Your cameras see everything. Your software sees nothing.
We build the model layer that turns raw footage and images into decisions: detect, classify, count, alert.
Live detection
Scanning line hits the frame. Objects surface. Bounding boxes lock.
The problem
A camera without a model is just a hard drive filling up.
Every camera on your factory floor, your retail shelf, your warehouse dock is generating data that a person has to watch or that disappears unreviewed. The information is there. What is missing is a system that reads it and acts.
Computer vision gives software the ability to see. Detection finds objects in a frame. Classification tells you what they are. Segmentation traces exactly where they are. All in real time, at a scale no human review team can match.
The hard part is not finding a pretrained model. It is making one that works on your environment. A YOLO model trained on public datasets will miss defects your quality team spots in seconds because it has never seen your product, your lighting, or your edge cases. Building a vision system that actually performs on your data is the engineering problem. That is what we do.
Picture this. The demo runs on a laptop in a clean office. The sample images are crisp, evenly lit, shot straight on. Detection hits every box. Everyone nods. Then the same model goes onto the line camera mounted above the conveyor, where the part is wet, the overhead light flickers, the belt vibrates, and half the frames are shot at an angle nobody trained for. Accuracy falls off a cliff. The model did not break. It was never tested on the world it had to live in. If that is the story you are afraid of, you are reading the right page.
The thing nobody warns you about
The model does not fail loudly. It drifts.
Most teams think a vision model either works or it does not. The real failure is quieter. The world the model was trained on and the world it runs in slowly stop matching. Researchers call it domain shift, and it is the single most common reason a model that aced its test set quietly rots in production. Your training images were daytime. Now a shift runs at night. The lens was clean in the dataset. Now it is smeared with dust. The packaging got a new label. The camera got swapped for a different sensor. None of these throw an error. The model just gets a little more wrong every week, and nobody notices until the misses pile up.
This is why a benchmark score lies to you. A number on a curated test set tells you how the model does on images that look like its training data. It tells you nothing about the angle, the glare, the motion blur, or the season your real camera will hand it. The gap between those two distributions is where almost every vision project dies. Closing it is not a tuning trick. It is the whole job.
Now the cruel part. The one thing you actually built the system to catch is usually the rarest thing in your data. A factory makes good parts almost every time, so defect-free images are trivial to collect and real defects are scarce. The model sees thousands of normal frames and a handful of the failure you care about. Left alone, it learns the easy, lazy answer: predict normal and be right almost always. That is class imbalance, and it means your accuracy can look excellent while the model misses the exact event that justifies the project. The score rewards the wrong behavior.
So we design against drift from the start. We collect images that span your real conditions, not your best ones. We weight training so the rare class is not drowned out. We measure on data that looks like next month, not last quarter. And we hand your team a way to spot the drift and retrain before it costs you, because the environment will change, and a model that cannot be retrained is a model with an expiry date.
How we build it
Trained on your environment, not a benchmark.
We build around the images you actually have, not the clean ones from an open-source dataset. These are the patterns we work with.
Custom dataset from your real images
We start with your footage, your defects, your objects. A model trained on stock photos of your product category is not a model trained on your product. We label, augment, and train on the data that looks like what the model will actually see. Augmentation is not decoration here. We deliberately throw the model the conditions it will meet on the line: rotation for off-angle mounts, brightness and contrast swings for the light that never holds steady, blur for motion on a moving belt, occlusion for the part that is half hidden. The point is to teach the model your worst case before your worst case teaches it.
Architecture selection
YOLO for real-time detection. EfficientDet for accuracy-constrained use cases. Custom heads for multi-task outputs. We pick the architecture that fits your latency and accuracy requirements, not the one that looks good on a leaderboard. A one-stage detector reads the whole frame in a single pass and answers fast, which is what you want when frames arrive faster than a human can blink. A two-stage or higher-capacity model spends more compute to squeeze out accuracy when a miss is expensive and you can afford the milliseconds. There is no free lunch between the two, so we match the choice to your actual frame rate and your actual cost of being wrong.
Edge and camera-native deployment
If the model needs to run on-device without a cloud round-trip, we handle quantization, ONNX export, and edge runtime integration. The same model you validated on a server can run on a camera with an embedded processor. Quantization shrinks the model from full-precision floats down to smaller integers so it fits the memory and the chip you actually have, and we check accuracy after the shrink, not before, because the version that runs in the field is the only version that counts. A cloud GPU has room to be lazy. An embedded board does not, so we size the model to the hardware instead of wishing the hardware were bigger.
Annotation quality sets the ceiling
Your labels are the answer key the model learns from, and the model can never be more correct than its answer key. If two people draw the same defect three different ways, or disagree on where one object ends and the next begins, the model learns the confusion, not the rule. No architecture recovers from a noisy label set, so we define the labeling rules up front, check that different annotators agree, and treat ambiguous edges as a decision to make on purpose rather than a coin flip. Clean, consistent labels are boring work and they are the difference between a model that holds up and one that almost works.
Confidence thresholds tuned to your cost of error
A false positive in a quality control system is a rejected good product. A false negative is a defect that ships. We tune thresholds to your cost of each error type, not to maximize a generic F1 score. These two errors trade against each other: push the threshold to catch every defect and you start flagging good parts; relax it to stop annoying the line and real defects slip past. Which way you lean is a business call, not a math default. A part that injures someone is not the same as a part that scuffs a box, so we set the dial to whichever miss actually hurts you and we say out loud what you are trading for it.
Evaluation on your real distribution
We measure on held-out images that look like production, not a curated test set. If the model performs on benchmarks but fails in your environment, the benchmark was the wrong test. We also refuse to hide behind a single accuracy number, because on imbalanced data a model can score high by quietly ignoring the rare class you care about most. So we report how it does on the failure case specifically, and we hold back images it has never seen from your real conditions, because a test the model has already memorized is not a test.
"A model that detects everything on a benchmark and misses your defects is not a model. It is a false sense of coverage."
What you get
A model that works in your environment.
Not a checkpoint from a leaderboard aimed at your problem and hoping. Every component documented so your team can retrain when the environment changes.
- A custom-trained detection or classification model on your labeled dataset
- Preprocessing and augmentation pipeline for your image distribution
- Evaluation report on held-out production-representative images
- Deployment package: ONNX, TensorRT, or framework-native depending on your runtime
- Confidence thresholds tuned to your specific cost of false positives vs false negatives
- Documentation so your team can retrain when your environment changes
Not sure if your images are clean enough to train on? Send us a few real samples and we will tell you straight.
Invoke usIs this the right call
When this fits.
Good fit
- You have cameras or image inputs and currently review footage or images manually
- Quality control, defect detection, or object counting is done by human inspection today
- You need real-time decisions from video or images without a cloud round-trip
- Your data looks different from what pretrained models were built on
Wrong call
- Your images are already labeled and you just need inference on a public model. We can point you at one.
- The decision does not need to happen in real time and a person reviewing it periodically is fine
- You have fewer than a few hundred images. There is not enough data to train on; collect more first.
Deployment and scale
On-device, on-server, or both.
Where the model runs depends on where the decision needs to happen. If latency matters, the model runs at the edge: on the camera, on a local GPU, on an embedded processor. If you need central coordination across many cameras, it runs on a server. We design for the right runtime, not the easiest one to demo.
Everything ships as a container. The same model runs locally during testing and in production without behavior changes. You are not debugging environment differences when something fails.
As your volume grows, the architecture handles it. One camera or two hundred, the inference pipeline scales without a rewrite. We size it for your current load and leave room for growth without over-engineering it now.
What we settle before we begin: your latency requirement, your acceptable error rate for each error type, and where the model is allowed to run. Everything else follows from those three.
Show us what your cameras are missing.
Tell us what you are trying to detect, where the cameras are, and what happens today when something goes wrong. We will tell you whether a vision model can catch it reliably, and what it should look like.