine Learning and Computer Vision
Models that classify, predict, read documents and recognise faces, shipped behind an API with evaluation you can check, not a notebook that only works on the sample data.
Where these projects go wrong
Most ML projects stall between a good offline score and a working product: the model meets real inputs, drifts, or cannot be served fast enough. The work is in the evaluation, the serving path and what happens when the model is unsure.
What We Build
Computer vision and OCR
Face search, emotion detection and document extraction, with confidence thresholds and a review path for unclear cases.
Image generation and LoRA
Stable Diffusion pipelines and LoRA training on GPUs, with every model checked for commercial use.
Prediction and scoring
Classification, forecasting and risk scoring with a baseline first, so you know what the model actually adds.
Honest evaluation
Held-out and cross-day tests, and a written note of what the numbers do and do not show.
Related Work
Tell us what you are building
Send the idea or the problem. You get a straight answer on scope, stack and what it would take.
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