Practice area
Computer Vision Services
DataWise provides computer vision services: visual inspection, counting, segmentation, and measurement from images and video, validated on your imagery first.
DataWise provides computer vision services for industrial and geospatial work: visual inspection and quality control, counting, segmentation, and measurement from images and video. We have built and demonstrated a segmentation system that turns the costliest phase of geospatial vision work into a supervised, minutes-long task, and we architect vision workloads for closed networks where imagery cannot leave the building. Every engagement starts with feasibility on your own imagery, before you commit to a build.
What computer vision services does DataWise offer?
DataWise offers five computer vision services, each aimed at an output your team acts on: a measurement, a count, or a pass or fail decision, not a demo. The table names where our own record stands on each, in the same verbs we use across this site, and the question each one answers.
| Service | Where our record stands | The question it answers |
|---|---|---|
| Aerial and satellite image segmentation and measurement | Built and demonstrated, on airfields | Where are the features in this imagery, and how large are they? |
| Correction and annotation tooling | Built and demonstrated, as the correction UI of the aerial system | Can a professional check, fix, and sign off on what the model produced? |
| Vision inside closed networks | Architected and specified, as part of a zero-egress AI enclave | Can object detection and video analysis run with nothing leaving the network? |
| Counting from images and video | Consulted and advised: a vision-based counting roadmap for a plant nursery | How many plants, items, or objects are there, without a manual count? |
| Visual inspection and quality control | Offered feasibility-first; our built vision work so far is geospatial, not factory-floor | Is this part good, and if not, what is the defect? |
The deepest of these is the aerial system: a SAM-based (Segment Anything) system that automatically segments airfields in satellite and aerial imagery and measures them with precision tooling, wrapped in a purpose-built UI with manual correction and editing, object addition, and report generation. The closed-network work was for a security-sensitive organization, where models run entirely inside the network. The counting roadmap came out of agricultural planning and procurement work. The common thread is supervised automation: the model does the heavy lifting, a person corrects and approves, and the output is a report your organization can act on.
By industry, the same services show up as inspection coverage that sampling cannot reach in manufacturing and industrial operations, counting and monitoring in agriculture, and vision that runs inside the perimeter for defense and critical infrastructure. Geospatial teams that measure from aerial or satellite imagery are the closest match to what we have already built.
Two adjacent needs sit in other practices. Reading scanned documents and forms is document AI, where we design pipelines that extract structured fields from scans for a defined decision. Vision on networks that never touch the internet is part of secure and air-gapped AI, covered there end to end.
Why “supervised, minutes-long” instead of “fully automatic”?
Because in real inspection and mapping work, the expensive part is not detection, it is trust. A segmentation that is 95% right and uneditable is useless to a professional who signs off on the result. DataWise builds the correction loop as a first-class feature: automatic segmentation and measurement first, then manual correction, editing, and object addition in a purpose-built UI, then report generation. The person stays in charge; the machine removes the hours. This is also the honest answer on model quality: vision models earn their place through validation on your imagery, not through vendor accuracy claims, and the tooling assumes the model will sometimes be wrong.
What does a computer vision engagement look like?
A computer vision engagement follows the same staged arc as every DataWise project, with a real decision point after each stage, and the person who scopes it is the person who builds it:
- Characterization: define the visual task (segment, measure, count, detect), the imagery you actually have, and the success metric.
- Feasibility on your real imagery: we validate the approach on your data before you commit to a build. If your imagery cannot support the accuracy the decision needs, we say so at this stage.
- Build with a human-in-the-loop UI: automation plus correction tooling, so professionals adopt it rather than fight it.
- Deployment where the data lives: cloud, on-premises, or fully inside a closed network for organizations whose imagery cannot go out.
- Ownership: models, code, and tooling are yours.
How long it takes. It depends on your data readiness and access to imagery. As a rough orientation, characterization typically runs a few weeks, and a feasibility stage on real imagery is typically measured in weeks to a few months, not days and not years.
Where the model runs. Options include your cloud account, servers on your premises, a closed network with no internet connection, or hardware next to the camera when latency rules out a round trip to a server. The choice follows from the task’s latency, connectivity, and data rules, and it is settled during characterization rather than assumed.
How pricing works. Vision work is priced per stage, like every DataWise project. The first stage is a bounded, fixed-price piece of work, and it ends with a written scope and a firm quote for the next stage. We do not publish a rate card, because scope drives cost. The factors that move the price are the ones on our AI pricing page: data readiness, which for vision means the imagery and labels you already have; how many decisions the system serves; how deeply it integrates with your systems; and whether it must run inside a closed network.
How to start. A short call, usually 20 to 30 minutes, about the visual task and the imagery you have today. If it makes sense to continue, you receive a written proposal for a characterization stage. You do not need a specification or a labeled dataset to have that conversation: tell us about the visual task.
Proof
Showcase cards
automated segmentation and measurement for aerial imagery (built and demonstrated), secure, fully-isolated AI infrastructure including self-hosted vision object-detection (architected and specified, with a built and demonstrated assistant), plus advisory work on a vision-based counting roadmap for a plant nursery.
See selected workClosest comparable work
the closed-network vision capability connects to our secure AI practice; there is no separate published vision engagement, and we prefer saying that plainly to inflating one.
Industry evidence
Audi's Neckarsulm plant uses AI on welding-control data to analyze roughly 1.5 million spot welds per shift, replacing sampled ultrasound checks of about 5,000 welds per vehicle, per Audi's press release. The category is proven close to home too: Siemens acquired Israeli machine-vision-QC firm Inspekto (Ramat Gan) in 2024, a system that trains on just 20 to 30 good samples, per Vision Systems Design.
FAQ
Do we need thousands of labeled images to start?
Often not. Modern foundation models like SAM start from strong general segmentation ability, which is exactly what our aerial-imagery system builds on; the feasibility stage tells you how much labeling your specific task actually needs before you spend on it.
Our imagery is classified or commercially sensitive. Can it stay inside?
Yes. DataWise architected vision object-detection as part of a fully isolated AI enclave, everything running inside the closed network with nothing leaving it. Deployment follows your data's constraints, not ours.
Can this run at the edge or on the line?
Deployment target is part of characterization. What we commit to is validated accuracy on your imagery and tooling your team adopts; where the model runs follows from the task's latency and connectivity constraints.