Applied AI
Data foundations for AI: centralising and organising business data
Data foundations for AI are the work of centralising the information an AI system needs into a small number of governed sources of truth, and organising it so that every data domain has an owner, a sensitivity classification, permissions that reflect who should see it, and a retention rule that is enforced. Most AI projects that stall do so on data rather than models. Hexploits does this work one use case at a time, so each piece of clean-up unlocks something the business can use.
A named engineer replies within one working day. A written scope and an indicative price within two.
Who this is for
- Businesses whose customer, product and operational data is spread across SaaS tools, spreadsheets and shared drives with conflicting versions.
- IT directors about to roll out an AI assistant or Copilot who have not reviewed what it will be able to read.
- Operations teams whose AI pilot gave confident wrong answers on real data.
- Data protection leads who cannot say where personal data goes once AI is involved.
What you get
Deliverables, not slogans. Each one appears in the statement of work.
- A data inventory: every system holding business data, what it holds, who owns it and how it connects.
- An agreed source of truth for each data domain, with integrations so data is entered once and flows to where it is needed.
- Spreadsheets doing a system’s job turned into systems, and duplicate or superseded documents consolidated into one governed store.
- Sensitivity classification of the data, so you know which AI workloads may use it and where they may run.
- A permissions review and clean-up, so AI retrieval honours access that is actually correct.
- Retention rules enforced across sources, AI indexes and logs, and personal data minimised or redacted before it reaches a model.
Our engineers work across the major languages, frameworks and cloud platforms. We build on the stack you already run, with technology choices explained in writing before work begins.
How it is delivered
The same four stages as every Hexploits engagement, applied to this capability.
Stage 1
Map
Starting from the AI use case you want first, we map the data it needs: where it lives, who owns it, who can see it and how current it is.
Stage 2
Consolidate
Sources of truth agreed, integrations built, spreadsheets and stale documents retired, with reconciliation so nothing is lost on the way.
Stage 3
Govern
Owners named, data classified, permissions reviewed and retention enforced, recorded so a DPIA or an EU AI Act data-governance question can be answered from evidence.
Stage 4
Connect
The AI system is pointed at the governed sources, with retrieval filtered by permissions and every answer citing where it came from.
How success is measured
Every engagement agrees its measures and the measurement period in writing before work starts.
- Answer accuracy on an evaluation set built from real questions, before and after the clean-up.
- Records out of sync between systems, trending to zero.
- Folders and documents with excessive access, found and fixed.
- Data held beyond retention, trending to zero.
Proof
Case studies with numbers, and reviews linked to Google where they were left there.
JobVantage · Recruitment technology
99.9% availability and sub-100ms responses for a recruitment intelligence platform, at negligible infrastructure cost
PII Purge · Data protection
99.7% of personal data redacted and nothing retained: GDPR document redaction for CVs, subject access requests and AI pipelines
Director, Gradvisor
“Fantastic company and our development partner for Gradvisor, a social mobility careers platform with national ambitions. Extremely responsive and mission-oriented. Cameron owns any shortfalls humbly - rare for IT providers. Thinks like a client too.”
Director, JobVantage
“Working with Hexploits has genuinely been a pleasure, and I see them as my scaling partner for the foreseeable future as JobVantage grows. If you’re looking for a development team who combine strong AI/engineering capability with honesty, flexibility and a real interest in your business, I’d strongly recommend them.”
Questions we get asked
Why does data centralisation matter for AI?
Do we need a data warehouse or a data lake?
Will Copilot or an AI assistant expose data people should not see?
How does this relate to GDPR?
Do we have to fix all our data first?
Related
Sectors where this is most often needed
- Financial services and payments
- Recruitment and HR technology
- Compliance and regulation technology
- Charities and education
Reading
AI Readiness for SMEs and Enterprises: The EU AI Act, GDPR and Getting Your Data in Order
What AI readiness actually means for a 30-person firm and a 3,000-person group: the EU AI Act deadlines that apply now, how GDPR governs every AI system that touches personal data, and why centralising and organising your data decides whether AI works at all.
AI Sovereignty: What It Costs to Teach an AI Your Business
AI agents can triage support tickets, write your documentation, and answer questions from your own records - but only if you feed them your data. Here is what that trade actually costs, and how AI sovereignty lets you get the value without giving up control.
More in applied ai
AI readiness assessment for SMEs and enterprises
Internal AI assistants and copilots for UK businesses
EU AI Act readiness for UK businesses
AI document and email processing
AI decision support and forecasting
Agentic AI workflows with governance
MCP servers and Claude connectors: putting your product inside AI assistants
AI sovereignty: private and EU-hosted AI
AI governance and ISO 42001
AI in payments, credit and fraud under the EU AI Act
AI for recruitment: sourcing, screening and matching automation
EU AI Act for recruitment and HR: high-risk hiring AI explained
AI governance readiness for recruitment and HR businesses
Next step
Request a proposal.
Tell us about the system and the sector. A named engineer replies within one working day. A written scope and an indicative price within two working days of a short scoping call.