Depth for every pixel. Objects that are hidden. Events nobody can stage. Ground truth no camera captures and no labeller can draw.
Models with unique capabilities
Trained on that data, so they do things other models cannot — at sizes that run on the cameras you already have.
Tools for computer vision products
The layer between a detection and the feature your customer actually sees. Bring your own model or use ours.
There is nothing to count in the photograph on the left. The crop is completely hidden under a closed canopy. We counted it anyway — from that same ordinary photograph.
Helping CV engineers say yes to differentiating features.
We don’t need your data to start. No collection, no labelled set, nothing captured first.
Rendering today. Shipping to customers now.Benchmark independently validated with the University of South Carolina · data, code and results published
what people bring us
You already know the feature. You’ve been told it isn’t possible.
“Read every trailer number in the yard, at speed.”
Intermodal, in motion, in weather, at angles nobody would choose. The footage exists. The labelled examples of every failure mode do not.
“Give me a crop model before the crop exists.”
The demo is in March. The season is in July. There is no version of that where waiting works.
“Add one more detection class to what we already ship.”
The product is live and the customer wants one more thing in it. Nobody has six months to go and collect it.
“Classify it in the field, not in the packhouse.”
Real light, real dirt, real occlusion, real variability. Every condition you'd never get a clean photograph of.
Every one of those is blocked on the same thing, and it isn’t the model.
It’s data that doesn’t exist yet — and the answer your team gives you is a season, a crew, six months of labelling, or simply no. We build it instead, in two to four weeks.
and this is what comes back
Running on ordinary footage, from cameras that were already there.
Score every driver, automatically.
Who's tailgating, how fast the gap is closing, and whether they could actually stop.
Needs metric depth · speed · tracking
Know where everyone is on site.
Position and distance for every person and vehicle in frame, on a live plan of the area. One camera, no calibration visit.
Needs full depth per pixel
Yield estimates without walking the field.
Per-region counts across the operation — including under a canopy that has closed over and hidden the crop entirely.
Needs density · annotated hidden objects
Vitals without a wearable.
Heart rate and breathing from across the room. No device to fit, charge, lose, or persuade anyone to wear.
Needs pose · sub-pixel motion
our work · Bauer
The crop was invisible. So we counted what was hiding it.
Once the leaves close over they completely cover everything below them. From above there is nothing left to photograph, so growers walk the rows, count what they can see, and extrapolate across the field.
We stopped counting the crop, and counted the canopy instead.
Built and demonstrated on a Bauer pivot at their research site in São Paulo — density read from ordinary RGB, with no specialised sensor.
Above: a closed canopy and the model’s density response. Below: a pivot field rebuilt from pivot-mounted cameras as it turns — captured, not rendered.
our work · Aigen
Seasonal availability is one of the biggest issues in agricultural computer vision.
Selling ahead of a season means last year’s pictures. Or Synetic.
No amount of budget or effort makes a season arrive early. A customer demo was on the calendar and the crop was out of season, so we built the crop instead of collecting it.
When the deadline matters, faster and more dependable than trying to capture it.
“We trained a model on 99% synthetic data that successfully deployed on our field robots, identifying weeds and triggering treatment without damaging a single crop plant. The data quality was solid enough that we went straight from synthetic training to real-world deployment with minimal friction.”
Yuri Brigance · Director of AI & SW, Aigen
the difference
Two to four weeks.
Without us
A season. Or six months.
And you get the conditions the weather gave you.
With us
Two to four weeks.
And you choose the conditions — including the ones you have never captured and the ones you could never stage.
Then a change is hours, not another year.
for the conversation with your team
Send this part to your engineer.
This is the list that decides whether the feature is buildable. It is not a list of things we do better. It is a list of labels that do not exist in a photograph, so no annotation budget of any size produces them.
Because we build the scene, we know all of it before the image exists.
Full depth, per pixel
Annotated hidden objects
Annotated optical flow
Surface normals
Identity through total occlusion
Density where nothing can be counted
Metric size and 6DoF pose
Rare events nobody can stage
how it starts
Four steps, and you own everything at the end.
30 minutes
A conversation
Tell us what you'd build. We'll tell you honestly whether it's a problem we solve — and say so if it isn't.
A few days
We scope it
Your objects, your cameras, your conditions, your edge cases. And how you'll know it worked.
2–4 weeks
You have it
Data, or a trained model, or both. Yours outright — no royalties, no restrictions.
3 months
We keep going
Iteration and retraining included, until it does what you needed it to do.
All we need from you
A description of the problem·What your cameras look like·How you'll measure success
Not your data. Not a labelled set. Nothing collected first. We use customer imagery for one thing only — the validation set.
Are you an engineer?
You probably want to try something rather than book a meeting. LYNX is our SDK — bring your own model, run it on your own footage, see what comes back. Free for 30 days, no procurement, no call.
Thirty minutes. Tell us what you’d build if the data already existed, and we’ll tell you straight whether we can get you there. No pitch, no junior account rep — a real conversation with someone who has built this.
