The camera is the product
We ship capture, inference, and the UI operators use — not a model file with no home.
Computer vision
Detect, classify, and measure
from images and video
Quality inspection, document capture, and on-device camera pipelines — shipped as a product your operators can run, not a lab notebook sitting next to the line.
- Cloud + edge
- Inspection
- Document capture
VisionCloud and on-device
LoopsHuman review into training
6–12 wksTypical production delivery
100%Code & IP ownership
How it works
From frame to decision — then better next week
Capture, detect, decide, retrain. The loop is the product, not a one-off model file.
01
Capture
Station cameras, phones, or document scanners — lighting and framing tuned so the defect is actually visible.
02
Detect
Models classify, locate, and measure on cloud GPUs or on-device runtimes with a latency budget you set.
03
Decide
Pass/fail, field extract, or escalate — with the crop the operator needs, not a raw probability dump.
04
Retrain
Misses and disagreements go back into a versioned dataset so accuracy improves after lighting or SKU changes.
Operator console
Bounding boxes operators can act on
We ship the capture UI, inference, and review queue together — so a miss becomes a labeled example, not a Slack screenshot.
- Cloud GPU or on-device runtime with a clear latency budget
- Crops and confidence in the same screen as pass/fail
- Disagreements routed into the next training set
Capabilities
What we build in computer vision
Detection, classification, measurement, and document understanding — chosen against lighting, cameras, and the decision a person has to make.
Line
Quality inspection
Defects, assembly errors, and pass/fail at the station. Alerts with the crop the operator needs, not a raw probability.
- Station-level pass/fail
- Bounding crops in the UI
- False-positive budgets you set
Documents
Capture and extraction
IDs, invoices, forms, and labels — layout, fields, and confidence — into the system of record instead of a shared inbox.
- Layout and field extraction
- Human confirm on low confidence
- Export to ERP or DMS
Edge
On-device and mobile
Core ML, TFLite, or ONNX on phones, tablets, and industrial PCs when the network is the constraint.
- Quantized mobile models
- Offline capture queues
- Camera UX that does not fight the user
Video
Streams and events
Count, track, and flag events in live video with sampling that your GPU bill can survive.
- Tracking across frames
- Event alerts, not 24/7 storage
- Retention you can defend
Ops
Labeling and retraining
A review queue for misses, versioned datasets, and a retraining cadence so lighting changes do not silently degrade the line.
- Review console for operators
- Dataset versioning
- Scheduled retrain jobs
Product
The application around vision
Dashboards, mobile capture, and APIs — the same product discipline we use on web and iOS. Bud Proof AI is this pattern in production.
- Operator and admin UIs
- API for downstream systems
- Web + mobile clients
Where vision pays off
Vision problems we take on
If the camera is already in the workflow, we can usually attach a model. If nobody will look at the output, we will say so.
Manufacturing inspection
Defects and assembly checks at the station, with a human override that feeds the next training set.
Agriculture and field capture
Phone or fixed cameras for harvest, grading, or plant health — including products like Bud Proof AI.
KYC and document intake
IDs and forms that have to be readable the first time, with a reject reason the user can act on.
Safety and PPE
Presence checks on sites and floors where a missed detection is more expensive than a false alert.
Stack
Models, runtimes, and cloud you can operate
We pick the stack against lighting, latency, and where the images may live — not against a slide deck.
- OpenAI · Gemini vision
- ONNX / Core ML / TFLite
- AWS · GCP · Azure
- Postgres + object store
- Eval harness
- SSO / SAML
Process
From sample frames to handover
- 01
Discuss requirements
A short call on the decision you need, existing systems, data, and whether custom AI is the right spend. Mutual NDA first.
- 02
Define the solution
Architecture, scope, timeline, and cost in a written proposal — including evals, cloud, and what you will own at handover.
- 03
Develop the solution
Engineers build and test with regular working releases on shared staging. You see retrieval quality, vision accuracy, or agent tool use on real examples.
- 04
Deploy and handover
We deploy into your cloud, transfer repositories, documentation, and IP, and leave monitoring so the system does not silently drift.
Why Zupitrix
Tangible reasons to choose Zupitrix
Senior engineers only
The people who scope the project build it. You are not handed off from a sales deck to a rotating bench.
Honest feasibility
If a SaaS tool or a rules engine is enough, we say so on the first call. Custom AI has to earn the spend.
Your code and IP
Repositories, prompts, eval sets, and models we train for you sit in your accounts. No vendor lock-in tax.
Direct communication
Backlog, pull requests, and weekly demos. You always know what shipped, what is blocked, and why.
FAQ
Questions about computer vision
Need computer vision?
Send a sample of real frames
We will look at lighting, the decision the model must support, and whether vision is the right spend — under NDA if you need it.
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