Service / 03 Integrate
Data Engineering & AI Pipelines
The data foundations AI needs: reliable pipelines, clean models, governed access and lineage.
/ 01The business problem
Why this matters
AI amplifies data problems. Duplicate customers, inconsistent definitions and undocumented tables turn into confident wrong answers once a model sits on top.
We build the pipelines and models that AI systems depend on, with quality checks and lineage so you can trace an answer back to its data.
Who it is for
- Data platform teams
- Analytics leaders preparing for AI use cases
- Organisations moving to a modern cloud data platform
/ 02Scope
What the engagement covers
- 01Data source assessment and platform design
- 02Batch and streaming ingestion pipelines
- 03Data modelling and semantic layer
- 04Quality tests, observability and lineage
- 05Document and unstructured data pipelines
- 06Access controls and governance
/ 03Technical approach
How we work on it
Model for meaning
Metrics and entities get single, documented definitions that both people and AI systems use.
Test data like code
Freshness, volume and validity checks run in the pipeline and alert before bad data reaches users.
Treat unstructured data seriously
Documents, emails and transcripts get the same pipeline discipline as tables.
/ 04Process
From first call to production
- 1
Consultation
A focused conversation about the problem, constraints and what success would mean.
- 2
Discovery
Evidence gathering: data samples, process walkthroughs and a measured baseline.
- 3
Proposal
Scope, approach, team, timeline, risks and a fixed or estimated price.
- 4
Delivery
Iterative sprints with demos and evaluation results at each milestone.
- 5
Handover or run
Documentation, runbooks and training, or ongoing operation by our team.
/ 05Deliverables
What you receive
- Data platform architecture
- Production pipelines with tests
- Curated data models and metric definitions
- Data quality dashboards
- Access policy implementation
- Documentation and lineage
Engagement options
Delivery team
A cross-functional squad for a build or integration, planned in milestones with demos and evaluation results at each one.
Ongoing partnership
A monthly capacity agreement for continuous improvement, operations and support with agreed response times.
/ 06FAQ
Common questions
Do we need a new data platform before starting AI work?
Not necessarily. Many AI use cases can start on a narrow, well-prepared dataset while the wider platform matures.
Which tools do you use?
We work with common cloud platforms and tools such as dbt, Airflow, Spark and managed warehouses, chosen to fit your existing stack.
Related solutions
Related product concepts
Talk to an engineer about data engineering & ai pipelines
Bring the problem and any constraints. You will leave the conversation with an honest view of the options, even if the answer is not us.