# Data readiness · Use cases · Dscvry

> Data readiness: data collected from where it lives, cleaned, de-duplicated and labelled by domain experts, with a readiness check before any model is...

Source: https://dscvryai.com/use-cases/data-readiness/

# 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 through ·

1. [Home](https://dscvryai.com/)
2. [Use cases](https://dscvryai.com/#use-cases)
3. Data readiness

## The 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.

### Scattered across systems.

Reports, scans, photos and logs sit in shared drives, inboxes and old systems.

### 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

1. [Sense](https://dscvryai.com/technology/sense/) Collects documents, images and logs from wherever they live, without moving your systems first.
2. [Understand](https://dscvryai.com/technology/understand/) Extracts and structures what each file contains, so it can be searched and sampled.
3. [Check](https://dscvryai.com/technology/check/) Removes duplicates, finds gaps and measures how far the labels agree.
4. [Operate](https://dscvryai.com/technology/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.

Readiness check · The reasoning ·

Readiness check · Inspection photos for a defect model Illustrative

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 |

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
https://dscvryai.com/technology/understand/

### Quality measured, not assumed.

Duplicates, gaps and label agreement are measured before training.

How Check works
https://dscvryai.com/technology/check/

### Your data stays yours.

Collected and labelled inside your boundary, and only ever used for your models.

Deployment options
https://dscvryai.com/#deployment

## 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 →
https://dscvryai.com/use-cases/data-quality/

Is this part defective?

### Defect inspection

Catch defects on the line, not at the customer.

See how →
https://dscvryai.com/use-cases/defect-inspection/

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 →
https://dscvryai.com/use-cases/ask-your-data/

More in Data
https://dscvryai.com/#use-cases

Every use case
https://dscvryai.com/use-cases/

## 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 →](https://dscvryai.com/contact/?topic=Data%20readiness%20inquiry)

**London**Discovery AI Limited · United Kingdom

**Hyderabad**ReDscvry Technology Private Limited · India

**Chennai**ReDscvry Technology Private Limited · India