Computer Vision Development Services
Computer vision for medical, industrial and satellite imagery: detection, segmentation, super-resolution and real-time inference on edge devices.
Computer vision is where most applied AI projects either prove themselves or stall. A model that scores well on a benchmark can still fail on a new scanner, a different lighting rig or a cloudier satellite pass. We build vision systems for the conditions they will actually run in, and we measure them against the numbers your operators use to make decisions.
What we build
- Detection and segmentation for medical, industrial and remote-sensing imagery, including lesion detection in live ultrasound video.
- Super-resolution and denoising, including diffusion-based architectures that recover diagnostic detail from degraded inputs.
- Hyperspectral and multispectral analysis: spectral unmixing, spatial-spectral feature extraction and attention-based band reweighting.
- Registration pipelines that align images across time, modality or sensor.
- Real-time inference services for edge devices and on-prem GPUs, with quantization and kernel-level optimization where the latency budget demands it.
How an engagement runs
We work alongside your team from the first scoping call to handover. Each computer vision project runs in four phases:
- Scoping. We define the decision the model supports, the failure modes that matter and the acceptance metrics, before any training begins.
- Research. We audit the data, build a baseline and iterate on architecture. When the right approach is not yet in the literature, we develop it.
- Production. We harden preprocessing, package the model for the target hardware and integrate it with the systems that consume its output.
- Handover. Your team receives the code, weights, evaluation harness and documentation, and we work alongside them until they can retrain and operate the system on their own.
Where we have shipped it
- CT Scan Super-Resolution & Denoising: a diffusion-based architecture for simultaneous 4× super-resolution and denoising of low-dose CT, enabling diagnostic-quality images from 75% reduced radiation exposure.
- Real-Time Endometriosis Detection: lesion detection and segmentation on noisy endovaginal ultrasound video for Femnov, with temporal tracking at near-live framerate.
- Identity Verification Engine: the AI engine behind Oneex's identity-verification platform, processing 10M+ documents a year with MRZ OCR at 99.65% character accuracy, multi-spectral forgery detection and biometrics, fully on CPU-only edge hardware.
- Virtual Try-On Diffusion Model: a diffusion model built from scratch for Wearit on 3M+ labeled fashion images, serving try-on results in under 2 seconds.
- Industrial OCR Engine Audit: benchmarking and architecture audit of Tessi's high-volume OCR pipeline, with the roadmap for their next-generation platform.
- Hyperspectral Heavy Metal Detection: a pipeline that detects and quantifies soil contamination across mining regions at 94.2% detection accuracy. We wrote up the architecture in detail in our hyperspectral deep learning article.
For the clinical side of this work, see Healthcare AI. For remote sensing and industrial operators, see Industrial & Public Sector.
Deployment and stack considerations
Vision models are usually constrained by where they run, not how they were trained. We decide on the deployment target during scoping, because it shapes the architecture:
- Edge and real-time settings need a tight, predictable latency loop. We profile end to end, from frame capture to overlay, rather than measuring the model in isolation.
- On-prem deployments keep imagery inside your perimeter. We package models to run on your GPUs and integrate with your existing storage and review tools.
- Large-area and batch workloads, such as satellite scenes, are bound by throughput and preprocessing. Deterministic calibration, masking and co-registration matter as much as the network.
Across all three, we ship with an evaluation harness so you can track performance as data drifts, and we document the assumptions the model depends on.
If you have imagery and a decision that depends on it, book an intro call.
Frequently asked questions
What kinds of imagery do you work with?
We have shipped vision systems on medical imaging such as CT, on live ultrasound video, on identity documents, and on hyperspectral satellite imagery. The common thread is data that is noisy, expensive to label and unforgiving of errors, where off-the-shelf models rarely hold up.
Can your models run on the edge rather than in the cloud?
Yes. Real-time inference on the edge is a core part of the practice. Our identity-verification engine for Oneex runs fully on CPU-only edge hardware, with the full pipeline brought from 10s to 2s, and we design the model, quantization and serving path together so the latency budget is met on the target hardware.
Do you only build models, or the full pipeline?
The full pipeline. That includes ingestion and preprocessing, calibration, training, evaluation against the metrics your operators care about, and the inference service itself. We hand over code, weights and documentation your team owns.
How do you handle limited labeled data?
We scope the labeling problem early and design around it, for example with transfer learning across domains, physics-aware preprocessing and architectures suited to the signal. Our hyperspectral work used cross-domain transfer learning to generalize beyond the sampled sites.
Selected work
- Computer VisionAI Identity Document Verification Engine for OneexDesigned and built the AI engine of Oneex's identity-verification platform, which processes over 10 million identity documents a year in airports, banking, defence and sovereign infrastructure. OCR, multi-spectral authenticity analysis, print-technique forensics and biometrics, all running on CPU-only edge hardware.
- Remote SensingHyperspectral Heavy Metal DetectionBuilt a complete pipeline for processing hyperspectral satellite imagery to detect and quantify heavy metal contamination in soil across mining regions. Achieved 94.2% detection accuracy using custom spectral unmixing algorithms.
- Medical ImagingCT Scan Super-Resolution & DenoisingDeveloped a novel diffusion-based architecture for simultaneous super-resolution (4x) and denoising of low-dose CT scans, enabling diagnostic-quality images from 75% reduced radiation exposure.
- Healthcare AIReal-Time Endometriosis Detection on UltrasoundBuilt a real-time AI proof of concept for Femnov that detects and segments endometriosis lesions on endovaginal ultrasound video, with temporal tracking at near-live framerate, delivered as a containerized API.
- Generative AIVirtual Try-On Diffusion Model for WearitArchitected Wearit's core generative AI pipeline for virtual clothing try-on, from R&D to real-time production: a diffusion model built from scratch, trained on the Jean Zay supercomputer on 3M+ labeled fashion images, serving results in under 2 seconds.
- Document AIIndustrial OCR Engine Audit & Roadmap for TessiAdvised the Technical Direction of Tessi, a European leader in document processing, on the evolution of their industrial OCR engine: a benchmark against state-of-the-art models, an end-to-end architecture audit and a technical roadmap for the next-generation platform.
Industries we apply it in
- IndustryHealthcare & Life SciencesMedical imaging, real-time ultrasound AI and diagnostic systems built to hospital reliability standards. We have built real-time endometriosis detection on ultrasound video, CT super-resolution and registration pipelines with Siemens Healthineers and academic partners.
- IndustryIndustrial & Public SectorHyperspectral remote sensing, geospatial intelligence and edge vision for industrial operators and the public sector. Our models run on satellite data and on edge hardware at remote sites with little bandwidth.
Have a computer vision problem worth solving properly?
Book an intro call