Make your dataAI-ready
AI plans stall when the data behind them is scattered, insecure or not GDPR-ready. We put that foundation in place first, so your AI projects go live and keep up with whatever comes next.
- A future-proof architecture
- One flexible architecture that supports every kind of AI project, so a new use case or a new AI tool builds on what is already there instead of starting over.
- A modern data platform
- Works with structured data such as tables and unstructured data such as documents, images and video, so your AI projects can use all of it.
- Usable results every phase
- You get usable value from the first week, and every phase ends with something your teams can work with.
The problem
The demo works, but your data is not ready
AI plans often start with something impressive someone saw in a demo. What gets underestimated is the work underneath: getting the data sorted, secure and GDPR-compliant before anything can go live. Meanwhile AI itself moves faster than most organisations can keep up with.
Common symptoms
The AI project stalls once it meets your real data
Scope keeps growing as every department adds its own wishes
Employees already use the next AI tool, such as Claude, while the company is still rolling out ChatGPT
What this costs you
Budget goes into a pilot that never reaches production
Staff put company data into AI tools nobody has approved
Every new AI tool starts the data work again from scratch
The AI Readiness approach
Every AI use case starts with checked data
Before anyone builds, we check three things for each AI use case and fix what falls short: the data itself, who owns it and may use it, and whether your documents and teams are ready to work with AI. We work in phases that each end with something usable.
Reliable data
Focus
Data extracted from your source systems into Azure Databricks, on an architecture designed for your organisation, and tested row by row against the source.
Key outputs
Data architecture design, extraction code, a configured Azure Databricks environment and test results.
Clear ownership and access
Focus
Each key dataset and definition gets a named business owner, and the rules on who may see and use which data are agreed for GDPR, audit and AI use.
Key outputs
Agreed definitions with named owners, and access rules set up in the platform.
Documents and people ready
Focus
Documents made usable for AI wherever you store them, and your teams trained to work with Databricks and with AI on your own data.
Key outputs
Connections to your document stores, Databricks courses and AI-usage courses.
Capabilities
What we deliver
The parts of the AI Readiness package, from on-site scoping to trained teams. You receive all of them, not only advice.
On-site scoping
Requirements for each AI use case gathered on site with the people who will use it, so the scope is agreed before any build starts.
Data architecture design
An architecture designed for the context of your organisation, so this use case and the next ones build on the same foundation.
Extraction into Azure Databricks
Code, usually Python, that extracts data from the source systems you already run, standard or custom-built; where no standard connector exists, we build one. Azure Databricks is configured to prepare and serve the data.
Row-by-row testing
Extracted data compared with the source database row by row, so problems show up in testing rather than in an AI answer.
Documents ready for AI
Documents made usable for AI, whether they sit in SharePoint and OneDrive or in another document store, so the use case can draw on files as well as databases.
Databricks and AI courses
Courses in using Databricks and in using AI, so your teams can run, check and extend what we deliver.
Where it creates value
Who benefits most
Practical value for the teams that carry the most complexity.
Head of Data
Accountable for the data the AI will run on, with a team that is already fully booked.
- Each AI use case scoped and its data checked before your team commits to it
- Senior experts who build alongside your team, not only advise
- Handover documents and courses so your team can run it afterwards
CIO / IT
Responsible for the platform, its security and the technology that is reaching end of life.
- A data architecture on Azure Databricks, designed for your organisation
- Modernisation planned around the systems and reports that run on the current data
- Ageing technology replaced as part of the same phased route
CFO / Finance
Needs AI to work from figures that match the source systems, and to know up front what each phase costs.
- Data tested row by row against the source systems
- Fixed-price phases with a usable result at the end of each
- Each KPI with one definition and a named business owner
Security and Compliance
Must be able to show who may see and use which data, under GDPR, audit and AI rules.
- Access rules agreed before data is used for AI
- A named owner for every key dataset
- Architecture and access decisions recorded in the handover documents
Business teams
The people who will use the AI and need to trust what it tells them.
- Their own questions and figures used in the acceptance tests
- Courses in working with AI on company data
- Clear definitions, so they know what each figure means
Less relevant if...
When another package or another starting point fits better.
- You have no AI use case in mind yet: start with an assessment from the AI & Automation overview
- Your data is already tested, owned and in use, and you need the AI application itself: see AI Company Brain
- You only need to find documents faster: AI Document Search is the more direct route
- You need extra senior capacity on your own project rather than a scoped package: see Senior Expert Placement
- You need to move data from an old system or platform to a new one, with no AI use case attached: see Internal Data Migration
How it works
A practical delivery approach
Every engagement follows a clear path from diagnosis to delivery and handover.
Scope
We gather requirements on site for each AI use case and check the data it depends on: the sources, their owners, their quality and who may use them.
Agreed scope per use case and a list of the data gaps to close before building.Design
We design a data architecture that fits your organisation, including how data reaches Azure Databricks and how access is controlled.
Data architecture design and a phase plan with a usable result per phase.Build and test
We write the extraction code, set up Azure Databricks, prepare the data and connect your document stores, such as SharePoint and OneDrive. Data extracted from your databases is tested row by row against the source.
Prepared data in Azure Databricks, with test results that compare it with the source.Acceptance
Your users test the result against their own questions and figures before it goes live.
User acceptance test results, agreed with the business owners.Hypercare and handover
After go-live the same senior experts stay available for questions, and we run the Databricks and AI-usage courses for your teams.
A live result, a demo, handover documents and trained teams.
Proof & trust
Results that speak for themselves
A future-proof architecture
One flexible architecture that supports every kind of AI project, so a new use case or a new AI tool builds on what is already there instead of starting over.
A modern data platform
Works with structured data such as tables and unstructured data such as documents, images and video, so your AI projects can use all of it.
Usable results every phase
You get usable value from the first week, and every phase ends with something your teams can work with.
Example engagement
What it looks like when it works
How an engagement like this typically runs. An illustration, not a client result.
Context
Example: a large organisation wants to use AI on its operational and financial data, but an earlier pilot stalled once it met the real data.
Challenge
The data sits in several source systems, definitions differ between departments, and nobody can say who may use which data for AI. The internal data team is fully booked with existing work.
Solution
We scope the first AI use case on site and check the data it needs. We design the architecture, extract the data into Azure Databricks and test it row by row against the source. Users run the acceptance tests, the same experts stay on during hypercare, and the teams follow the Databricks and AI-usage courses.
What you get
Data check per use case
What data the use case needs, where it lives, who owns it and what must be fixed first.
Tested data in Databricks
Source data extracted into Azure Databricks, with row-by-row test results against the source.
Handover documents and courses
Documents your team can run the platform from, and courses in Databricks and AI usage.
Context
Example: an organisation whose data platform is reaching end of life, while the business wants to start using AI on the same data.
Solution
We design a target architecture on Azure Databricks that serves the first AI use case, move the data it needs in phases, and test each phase row by row against the old platform before anyone switches over.
What you get
Target data architecture
An architecture design on Azure Databricks, with the order in which the sources move across.
Row-level test results
Test results per phase that compare the new data with the old platform, row by row.
Phase plan
Phases that each deliver something usable, planned so existing reports keep running while the AI use case moves forward.
Frequently asked questions
Your questions, answered
Yes, if you have one or more AI use cases in mind but are not sure the data behind them is reliable, owned and allowed to be used. It also fits when an earlier AI pilot stalled on the data. We start by scoping the use case before we recommend anything.
For each AI use case: which data it needs, where that data lives, who owns it, whether it matches the source, and who may see and use it under GDPR, audit and AI rules. Whatever falls short goes into the phases that follow.
A business sponsor, the people who will use the AI, access to the source systems and your Azure environment, and someone from IT or security who can decide on access rules. Your data team reviews and accepts the work; our senior experts do the build.
In fixed-price phases, each scoped up front and ending with live results, a demo and handover documents. The work can start with a fixed-price assessment. Advice outside a phase is billed by the hour. After hypercare, support can continue under Managed Services, as an SLA. You receive a quote once the scope is agreed.
That depends on the number of use cases and source systems. We set the phases during scoping, and each phase delivers something usable, so you see results along the way rather than only at the end.
No. Where systems and reports run on the current data, we plan around them and move in phases. Where technology is reaching end of life, its replacement can be part of the same route.
You own your data, your client-specific code and the documentation. SolvX keeps the rights to its generic building blocks; the contract sets out the details. The architecture design, the extraction code, the test results and the handover documents are handed to your teams, and the courses prepare them to run and extend it without us.
When the data behind your use case is already tested, owned and in use, and you need the AI application itself: then AI Company Brain fits better. If you only need to find documents faster, see AI Document Search. If you need extra senior capacity on your own project, Senior Expert Placement is the better route.
Next step
Where do you want to start?
Pick the path that matches where you are today. Both begin with a conversation.
Find out whether your data is ready for AI
Tell us which AI use case you have in mind. We look at the data it depends on and outline the phases to get it ready, as fixed-price work with a usable result at the end of each phase.
- Talk through your AI use case and the data behind it
- See which data gaps to close before anyone builds
- Get an outline of the phases and how the work is bought
No obligation. Typical response within 1 business day.
Start with an assessment
Not ready to commit to a pilot? A 2 to 4 week assessment maps your current state and identifies the highest-value starting point, with no obligation to proceed further.
- Map your current situation and pain points
- Identify 2 to 3 high-value improvement opportunities
- Get a recommended pilot scope and expected outcomes
No obligation. Typical response within 1 business day.
You might also be interested in
Related solutions
AI Company Brain
One linked company brain of documents and data, with a personal AI workspace for every employee and access by security group.
ExploreAI Document Search
Ask in plain language and get answers from your own documents, with the source document and page.
ExploreData Quality Monitoring
Alerts your data team when a pipeline or data test fails, with lineage that shows what an incident hit.
Explore