AI & Intelligent Systems
Eyes and ears for your operations
Cameras and microphones turned into structured, decision-ready data streams.
Factories, sites, stores, and call centres generate rivers of visual and audio signal that no human team can watch. Vision and speech systems convert that river into inspection results, safety alerts, counts, and searchable transcripts — when engineered for your real conditions, not lab conditions.
- Inspection coverage from sampled to total
- Safety incidents flagged in seconds, not shift reports
- Every customer call searchable and scored
Capabilities
What the work actually involves
Visual quality inspection
Defect detection on lines and assets, tuned to your tolerances and lighting reality.
Safety & compliance monitoring
PPE, zone intrusion, and unsafe-behaviour detection feeding your EHS workflows.
OCR & document vision
Forms, IDs, meters, and handwriting digitised at production accuracy.
Speech analytics
Call transcription, intent and sentiment tagging, and QA scoring across languages your customers speak.
Edge deployment
Models running on-site where bandwidth, latency, or privacy demand it.
Dataset and annotation operations
Collecting and labelling images from your own line rather than a public set, with an annotation standard, review, and a path to add the defect classes that appear after go-live.
Camera, lighting and placement design
Where the camera sits, what illuminates the part, and what the fixture holds — decided with the line running, because most accuracy problems are optical rather than algorithmic.
Deliverables
What you are handed.
Yours to keep, and written so another team could pick them up.
A model tuned on your conditions
Your lighting, your camera angles, your accents. Benchmark performance rarely survives a real shop floor.
An annotated dataset you own
The labelling is most of the cost and most of the value. It stays yours.
Edge or server deployment
Whichever the latency and connectivity actually allow, decided by measurement.
Accuracy reported by class
Aggregate accuracy hides the failure that matters. Reported per category.
We do not publish prices — scope drives them. Everything else, here.
- Starts with
- A feasibility spike on a sample of your real footage or audio
- Typical duration
- 10–18 weeks
- Who you get
- A CV/speech engineer, a data engineer, a platform engineer
- Commercial model
- Fixed-scope spike, then phased build
How to decide
What the answer depends on.
Two sets of conditions. Read both against your own situation — most organisations recognise themselves in one column within a sentence or two.
This is the right call when
- A visual or spoken check happens many times a day and costs attention.
- Conditions are consistent enough to be characterised.
- You can supply or capture representative examples.
A different approach fits better when
- You need certainty. These systems give confidence scores, not guarantees.
- Conditions vary wildly and no representative sample exists.
- The consequence of a miss is severe and no human review is possible.
Before you ask
Questions about computer vision & speech
Sectors
Where this comes up most.
The regulatory context and the systems already in the building change the build. Each sector page says how.
Next step
Bring us the problem. We will bring the architecture.
A discovery call takes forty-five minutes. You leave with our read on the problem, the shape of the system we would propose, and a straight answer on whether we are the right team for it.
- No sales deck
- An engineer on the call, not an account manager
- NDA before you share anything