Practice area
Computer Vision & Inspection
DataWise builds image and video analysis systems: automated segmentation, measurement, visual quality control, and counting, including in closed networks.
Updated:
DataWise builds image and video analysis systems for industrial and geospatial work: automated segmentation and measurement of aerial imagery, visual quality control, object detection, and counting. 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.
What vision problems does DataWise solve?
DataWise works on vision problems where the output is a measurement or a decision, not a demo. Built and demonstrated: 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. Architected and specified: vision object-detection as part of a zero-egress AI enclave for a security-sensitive organization, where models run entirely inside the closed network. Consulted and advised: a vision-based counting roadmap for a plant nursery, as part 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.
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 an engagement look like?
- 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.
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 workNearest flagship anchor
the closed-network vision capability connects to our secure AI practice; there is no separate vision flagship, 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.