Automate Visual Inspection,Recognition, and Analysis at Machine Speed.
We build computer vision systems that see what your business needs to see — accurately, consistently, and at a scale that human visual review cannot match and cannot sustain.
Systems that see what people miss.
We build computer vision systems that see what your business needs to see — accurately, consistently, and at a scale that human visual review cannot match and cannot sustain.
Pre-built APIs work — until they don’t.
Google Vision, AWS Rekognition and similar APIs handle common use cases accurately and cost-effectively, and for standard requirements they are the right choice. Custom models become necessary when your objects or defects are domain-specific and absent from general training data, when accuracy requirements exceed what off-the-shelf models achieve, when images cannot be sent to external APIs for privacy reasons, or when latency demands on-device inference. The practical middle path: pre-built APIs as a first pass, custom models for the domain-specific layer where general models fall short.
Locating and identifying the objects that matter in a frame — at line speed, and at volumes no review team could sustain.
Pixel-level masks that separate regions of interest so they can be measured, counted, and inspected precisely.
OCR pipelines that turn scans, invoices, contracts, and forms into structured data your systems can actually query.
Automated visual inspection tuned to the defects specific to your line — not a generic catalogue of someone else’s faults.
Continuous analysis of live or recorded streams for events, movement, and anomalies as they happen.
Verification systems built with privacy, consent, and acceptable error rates defined before a single model is trained.
Models compressed and optimised to run at the point of capture, where sending images to the cloud is not an option.
Dataset creation and training from scratch when general-purpose models fall short of your domain.
A model is only as good as its data.
Accuracy is determined almost entirely by the quality and diversity of the training data — so we design data pipelines as carefully as model architectures.
Defining what images to collect, in what conditions, with what variation — so the model generalises to real-world conditions and not just the best-case images.
Bounding boxes, segmentation masks, and classification labels — managed with tooling, guidelines, and quality control that keep labelling consistent across large datasets.
Rare categories — unusual defects, infrequent object types — represented adequately, so the model does not quietly ignore them in favour of common cases.
Rotation, flipping, brightness variation, and synthetic generation to improve robustness and reduce the volume of labelled data required.
Test data the model never sees during training, held back for final evaluation. We report accuracy on held-out data — never on the training set.
We process images where they’re captured.
Many vision use cases have to run at the source — factory floor, vehicle, inspection point — because the cloud adds latency, bandwidth cost, and privacy risk that the use case cannot absorb.
Where we create the most value.
Vision pays for itself fastest where the visual work is repetitive, high-volume, and expensive to get wrong. These are the five places we see that most often.
Automated visual inspection at 100% coverage, at line speed, with consistent accuracy — replacing or augmenting human inspection wherever fatigue and variation are risks.
Barcode and label reading, package dimension measurement, item identification — automating the visual work that slows high-throughput operations down.
Shelf availability monitoring, planogram compliance, and queue management — giving operations teams real-time visibility into physical spaces.
Perimeter intrusion detection, crowd density monitoring, and access control — continuous monitoring at a cost and consistency manual review cannot match.
OCR and intelligent document understanding for invoices, contracts, and identity documents — structured data pulled out of unstructured visual content at scale.
Technologies we work with.
We pick the right tool for the job — here's what our teams reach for across every layer.
PyTorch
TensorFlowFrom brief to delivery.
Five stages from first call to a model running in production — each one with something you can see, test, or sign off on.
We define the visual task precisely — what the system must detect, the acceptable error rate, and the conditions it will operate in. This determines whether a pre-built API or a custom model is appropriate.
We assess your existing image data and design a collection and labelling strategy if more data is needed. Volume, quality, and diversity are assessed against the requirements of the chosen model architecture.
Models are trained on your specific data and evaluated against real-world conditions, not just benchmark datasets. Evaluation includes the failure modes most relevant to your use case.
Vision models are integrated into your existing systems — cloud API, edge device, or mobile app depending on latency requirements. We design the inference pipeline for your throughput needs.
Performance is tracked in production. When accuracy drifts due to changing real-world conditions, we retrain on new data without disrupting the live system.
Voices from the people we built for.
Dexsof rebuilt a system we'd been promised twice before. They shipped in eleven weeks what two other teams couldn't in eighteen months — and the code is the cleanest I've reviewed in a decade.
Genuine senior engineers. The kind who say 'we shouldn't build that' before we waste a quarter on the wrong thing.
We came for a 6-week prototype. Three years later they still run our core platform.
The team integrated seamlessly with our in-house engineers and elevated the entire output. We shipped on time and under budget.
The mobile app they built has a 4.8-star rating on the App Store. The UX work alone was worth every dollar.
From discovery to deployment in eight weeks. Dexsof is what a modern dev studio should look like.
We brought Dexsof in mid-project to rescue a failing build. They diagnosed the architecture problems in days, refactored the core, and had us back on track within two weeks — without losing a single feature.
Fast and reliable.
Every deadline hit, every estimate accurate. Working with Dexsof felt like having a co-founder with a full dev team behind them.
They picked up our legacy codebase that three other contractors had given up on, cleaned it up, and shipped three new features in the same sprint — all without touching the production schedule. Impressive discipline from the entire team.
Dexsof flagged two architectural issues that would have cost us six months.
Their design and engineering teams worked as one. The result was a product that looked premium and performed even better under load.
Common questions.
Anything not covered here, ask us directly — we answer within 24 hours.
Do we need a large dataset to get started?
Not always. Transfer learning allows us to fine-tune existing models with relatively small datasets for many common vision tasks. We assess feasibility in discovery and give you an honest estimate of the data volume required before any commitment.
Can the model run on-device or does it need cloud connectivity?
Both are possible. Edge deployment on NVIDIA Jetson, Raspberry Pi, or mobile is appropriate when latency is critical or cloud connectivity is unreliable. We recommend based on your specific latency, privacy, and infrastructure requirements.
How accurate does it need to be for production use?
That depends on the cost of errors. A quality inspection system with high false-negative costs needs different accuracy thresholds than a content moderation system. We define acceptable error rates in scoping — before training begins — so expectations are grounded in reality.
Can you process video in real time?
Yes — real-time video processing at 30fps or more is achievable on appropriate hardware. We design the inference pipeline for your specific throughput and latency requirements, whether that is a cloud stream or edge-side processing.
Let’s talk about it.
Tell us what you are building and we will get back to you within 24 hours — with honesty, not a sales pitch.