Scattered across systems.
Reports, scans, photos and logs sit in shared drives, inboxes and old systems.
Is our data ready for AI?
Your documents, records and images collected, cleaned and labelled by people who know the work, so a model has something good to learn from.
See it worked throughThe data is there.
It isn’t ready.
Most AI projects stall before the model. The data is scattered, inconsistent and unlabelled, and nobody knows how much of it is usable.
Messy and repeated.
The same record appears in different formats, with gaps and duplicates.
No labels to learn from.
A model needs examples marked up by someone who knows the work, and there are none.
What the AI reads.
Everything that could teach a model, gathered from where it lives and checked before anyone labels it.
In
- Documents and scans
- Photos and video
- Sensor and system logs
- Databases and spreadsheets
- Your experts’ knowledge
At work
- Sense Collects documents, images and logs from wherever they live, without moving your systems first.
- Understand Extracts and structures what each file contains, so it can be searched and sampled.
- Check Removes duplicates, finds gaps and measures how far the labels agree.
- Operate Runs the labelling with your experts and reports what is ready to train on.
Out
A labelled, checked dataset, and a clear view of what is still missing.
Every source assessed.
Every gap named.
Before any model is trained, the team sees what is usable, what needs cleaning and what still has to be labelled.
5 sources · 2 ready · 2 need work · 1 not usable
| Source | Content | Readiness | Detail | Items |
|---|---|---|---|---|
| SRC-1 | Line camera photos, 2023 to 2024 | Ready | Labelled by inspectors; labels agree 96% of the time | 18,200 |
| SRC-2 | Phone photos from quality audits | Needs cleaning | 2,300 duplicates and blurred images to remove | 6,400 |
| SRC-3 | Rejected-part reports | Needs labelling | Defect type written in free text; needs coding | 3,150 |
| SRC-4 | Supplier photos | Not usable | Different lighting and no part numbers | 1,900 |
| SRC-5 | Defect type definitions | Ready | Agreed with the quality team | 42 types |
Not usable
Different lighting and no part numbers.
SRC-4 · Supplier photosNeeds cleaning
2,300 duplicates and blurred images to remove.
SRC-2 · Phone photos from quality auditsNeeds labelling
Defect type written in free text; needs coding.
SRC-3 · Rejected-part reports
Illustrative example. Sources, counts and defect types are fictional.
Built for real-world data.
The AI does the heavy lifting. Your experts decide what the labels mean.
Labelled by people who know the work.
Your inspectors, engineers or analysts set and check the labels.
How Understand works ↗Quality measured, not assumed.
Duplicates, gaps and label agreement are measured before training.
How Check works ↗Your data stays yours.
Collected and labelled inside your boundary, and only ever used for your models.
Deployment options ↗Same records.
Other questions.
The documents and data behind this work answer other questions too.
Can we trust these numbers?
Data quality
Find duplicates, gaps and records that disagree across systems.
See how →Is this part defective?
Defect inspection
Catch defects on the line, not at the customer.
See how →How did the North region do last quarter?
Ask your data
Plain-language answers from your own databases, within each person’s access.
See how →Bring us a sample of the data you want a model to learn from.
We’ll assess it and show you what is ready, what needs cleaning and what still needs labels.
Talk to us →LondonDiscovery AI Limited · United Kingdom
HyderabadReDscvry Technology Private Limited · India
ChennaiReDscvry Technology Private Limited · India