Secure data pipelines for AI, delivered as an engagement.
A scoped piece of work that stands up privacy-first ingestion, data masking and governance in your AWS accounts. Engagement-based, fixed scope, clear handover.
Your team ends the engagement owning the pipelines, the policies and the operational knowledge.
What you get
Automated data masking
Production RDS snapshots scrubbed with your masking rules and delivered as clean staging data. The safest model boundary starts before inference: sensitive fields should never enter prompts, embeddings or training sets unless your policy allows it.
Source-to-AI pipelines
Connectors for your existing databases, event streams, SaaS exports and cloud stores. Normalised, versioned and reusable.
Governance built in
Classification, lineage and access policies as code in your Git. Audit-friendly from day one.
Quality at ingest
Schema validation, drift detection and bad-record isolation. Bad records and sensitive fields are stopped before they reach downstream model calls.
Cost-aware design
Storage, compute and transfer shaped to the workload. No accidental petabyte bills.
Handover built in
Documentation, runbooks and training so your team operates what we built.
Engagement-based. Fixed scope. Knowledge transfer.
Discovery
- Current-state mapping of all data sources
- Scoring data sources for AI-readiness
- Gap analysis against your target architecture
- Governance review against ISO 27001, APRA CPS 234 or your chosen frameworks
- Prioritised roadmap with effort and risk estimates
Design
- Target ingestion patterns and pipeline blueprints
- Data masking rules for production-safe staging environments
- Governance model committed to Git
- Pipeline orchestration design
- Advisory on tool selection across your existing stack
Delivery
- Pipelines and automated masking built and tested in your AWS account
- Policies and governance committed to your Git repositories
- Dashboards for lineage and data quality
- Training sessions for your team
- Signed-off handover with runbooks
How it works
Discover. Design. Build. Hand over. A structured engagement with a clear end point and your team ready to operate.
Discover
We scope the data landscape, assess AI-readiness and find the gaps.
Design
Target pipelines, anonymisation rules and governance in Git. Reviewed with your team.
Build
Pipelines stood up in your AWS account. Tested end to end against production-shape data.
Handover
Documentation, runbooks, training. Your team takes it forward.
Audited and certified
AWS DevOps Competency
ISO 27001 Certified
AWS SaaS Competency
See what an engagement looks like against your data.
Walk us through your sources and target use case. We will scope the engagement on the first call.
Frequently asked questions
What is Data Integration for AI?
Data Integration for AI is a scoped engagement that builds data pipelines, masking rules, lineage, governance and handover for AI workloads in your AWS accounts.
Is this a subscription or a one-off engagement?
A one-off engagement with fixed scope. If you want ongoing operations afterwards, add DevOps as a Service or AI Factory.
How long does it take?
Typical engagements run six to twelve weeks, depending on source count and governance scope.
Can you work with our existing tools?
Yes. We integrate with dbt, Airflow, Fivetran, Glue, Kafka, Snowflake, Redshift and whatever else you already run. We add governance and safety, not a replatform.
Who owns the code after handover?
You do. Everything lives in your Git repositories and your AWS account. Our access is revoked on sign-off.
Do you work with financial services data?
Yes. We align the engagement to ISO 27001 controls and APRA CPS 234 expectations where required, with mapping available on request.
What if our data is not ready for AI yet?
Start with Data Readiness. It scores your data against AI use cases and gives you a prioritised plan before you invest in pipelines.