Data

frAIme

AI needs data that is
joined up, reachable
and ready to use.

Cloud-native data fabric · company sources integrated · data made available without moving it out of its systems of record

Tell us about your case How it works

Data fabric · integrating data without moving it

Data fabricData stays in its systems of record

Cloud-nativeGrows with what the business needs

Ready for AIJoined up and available to machine learning and generative AI

More autonomy over the dataPeople reach the information themselves, with fewer requests to IT

Schematic view of the architecture: source systems, integration layer, and the applications that use the data.

The context

Company data is scattered.
Joining it up is the first
step to using it.

AI projects need data that is reachable, consistent and current. In most companies the information sits across different applications, with uneven quality and no common rules. Joining those sources together, without multiplying copies and manual work, is the first step to using the data in business processes and in AI projects.

Data spread across systems

Information sits across applications and departments. Without integration each area works on its own slice, and an overall view is hard to assemble.

The same data, many copies

Making data available usually means extracts, intermediate files and duplicated stores. That costs to maintain, and raises the risk that the copies no longer match the systems they came from.

Getting to the data is slow

Whoever needs to analyse the information usually depends on IT for extracts and preparation. The queue builds up and the analysis takes longer.

Information not yet ready for AI

Machine learning models and generative AI applications need data that is joined up, reliable and understood. If quality and meaning are not governed, the results cannot be trusted either.

How it works

From company sources
to data that is ready to use.

frAIme builds an integration layer between the company sources and the applications that use the data. It starts with a survey of the systems, goes on to integration and governance, and ends with the data available to BI, machine learning and generative AI.

The architecture

Integrating data
without moving it out of its systems.

The data fabric connects different sources and makes the data available to the applications that need it, without necessarily copying it into a new store. The information stays where it is and can be used by Business Intelligence tools, machine learning algorithms and AI applications.

Drag the handle: on the left scattered data, extracts and copies; on the right the same sources connected through one integration layer.

Before After

Schematic representation of the integration flows, not a specific installation.

What it changes

Data joined up.
More you can do with it.

Integrating data is not an end in itself. It exists to make the information reachable and usable by the people who analyse it, run the processes and build the applications.

Different sources brought into one coherent view

More autonomy over the information, fewer requests to IT

More of the data handling automated

Data available to advanced analytics, machine learning and AI applications

Inside the product

One architecture,
three points of view.

frAIme answers the people who analyse the data, the people who prepare it for the algorithms, and the people who run the infrastructure. Three points of view, one architecture.

Reaching the data without asking

Analysts reach what they need through one coherent view of the sources, without requesting a new extract each time or having to know the systems underneath.

One unified view of the sources
More autonomy over access
Less manual work, less room for error

Business view

Preparing data for the algorithms

The data fabric gives machine learning and generative AI data that is joined up and documented. Teams work from a consistent, reproducible base instead of rebuilding the sources for every project.

Consistent, reproducible datasets for development and testing
Quality and meaning of the data documented
A path from prototype to production

Data team view

Infrastructure that grows with the company

A cloud-native architecture lets the infrastructure evolve as the business needs, with scalability, security and flexibility in mind. It can proceed in steps, making use of the systems already there.

The infrastructure evolving in steps
Scalability and security considered from the design stage
Sources integrated without a mass data migration

IT and cloud view

Who it is for

Five situations where joining up the data changes things.

Situation	The difficulty	What frAIme contributes
A company growing	New systems and sources to join with the existing ones	New sources integrated step by step, making use of what is already there
Data split across departments	Each department uses its own data and its own reading of it	One integrated, coherent view of the information
Regulated sectors	Access, traceability and data quality have to be controlled	Governance over the information, without multiplying the stores
Building predictive models	The data the models need sits across different systems	The sources the models need, integrated
M&A	Systems and data estates to join after an acquisition	Data integrated step by step, before any work on the systems themselves

Frequently asked questions

The questions we are asked
most about frAIme.

Any other questions?

No. The data fabric integrates and uses the data while leaving it in its systems of record; it becomes reachable through one integration layer. Migrating the infrastructure is a separate decision, and can be done in stages.

A warehouse gathers data into one central store; a fabric connects different sources and makes the information reachable where it already lives. The two can coexist: the warehouse keeps its role inside a wider data ecosystem.

Yes. frAIme integrates information from the systems already in place without necessarily changing the applications that produce it. Any modernisation work is assessed separately, according to what the business needs.

From a concrete business need. We identify the information and the sources required, build the first integration and check the result on that use case. The architecture then grows, taking in new sources as other needs appear.

Yes. frAIme can be the integration layer under the other Add Value solutions: Tequery to query the information in plain language, TSV to analyse spend, and Calamìta to assess insurance exposure.

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Let’s start from your data
and build an architecture
that can grow.

Tell us about your case See the Data practice