Solutions | AI Data Foundation
Your agents are only as good as the data you let them see
A unified, secure, governed data foundation on AWS — Lake Formation, governed lakehouses, and access controls fit for regulated industries. The platform every other AI solution sits on.
Book a data foundation discovery call
AWS-native data foundation
Optional Databricks layerAI projects stall when the data foundation isn't ready
Data foundation problems split into two shapes — treating them as one is why most projects under-deliver. SMBs fragment their data across 20+ tools and never reconcile it. Regulated industries know where their data is — they just can't prove who's touched it.
Twenty databases. No single truth
Customer, finance, product and ops data live in separate tools. Analysts stitch reports together by hand, so every AI project starts with reconciliation.
FIX
We build one governed data layer so teams work from the same source of truth.
Governance can't prove access
Regulated teams often know where the data lives, but not who accessed it, changed it, or approved it for AI use.
FIX
We design permissions, lineage and audit trails before AI agents connect.
Agents can't reach the right data
Agentic builds and model workflows assume clean, governed and accessible data. Without it, every build gets delayed or blocked.
FIX
We create the data foundation every downstream AI solution can safely use.
A practical path from fragmented data to AI-ready foundations
3 phasesdata discoverygoverned foundationdownstream connections
Discovery — diagnose the flavour
A short discovery determines which flavour you have — sometimes both. Covers data sources, formats, access patterns, quality, and downstream use cases.
- Data source inventory — what exists, where, what format
- Access pattern audit — who reads what and through what tool
- Quality assessment — missing columns, schema inconsistencies
- Use case mapping — BI, agentic, model training, or all three
Output
Foundation flavour confirmed + scope agreed.
Foundation build — by flavour
Same AWS primitives in both flavours — S3, Lake Formation, Glue, Athena — but order of operations and depth of governance change completely.
- Flavour A: S3 golden landing zone, Glue crawlers for schema, Lake Formation for table/column access by role
- Flavour B: S3 with KMS encryption and domain separation, strict schema governance, Lake Formation + fine-grained row-level access, every read logged
- Both: Athena + QuickSight for querying and dashboarding, optional Databricks layer
Output
Queryable and governed data foundation.
Connect, govern & measure
Wire the downstream — BI tools, agentic systems, model training jobs. Stand up access governance. Establish the data quality scorecard.
- Downstream connections — BI tools, Bedrock knowledge bases, agentic system data access
- Agent access via permissioned views only — sensitive data never leaves the perimeter
- Data quality scorecard — completeness, freshness, schema-conformance per source
- Audit trail standing query — every read attributable and reviewable on demand
Output
Downstream connections verified + data quality scorecard live.
Discovery — diagnose the flavour
A short discovery determines which flavour you have — sometimes both. Covers data sources, formats, access patterns, quality, and downstream use cases.
- Data source inventory — what exists, where, what format
- Access pattern audit — who reads what and through what tool
- Quality assessment — missing columns, schema inconsistencies
- Use case mapping — BI, agentic, model training, or all three
Output
Foundation flavour confirmed + scope agreed.
Foundation build — by flavour
Same AWS primitives in both flavours — S3, Lake Formation, Glue, Athena — but order of operations and depth of governance change completely.
- Flavour A: S3 golden landing zone, Glue crawlers for schema, Lake Formation for table/column access by role
- Flavour B: S3 with KMS encryption and domain separation, strict schema governance, Lake Formation + fine-grained row-level access, every read logged
- Both: Athena + QuickSight for querying and dashboarding, optional Databricks layer
Output
Queryable and governed data foundation.
Connect, govern & measure
Wire the downstream — BI tools, agentic systems, model training jobs. Stand up access governance. Establish the data quality scorecard.
- Downstream connections — BI tools, Bedrock knowledge bases, agentic system data access
- Agent access via permissioned views only — sensitive data never leaves the perimeter
- Data quality scorecard — completeness, freshness, schema-conformance per source
- Audit trail standing query — every read attributable and reviewable on demand
Output
Downstream connections verified + data quality scorecard live.
What you get from the programme
One source of truth: “What's the number?” gets answered in minutes, not days.
Audit-ready by design: Permissions, lineage and access logs built into the foundation from day one.
Agents that work: Agentic systems, productivity tools and model training jobs can plug into governed data.
No vendor lock-in: AWS-native primitives, with Databricks only where it genuinely earns its place.
One foundation. Two paths: unify the data or govern the access
We don’t treat every data problem the same. Some teams need to unify fragmented data across tools. Others need governed access, lineage and auditability for regulated AI. The foundation uses the same AWS primitives, but the priorities change by context.
Clean, governed data is the platform every AI build stands on.
Every AI solution depends on data that is accessible, permissioned, traceable and usable. The shape of the foundation changes depending on whether the main challenge is fragmentation, governance or downstream AI access.
WHAT WE PRIORITISE
Unification path
S3 landing zone · Glue · Athena · QuickSight
Governance path
Lake Formation · KMS · row-level access · audit logs
AI access
Permissioned views · Bedrock knowledge bases
Data quality
Completeness · freshness · schema conformance
Financial AI needs governed data before it needs more models.
FinTech teams need data foundations that make customer, transaction and risk data usable without losing control. We prioritise permissions, lineage, auditability and safe access for regulated AI workflows.
WHAT WE PRIORITISE
- Customer data access
- Transaction data lineage
- Risk and fraud datasets
- Row-level permissions
- Audit-ready data views
Sensitive data needs access control before AI access.
Health Tech teams need AI-ready data foundations that protect patient, clinical and operational data while still making it usable for approved workflows. We prioritise privacy, lineage and controlled access from day one.
WHAT WE PRIORITISE
- Patient data controls
- Clinical data lineage
- Approved dataset access
- HIPAA / GDPR alignment
- Safe AI-ready views
Your product data should become operating leverage.
B2B SaaS teams often have customer, product, support and revenue data spread across tools. We build a governed foundation so AI agents, reporting and product workflows can use the same trusted data layer.
WHAT WE PRIORITISE
- Customer data unification
- Product usage data
- Support and success signals
- Revenue and retention views
- AI-ready data access
Clean, governed data is the platform every AI build stands on.
Every AI solution depends on data that is accessible, permissioned, traceable and usable. The shape of the foundation changes depending on whether the main challenge is fragmentation, governance or downstream AI access.
WHAT WE PRIORITISE
Unification path
S3 landing zone · Glue · Athena · QuickSight
Governance path
Lake Formation · KMS · row-level access · audit logs
AI access
Permissioned views · Bedrock knowledge bases
Data quality
Completeness · freshness · schema conformance
Choose the right data foundation engagement
Unification Sprint
3–6 weeks: ingest the top sources, set up the lake, wire query and BI. Land one usable analytics use case as proof.
- Top sources ingested and standardised
- Lake and Athena query layer live
- QuickSight dashboard with one confirmed use case
Cost
Fixed fee
Duration
3–6 weeks
Governed Foundation Programme
Phased — discovery → governance build → controlled access. Designed to satisfy security review and pass audit on the first attempt.
- Full access control and permission architecture
- Audit trail configured and validated
- Sensitive data isolation verified
- Standing governance and data quality scorecard
Cost
Fixed fee
Duration
6–10 weeks
AI Data Foundation for Agents
The foundation, scoped specifically to unblock an agentic AI build. Co-delivered with our Agentic AI Solutions team.
- Data access scoped to agent requirements
- Permissioned views for agent read access
- Knowledge base connections verified
Cost
Fixed fee
Duration
Scoped to build
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Is your data ready for the AI you want to build?
Book a focused 20-minute conversation. We'll help you understand whether your data challenge is fragmentation, governance, downstream AI access — or all three.
Why talk to us:
Data source and access pattern assessment
AWS-native data foundation design
Governance, lineage and audit trails
Agent-ready data access without unsafe exposure
Start with a focused 20-minute conversation about your goals — no pressure, no commitment.