Build a data foundation your teams can trust.

Replace fragile pipelines, conflicting metrics, and unclear ownership with a governed data system built for analytics and AI.

Book an architecture reviewSee what the engagement includes
Best for
Fragile reporting and AI-readiness gaps
First engagement
One data workflow or trusted dataset
Typical pilot
2-4 weeks
Decision
Scale, sequence, or stop with evidence

This is likely the right starting point when

  • Teams debate which numbers are correct
  • Pipeline failures are discovered by report users
  • AI initiatives are blocked by data access or quality
  • Ownership, lineage, or cost is unclear
Deliverables

What we deliver

  1. 01

    Trusted dataset or workflow

    Modeled, tested, documented, and owned.

  2. 02

    Production pipelines

    Automated ingestion, transformation, and quality checks.

  3. 03

    Access and governance

    Roles, lineage, cost controls, and audit visibility.

  4. 04

    Operating handoff

    Runbooks, architecture decisions, and a prioritized roadmap.

Implementation

From one workflow to a production foundation

A reference path from source systems to trusted data products, with access, lineage, monitoring, and cost controls across the flow.

SourcesData WarehouseConsumerstestfailpass
SaaS APIs
Databases
Events
Ingest
Raw
Staging
Quality
Marts
BI
ML
Reverse ETL

Architecture Decisions

  • Warehouse vs. lakehouse: Pure warehouse for BI; lakehouse adds unstructured/ML workloads at complexity cost.
  • Batch vs. streaming: Start batch-first; add streaming only where latency requirements justify complexity.
  • Transformation layer: dbt for SQL-first teams; Spark for complex ML feature engineering.
  • Cost model: Separate compute from storage; set resource monitors and auto-suspend.

Integration & Security

  • Source connectors: Fivetran/Airbyte for SaaS; custom for legacy or high-volume CDC.
  • Access control: RBAC with row-level security for multi-tenant or sensitive data.
  • Data quality: dbt tests + Great Expectations for contract validation.
  • Catalog & lineage: Atlan, DataHub, or native catalog for discoverability and impact analysis.
Focused engagement

From one workflow to a production foundation

  1. 01

    Assess

    Map the workflow, sources, owners, and failure points.

  2. 02

    Design

    Agree on contracts, quality rules, and target architecture.

  3. 03

    Ship

    Deliver one trusted dataset or workflow in production.

  4. 04

    Decide

    Scale, sequence, or stop based on evidence.

Operating handoff

What You Leave With

Architecture diagram

Production-ready design with stack choices, data flows, and security boundaries.

Data contracts

Schema definitions, SLAs, and ownership for each data product.

dbt project

Modeled datasets with tests, documentation, and CI/CD pipeline.

Access model

RBAC policies, row-level security rules, and audit logging.

Cost playbook

Query tagging, warehouse sizing, and optimization runbook.

Runbooks

Incident response, backfill procedures, and on-call documentation.

Scope

What the scope can include

  • Warehouse or lakehouse design aligned to current workloads and team capacity
  • Batch or event-driven pipelines with explicit quality checks and ownership
  • Dimensional and semantic models for consistent metrics and reporting
  • Infrastructure-as-code, role-based access, audit logging, and spend controls
  • Catalog, lineage, glossary, and data-contract patterns where they add operational value

Related Use Cases

See what this looks like in practice:

A clear division of responsibility

Semper AI brings

  • Solution lead and architecture
  • Hands-on implementation
  • Quality, security, and observability
  • Decision log, runbooks, and handoff

Your team brings

  • A workflow owner
  • Access to relevant systems and data
  • People who can make scope decisions
  • Time to review the working pilot
FAQ

Questions before you start

01Do you replace our BI tool?

Usually not. We design around the reporting and analytics tools your teams already use.

02How long until the first trusted workflow?

A focused pilot is usually scoped to 2-4 weeks. Timing depends on source access, data quality, and how quickly owners can review decisions.

03Can you work inside our cloud and security model?

Yes. We design around your current stack, access model, and deployment constraints.

Start with the workflow that is costing your team trust.

We’ll map the data, ownership, and failure points, then recommend the smallest useful first engagement.

Start the reviewCompare all services