Capabilities · Technology · Computer Vision
A computer vision practice spanning multi-location retail intelligence, object detection, image classification, OCR, edge inference, and video analytics — production-deployed across multiple verticals and deployment surfaces.
02 · What we build
Six capability areas across deployment surfaces and use cases — from cloud inference to embedded cameras, from retail outlets to document processing pipelines.
Visbi is our computer vision product for multi-location retail operators — brand standards monitoring, shelf intelligence, customer signal, and operational consistency across outlets. Deployed at retail chains with 30+ locations, on a continuous loop.
Detecting and counting objects in images and video streams across verticals — retail shelves, manufacturing lines, logistics environments, field operations. The foundational CV capability underneath most of our vision engagements.
Classifying images at scale — product categorisation, defect detection, content moderation, condition assessment. Production deployments across multiple verticals using custom-trained and fine-tuned vision models.
Extracting structured data from unstructured documents — invoices, contracts, IDs, forms, receipts. Production OCR pipelines combining classical optical character recognition with modern vision-language models where the document complexity warrants it.
Running vision models on the device, not in the cloud — embedded cameras, edge inference servers, low-latency deployments. For use cases where bandwidth, privacy, or real-time response makes cloud-only inference unworkable.
Tracking objects, behaviour, and events across video streams — multi-camera environments, behaviour analysis, anomaly detection. Beyond the Visbi retail-outlet use case, into manufacturing floors, logistics hubs, and field operations.
03 · How we think about it
The deployment surface — cloud, edge, embedded — isn't a deployment detail; it's an architectural decision. Bandwidth, latency, privacy, and total cost of ownership all change with the surface. We design vision systems backwards from where they need to run, not forwards from where models are easiest to train.
Most CV value comes from systems that watch over time — comparing today against yesterday, this outlet against that outlet, this shift against the average. One-shot vision projects produce one-shot results. We build vision systems as continuous loops with feedback, drift detection, and improvement built in.
A trained vision model is the easy part. The pipeline around it — data labelling, evaluation, retraining cadence, edge deployment, error handling, the human-review loop — is where vision systems live or die in production. We build the system, not just the model.
04 · Tools and ecosystem
Models, frameworks, and deployment surfaces we've shipped with — named honestly across cloud inference, edge deployment, and the broader ML supporting stack.
Core vision frameworks, fine-tuned and adapted to client domain and use case.
Vision workloads shipped to whichever surface fits the use case — cloud, edge, or hybrid.
The MLOps layer that keeps vision systems healthy in production.
05 · FAQ
Four things we get asked early in computer vision conversations. The honest answers, so you can decide whether a working session is worth your time.
It depends on the use case. Cloud inference works for most analytics-style workloads where latency tolerance is in seconds and bandwidth is fine. Edge inference matters when latency requirements are sub-second, bandwidth is constrained, or the data can't leave the device for privacy reasons. We'll walk through the deployment surface decision in the first working session.
Both, depending on what the engagement needs. For common patterns — object detection, classification, OCR — we lean on fine-tuned versions of strong open-source models rather than training from zero. For specialised use cases or proprietary data, we build custom models. The right choice depends on data availability, accuracy requirements, and how much you want to invest in training.
Most CV projects fail not on modelling but on labelling. We build labelling pipelines as part of the engagement — tooling, guidelines, quality checks, and where it fits, semi-automated labelling using earlier model versions. The labelled dataset is yours at handoff, and the pipeline keeps producing new training data as the system runs.
Your team owns it. The trained models, the deployment infrastructure, the labelling pipelines, the evaluation framework — all of it transfers. For Visbi specifically (our productised retail vision platform), the engagement model is different and includes ongoing operations — we'll explain how it fits if multi-location retail is your use case.
Most vision projects ship a model. We ship the system that uses it.
07 · Ready when you are
A working session with a senior CV engineer — 30-45 minutes, focused on your use case, your deployment surface, your data realities. No commitment. We leave you with useful thinking either way.
Related technology capabilities
LLM integration, SLMs, fine-tuning open-source, predictive ML, and the engineering layer that makes models reliable in production.
TechnologyModern data foundations on Snowflake, Databricks, and dbt — the ground layer most vision and AI work runs on.
TechnologyNewAI agents that reason and act inside your systems — not just answer questions. Built on top of the data foundation.
TechnologyDeployment and operations across GCP, AWS, and Azure — including on-customer-infrastructure and sovereign cloud targets.
Founded 2020 · AI & ML engagements delivered across North America, Australia, and India · Partnerships and methodology details on About
