AI Transformation · Enterprise AI Integration
Data Foundations for AI
AI is only as good as the data it can reach. We find the data your use cases need, fix it at the source, and build the pipelines, quality checks, permissions and indexes that make it usable by AI systems.
Example: data readiness map
Sources
- ERPReady
- CRMDuplicate records
- DocumentsNo owner, permissions unknown
- Event dataReady
Foundations
- Pipelines
- Quality checks
- Catalog and lineage
- Access controls
Ready for AI
- Search indexGenerative AI
- Feature storeMachine learning
- Evaluation setsTesting every release
Each source is checked before an AI system depends on it, and gaps are fixed at the source where possible.
How to get started
Three steps to a plan for Data Foundations for AI
- 1Tell us what you needUse the project form or book a call. A few sentences about the goal is enough to start.
- 2Free technical consultationWe go through your goals, users, existing systems and constraints with you.
- 3Your planA detailed plan covering the right tech stack, architecture, timeline and budget. Then you decide.
What AI needs
Different AI, different data
Choose the kind of AI you plan to build to see what it needs from your data, and the gaps we most often find.
What it needs from your data
Your documents and knowledge sources, in their current versions, with their owners and access permissions.
How it is prepared
- Text extracted, including from scans
- Split into passages and embedded for search
- Kept in sync as sources change
What we build
- Ingestion pipelines from each source
- A search index with hybrid search
- Permissions mapped from the source systems
- Freshness and coverage monitoring
Gaps we often find
- Duplicate and outdated versions
- Documents with no owner
- Permissions that aren't recorded anywhere
AI-ready data
Six properties we build in
Data that has all six can be used by AI safely and repeatably, not just for the first use case.
- FindableCatalogued, with an owner and a description of what each field means.
- AccessibleReachable through pipelines or APIs, with access agreed with your security team.
- Quality-checkedCompleteness, validity, freshness and duplicates tested automatically, with alerts.
- Permission-awareAccess rules carried with the data into the search index or model, so AI shows people only what they may see.
- TraceableLineage from each source to the model or answer that used it.
- ProtectedPersonal and confidential data masked or left out according to your policies, with access logged.
Deliverables
What you receive
- A data readiness map for your priority use cases
- Ingestion pipelines with tests and monitoring
- Data-quality checks and alerts
- A catalog with owners, definitions and lineage
- Permission mapping from source to AI system
- A search index or feature store, as the use case needs
- Versioned training and evaluation datasets
- Runbooks and documentation in your accounts
Once the data is ready
Where the work goes next
- AI Product EngineeringGenerative AI DevelopmentBuild the assistant or search on the indexed, permission-aware content.
- AI Product EngineeringMachine Learning DevelopmentTrain models on the features and history now in place.
- Enterprise AI IntegrationAI Governance & Risk ManagementRecord which data each AI system uses, and the controls around it.
Questions
Questions about
Data Foundations for AI
Do we need a data warehouse or lakehouse first?
Not necessarily. We build what your first use cases need, on the platform you already have where it fits, and design it so the next use cases can reuse it.
How is this different from Data Engineering?
Data Engineering, in our Data & Analytics practice, builds data platforms for reporting and analytics. Data Foundations for AI prepares what AI systems need in particular: document indexes, features, permissions that follow the data, and evaluation sets. The engineering standards are the same.
Can you work with documents, emails and scans?
Yes. Text is extracted, including from scanned pages, cleaned, split into passages and indexed with its source's permissions, and the index is kept in sync as documents change.
What happens to personal data?
It is masked or left out according to your policies before it reaches an index or a model, and access to the raw data is logged.
How is a project priced?
Well-defined scopes are delivered as fixed-price engagements; when requirements are still evolving, we provide a dedicated team instead. Either way, the free technical consultation ends with a plan covering tech stack, architecture, timeline and budget, so you know the cost before work starts.
Enterprise AI Integration
Other services in this line
- AI Integration & Legacy ModernizationAI connected to your ERP, CRM and core systems through APIs, and the older systems that block it modernized.
- AI Governance & Risk ManagementPolicies, risk assessment, security and oversight for every AI system, organized by the NIST AI Risk Management Framework.
Ready to talk about Data Foundations for AI?
Start with a free technical consultation: a plan covering the right approach, architecture, timeline and budget for your AI work.
Start a project