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Services · Computer vision

Get structured signal out of video, at production frame rates.

We build detection, tracking, segmentation, and pose pipelines that run at broadcast frame rates — on the edge, in your cloud, or hybrid — and ship with the eval bench your engineers need to defend the numbers.

What you get

Three concrete deliverables.

First production pipeline in 10 weeks

Tuned vision pipeline against your footage

Models tuned on your camera angles and lighting, not a public benchmark. Detection, tracking, segmentation, or pose — whichever the use case actually needs.

Deployed with the pipeline

Edge or cloud serving with monitoring

Real-time inference on Jetson or Orin where latency matters, autoscaling Kubernetes where it does not, and frame-level latency and accuracy dashboards on day one.

Wired before handoff

Annotation and continuous improvement loop

Annotation tooling, label review, and a retraining cycle that takes new edge cases from the field back into the next model version — without a rebuild from scratch.

How we work

From kickoff to production.

012 weeks

Footage and use-case audit

Look at the actual cameras and the actual footage. Score quality, identify the failure modes you will hit, and write the eval bench before anything else.

023 weeks

Annotation and baseline

Build a labeled set against the eval bench, train or fine-tune the baseline model, and write down where it breaks. No production traffic yet.

035 weeks

Pipeline, serving, and monitoring

Wire the full pipeline — pre-processing, inference, post-processing — pick the edge or cloud serving target, and turn on monitoring before any user touches it.

04Continuous

Field rollout and retraining cycle

Roll out by camera or by site, capture novel failure modes, retrain on a monthly cadence, and version every model deployed to the field.

The stack we build on.

Cloud-agnostic. We meet you where your tenant lives.

YOLO v8 / RF-DETRSAM 2 / SAM 3WHAM poseOpenCV / ffmpegNvidia Jetson / OrinTriton inferenceKubernetesAnnotation (custom + Roboflow)

Outcome metrics

30 fps
Edge inference, full frame

On Jetson Orin, post-tuning

94%
Detection mAP

On client-specific eval bench

<80ms
End-to-end latency

Frame in to event out, edge path

From the field

One we shipped.

Professional sports · biomechanics

Built a biomechanics pipeline that extracts joint positions from broadcast video at full frame rate — no wearables, no on-field sensors, no calibration day.

0

On-field sensors required

Broadcast video only

Read the case study

FAQ

Questions buyers ask first.

Edge or cloud?
Whichever the latency and bandwidth budget demands. Real-time alerts on a factory floor or a sideline run on Jetson at the camera; near-real-time analytics over 24-hour windows run in your cloud. We document the trade in writing in week two.
Do you fine-tune models on our footage?
Yes — and we measure whether it actually moves the eval bench before we ship. We start with the strongest off-the-shelf model, then fine-tune when the data justifies the time and ongoing maintenance.
How do you handle labeling at scale?
Active learning. The pipeline flags low-confidence frames, your reviewers label only those, and the model retrains against the growing set. Throughput beats brute-force labeling on day one.
What about privacy and consent on people in frame?
Identifiable detections can be hashed or blurred at the edge before any frame leaves the camera; we document what the model sees and what gets retained per camera. The privacy posture is part of the architecture, not a bolt-on.
Can we run this without GPUs?
Sometimes. Light-weight detection models on CPU at lower frame rates can work for non-real-time use cases. We benchmark on your hardware before committing to either path.

Related services

What buyers usually pair with this.

AI engineering

Production model serving, eval discipline, and on-call coverage for the vision pipeline itself.

See the service

Data engineering

Capture, labeled-data pipelines, and storage for the footage and the events the vision system emits.

See the service

Agentic systems

Wire vision events into multi-step workflows that close the loop — alert, dispatch, log, escalate.

See the service

Ready to scope this?

Thirty minutes with a principal. We will walk through your constraints and what a 30- to 90-day pilot would actually look like.