Beneath AnalyticsAI Consulting & Development
AI-Ready Data Platforms
AI-ready data platforms means Beneath Analytics builds the warehouses, pipelines, and governance that AI and analytics depend on: BigQuery and Snowflake warehouses with dbt transformation, data quality checks, and documented lineage you can defend. Pipelines scale with your data volume, so models and dashboards run on data your team trusts.
Technology we use
- Automation & agentic AI
- n8n
- BI & reporting
- Looker Studio
- Data & warehousing
- BigQuerydbtSnowflake
What this includes.
Data quality, governance, lineage, and the warehouses and pipelines that scale with your volume: the foundation every AI system and every dashboard stands on.
Data quality monitoring
Automated checks that flag data quality issues before they reach dashboards or decisions. Row counts, schema validation, freshness checks, and anomaly detection.
Data governance framework
Ownership, definitions, access policies, and change management processes for your data. Built to be practical and maintained, not a governance document that gathers dust.
Data catalogue and lineage
Documentation of where data comes from, how it is transformed, and where it goes. Enables faster debugging, onboarding, and regulatory compliance.
Master data management
Single, authoritative definitions for key entities: customers, products, transactions. Eliminates the reconciliation work that comes from multiple systems of record.
Data warehouse architecture
BigQuery or Snowflake implementations designed for your query patterns, team structure, and cost constraints. Schema design, partitioning, and access control.
ETL and ELT pipelines
Extract, transform, and load pipelines that move data from source systems to your warehouse reliably. Built with dbt, Airflow, or n8n depending on your stack.
Real-time data processing
Streaming data infrastructure for use cases that cannot wait for a nightly batch: event processing, fraud detection, live dashboards.
Data lake implementation
Cost-effective storage for raw data at scale, with the query capability and governance layer to make it usable and not just a data swamp.
What changes when it lands.
Audit, standardise, monitor; Assess, architect, implement
Method
Quality checks before dashboards
Trust
Documented lineage for GDPR
Compliance
Documented with runbooks
Handover
Performance and cost tuning
Operations
Common questions.
If two teams report different numbers for the same metric, or nobody can explain why a dashboard changed last week, the problem is data management rather than another tool.
No. We work with what you have. Most engagements add quality rules, ownership, and documentation on top of the existing stack rather than introducing a new platform.
Typically two to four weeks. You get a map of your sources, flows, quality issues, and governance gaps, so decisions about what to fix are based on evidence.
Yes. Documented lineage means you can answer where a customer's data sits, which systems process it, and how it is transformed: which is exactly what a regulatory inquiry asks for.
It depends on your query patterns, existing skills, and budget. Both are used in client work. We model the cost of each against your actual data volume before recommending one.
Usually. Slow reports normally mean the model was not designed for your query patterns or volume. We review schema design, partitioning, and transformation logic first, before suggesting more storage.
Yes. We audit the pipeline, add monitoring and alerting so failures are visible immediately, then restructure the parts that are fragile rather than rebuilding everything by default.
Through partitioning, clustering, lifecycle policies, and query review. Data infrastructure needs cost tuning after go-live, and we stay involved through that stabilisation period.
Related solutions.
- Tracking & Analytics
Analytics & Business Intelligence
Analytics and business intelligence means Beneath Analytics turns your tracked data into decisions: attribution modelling, predictive models, and dashboards built around the questions your team actually asks and adopted by the people who use them.
View solution - AI Consulting & Development
AI Workflow Automation
AI workflow automation removes the manual work between your systems: compliance reports, risk summaries, inventory updates, and weekly reporting compiled by hand. Beneath Analytics maps the process, builds pipelines with n8n, Python, or dbt, then monitors them, so reports arrive on time without someone spending days assembling them.
View solution
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