Computer Vision, Speech & Edge AI

Data the business already owns becomes usable input

Models read images, documents, audio and video directly, so archives and existing camera feeds stop being storage cost and start feeding a process.

Human attention moves to the cases that need judgement

The system handles routine perception across the whole volume; people handle the exceptions, the disputes and the appeals it routes to them.

The same criteria are applied to every item

A model does not tire, change shift or drift in strictness over an afternoon, so variation between inspectors or reviewers is removed from the routine cases.

Work that was impossible at volume becomes possible

Every unit can be inspected rather than a sample, and every call reviewed rather than a handful, because the marginal cost of one more item is compute rather than labour.

Cost is contained because the architecture is sized to the problem

Pre-trained backbones, smaller task-specific models, distillation and quantisation are proposed before large architectures, and a classical method is proposed where it is sufficient.

The system can run where the data already is

On-premises and edge deployment keep confidential images, recordings and documents inside the environment where bandwidth, latency or confidentiality require it.

Qualify

Define the task, and test whether a rule, classical model or ordinary analytics already answers it.

Feasibility finding, including the simpler-method result

Data

Assess the raw material: volume, variability, labelling state, consent and what more would cost.

Data and labelling assessment

Architect

Select the architecture against data type, latency, hardware, explainability and where it must run.

Architecture decision with trade-offs stated

Train

Fine-tune or train with tracked experiments, reproducible configuration and recorded data snapshots.

Trained candidate models

Evaluate

Test on conditions held out by site, device or period; analyse errors by class and input quality.

Evaluation report and model card

Optimise

Quantise, prune or distil and convert to the target runtime until it fits the hardware and the latency budget.

Deployment artefact with measured cost per item

Deploy

Release to cloud, on-premises or edge with human review and escalation where the consequence warrants it.

Running inference service with review workflow

Monitor

Watch input drift, confidence distribution and escalation rates, and retrain on the agreed trigger.

Monitored system with a retraining path

Our board has asked for deep learning. Will you just build it?

Only if the problem needs it, and establishing that is the first part of the engagement rather than an afterthought. A simpler method is tested against the same task and the result is recorded, so the decision is evidence rather than opinion. If ordinary analytics or classical machine learning answers the question, ARRIX will say so and propose that instead, even though it is a smaller piece of work, because deep learning carries costs that continue long after the build: specialist skills, hardware, retraining and a system that is harder to defend when someone challenges an output. Where the assessment shows the signal really is in images, language, audio or sequence, the deep-learning system is built and the reason it was necessary is written down.

How much labelled data do we need?

There is no universal number, and anyone offering one before seeing the task is guessing. It depends on how distinct the classes are, how rare the important case is, how much variation your environment produces, and whether a pre-trained backbone applies to your data type, which for common image, text and audio tasks reduces the requirement considerably. The practical method is to label a small representative pilot, measure whether performance still improves as the set grows, and stop when it stops improving. The labelling programme itself, including guidelines and quality checks, is covered by Training Data & Feature Engineering.

Can it run without sending our images or recordings to a cloud service?

Yes, and where confidentiality, residency or bandwidth require it that becomes an architecture input rather than a preference. Running on your own servers or on a device at the site constrains model size and latency, which is why the deployment location is decided before the architecture rather than after. Quantisation, pruning and distillation are used to fit the model to the hardware available, and the trade-off against accuracy is measured and shown to you rather than assumed. In an edge deployment only events and results leave the site, and the raw footage or audio stays where it was captured.

How accurate is it?

Accuracy is a property of a specific model on a specific dataset under specific conditions, not a property of a technique, so a number quoted before your data has been seen would be invented. What is agreed in advance is the evaluation protocol: which data is held out and by what criterion, which errors are counted, and how the segments are broken down. Results are reported per class and per condition rather than as one headline figure, because a single average hides the failure that matters. The decision threshold is yours to set, because it trades missed cases against false alarms and only the business can price that.

What happens when the model sees something it has never seen before?

That behaviour is designed rather than left to chance, because a model that answers confidently on an unfamiliar input is a design failure. Confidence thresholds and out-of-distribution checks route uncertain inputs to a human queue instead of forcing an answer. Escalation rates are monitored, and a rise in them is treated as a signal that conditions have changed and retraining may be due. Where the consequence of an error is material, human review stays in the loop by design and is removed only once the failure modes have been characterised, if at all.

Is this the same as the generative AI you offer?

No, although they use related architectures and are often combined in one system. This offering builds task-specific models that recognise, extract, classify or detect, and their output is a structured result you can act on and measure. The generative offerings build applications that produce language grounded in your knowledge, and they are evaluated in a different way. A typical combined system uses a model from this family to read a document or a recording, and a generative one to summarise or answer questions over what was extracted.