AI & IT Solutions
Turn images, video, and documents into decisions you can act on.
Seaggle builds detection, segmentation, OCR and document-understanding, and video analytics systems, including the annotation strategy and edge deployment that decide whether they work outside a clean test set.
When this fits
Signals that this is the right conversation
Vision projects rarely fail on model architecture. They fail on data — inconsistent annotation, a training set that does not represent the real lighting, angles, or hardware, and no plan for the long tail. The modelling is the tractable part; getting representative data and shipping to constrained hardware is the work.
- Inspection, counting, or verification is done by eye at volume
- Documents arrive as scans and images and are re-keyed by hand
- Camera feeds are recorded for evidence but never analysed
- A vision model works in the lab and degrades on real site conditions
- Inference has to run on-site or on-device for latency, bandwidth, or privacy reasons
What Seaggle delivers
Deliverables, arranged by when they arrive
Each of these is an artifact you receive and can use without us in the room.
Assess
- Feasibility on real captured data, not a curated sample
- Annotation schema, guidelines, and inter-annotator agreement measured
- Hardware and capture review — lighting, placement, resolution, and throughput
- Baseline of the current manual process, including its actual error rate
Build
- Detection, segmentation, classification, or OCR and layout models fit to the task
- Active learning loop that targets labelling effort at the cases that are failing
- Augmentation and synthetic data where the long tail is genuinely rare
- Vision-language models for open-vocabulary and document-reasoning tasks
Operate
- Edge or on-premise deployment with quantisation and runtime optimisation
- Streaming inference, tracking, and event logic tuned to real throughput
- Review interface where uncertain predictions go to a person
- Monitoring for capture drift — the camera moved, the lighting changed, the product changed
Solution patterns
Shapes this work commonly takes
These are patterns rather than products. Select one to see how it works, where a person stays in the loop, and what we design against.
Document understanding and extraction
Invoices, forms, or certificates arrive as scans and are typed into a system by hand.
- How it works
- Layout-aware parsing, table and key-value extraction against a schema with per-field confidence, validation against business rules, then automatic commit or review queue.
- Human checkpoint
- Per-field confidence thresholds decide what a person checks.
- Risks we design against
- Format drift and unseen templates. Handled with schema validation, drift alerting, and corrections fed back into training.
Visual inspection and verification
Defects, completeness, or compliance are judged by eye and the judgement varies by operator.
- How it works
- Detection or segmentation on standardised capture, with decision thresholds tuned to the asymmetric cost of a miss versus a false alarm.
- Human checkpoint
- Borderline cases are routed for review, and the reviewer's call becomes training signal.
- Risks we design against
- Rare defect classes with few examples. Addressed with targeted collection, augmentation, and anomaly-based methods rather than forcing a classifier.
Video analytics and safety monitoring
Camera coverage exists but is only ever reviewed after an incident.
- How it works
- Detection and multi-object tracking, zone and dwell logic, event generation with clip evidence, and alerting into the channel the team already uses.
- Human checkpoint
- Alerts carry evidence clips so a person confirms before any action is taken.
- Risks we design against
- Alert fatigue from false positives, and privacy exposure. Managed with tuned thresholds, retention policy, and masking where identity is not required.
Representative workflow
End to end, with the checkpoints visible
A worked example of how the pieces connect in production. The static sequence below is the whole content — nothing is hidden behind animation.
- 01
Capture reviewed
Lighting, angle, resolution, and throughput assessed against what the model will need.
- 02
Annotation defined
Schema and guidelines written, then agreement between annotators measured before scaling.
- 03
Baseline model trained
On real captured data, evaluated on held-out sites, shifts, or conditions.
- 04
Failures targeted
Active learning directs labelling effort at what is actually going wrong.
- 05
Optimised for the hardware
Quantised and compiled for the device, with latency measured on that device.
- 06
Deployed with review path
Uncertain predictions go to a person rather than being forced to a decision.
- 07
Capture drift monitored
Because the camera will be moved and the lighting will change.
Representative example
Evaluation & controls
What separates production work from a demonstration
A demonstration proves something can happen once. These controls are how you know it keeps happening correctly.
Annotation quality measured
Inter-annotator agreement is measured before labelling scales. Disagreement between humans is a ceiling on what any model can learn.
Evaluation on held-out conditions
Split by site, shift, device, or batch — never randomly. A random split on correlated frames reports a number that will not survive deployment.
Asymmetric thresholds
Decision thresholds reflect the real cost of a miss versus a false alarm, set with the people who live with the consequences.
Capture drift monitoring
Input statistics are watched so a moved camera or changed lighting is detected as a data problem rather than blamed on the model.
Privacy by design
Masking, retention limits, and on-device processing where identity is not required for the task. Applied at design time, not retrofitted.
Human review on uncertainty
Low-confidence predictions route to a person, and their decision is captured as training signal rather than discarded.
Technology we work with
Named to explain the work rather than to imply endorsement. Tool choices follow the requirement, and we work with what you already run wherever that is sensible.
- PyTorch
- Detection & segmentation models
- Segment Anything
- Layout & document models
- OpenCV
- ONNX Runtime
- TensorRT
- NVIDIA Jetson
- Label Studio
- Vision-language models
Getting started
Where a first engagement begins
A feasibility study on your own captured data: annotation schema defined, a baseline model trained, and a clear read on capture quality, hardware fit, and whether the accuracy you need is reachable.
Discuss a vision problemQuestions
How much labelled data will we need?
Less than most teams expect to start, more than they expect for the long tail. Modern pretrained backbones and active learning mean a few hundred well-labelled examples often produce a usable baseline; the effort goes into the rare cases that matter. The feasibility study gives you a grounded estimate rather than a guess.
Can inference run on-site rather than in the cloud?
Yes, and often it should — for latency, bandwidth cost, or privacy. We profile the model on the target device, quantise and compile for it, and design around the hardware you actually have rather than assuming a data-centre GPU.
What about people appearing in the footage?
Where identity is not required for the task, we mask or process on-device so identifiable data never leaves the site, and we set retention limits explicitly. Where identity is required, that is a legal review before it is an engineering decision.
Discuss a vision problem
Bring the problem, the current environment, and what better should look like. We will tell you what is realistic before anyone signs anything.