Home / Capabilities
Capabilities
Four disciplines we work in. Read these as competencies our team brings to a problem. We are a new company, and none of it describes a system we have already delivered.
Computer vision
01 / 04
Seeing what is actually there
Detection, segmentation, tracking, and change analysis over imagery and full-motion video.
Our vision work centers on the failure modes that decide whether a model survives contact with real data: small and low-contrast objects, sparse or noisy labels, sensor and domain shift, and scenes that look nothing like the training distribution.
Headline accuracy is the easy number. We care more about the cases a model gets confidently wrong, because those are the ones that cost an analyst their trust in the tool.
Model evaluation & assurance
02 / 04
Evidence a reviewer can check
Test design, benchmark construction, error analysis, and evaluation documentation for models that inform consequential decisions.
A model that cannot be evaluated cannot be responsibly fielded. We design the test before we tune the model: what the system is claimed to do, which inputs would falsify that claim, and what evidence a reviewer needs to accept it.
In practice that means held-out sets built to stress a hypothesis rather than flatter a score, and calibration treated as a result in its own right. We write the evaluation so someone outside the project can reproduce it without asking us.
ML engineering
03 / 04
From notebook to something maintainable
Training and inference systems: reproducible experiments, versioned data and models, optimization for constrained hardware.
Most of the distance between a promising result and a usable system is engineering. We build the parts that keep a model reproducible and revisable: pinned environments, tracked experiments, versioned datasets and checkpoints, and evaluation that runs on every change instead of by hand.
On the inference side we work on quantization, distillation, and runtime selection. Those choices decide whether a model runs where it is actually needed, inside the power and memory it actually has.
Data pipelines
04 / 04
Provenance as a requirement
Ingest, labeling workflows, and transformation for messy, multi-source, multi-modal data.
Model quality is usually a data problem wearing a modeling costume. We build ingest and transformation paths for heterogeneous sources, and labeling workflows that measure annotator agreement instead of assuming it.
We design lineage in from the start, so every training example stays traceable to its source and to what was done to it. That record is what makes an evaluation defensible six months later, when someone asks where a number came from.
How this usually starts
A scoped problem beats a capability statement.
The most useful first conversation is about one concrete problem: what data exists, what decision the model would inform, and what would count as good enough. From there we scope a short, bounded piece of prototype work with an evaluation attached, instead of a long proposal about what might be possible.
If the answer at the end is that machine learning is the wrong tool for your problem, that is a legitimate result and we will say so.