How it works

Interiu turns scattered data into working intelligence

Interiu connects documents, models, communications and legacy expertise into controlled analytical workflows — so each case supports the current output and strengthens future work.

Interiu — internal intelligence unit
Process

Five steps to a repeatable workflow

01
Define the task
We agree the problem, the sources in scope and the expected output before any data moves.
02
Configure the workflow
Data and questions are structured for analysis, with access controls set to match the sensitivity of the case.
03
Review the output
Findings are checked against source material and validated by your team before they are used.
04
Refine and reuse
Workflows are adjusted from real use and applied across similar matters as a repeatable analytical layer.
Data handling

Built for sensitive, high-stakes information

Interiu is used for confidential documents, transactions, communications and internal data. Access, processing and deployment are configured to the sensitivity of each case.

01
Data residency options
EU or US infrastructure and model options based on case requirements.
02
No training on client data
Data is used only within the agreed workflow and not retained for general model training.
03
Controlled access
Access controls can be configured by case, user, data source and workflow, where required.
04
Source-linked outputs
All findings remain connected to underlying material.
Deployment

Deployment that matches your sensitivity profile

01
Managed environment

Interiu hosts and operates the workflow with agreed access controls — usually the fastest setup.

02
Restricted data access

Workflows can be configured to limit what data is processed, stored or accessed by Interiu.

03
Client-side deployment

Deployment inside a client cloud can be discussed for cases with specific infrastructure requirements.

Start with a real case

Bring us the work that slows your team down

If your team is spending time reviewing documents, emails, transactions or models, we can show what that work looks like with an on-demand intelligence layer.

Start with one real case and a clear output.

Discuss your case ↗

Have questions about data handling, deployment or use cases? Read the FAQ.