From Architecture Blueprint to Production Platform in 8-16 Weeks

Solution Delivery

Most platform implementations fail at the handoff between design and delivery. We build what we design. Single accountability from architecture through go-live—working data platform, trained teams, and documented runbooks.

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Why Platform Implementations Fail

Strategy consultants deliver beautiful architecture diagrams, then disappear. Implementation vendors inherit requirements they didn't write and architecture they don't understand. The failure happens in the gap. We eliminate the gap. If we design it, we build it. If we build it, we train your team to run it. Single accountability from first diagram to final deployment.

Our Four-Phase Delivery Approach

A battle-tested methodology refined through dozens of enterprise platform builds, now optimized for speed and mid-market budgets.

Architecture Blueprint

We design your target platform architecture based on your specific business requirements, existing constraints, and team capabilities. Vendor-agnostic, cost-conscious, and built for your reality.

•Target architecture diagrams (ingestion, transformation, storage, analytics)
•Technology stack selection with cost-benefit analysis
•Data model design aligned to business requirements
•Security and compliance framework (access control, encryption, audit)

Foundation Sprint

We build the platform foundation—cloud infrastructure, core data pipelines, and governance framework. This 2-3 week sprint validates the architecture and de-risks the full build.

•Production cloud environment with IAM and network security
•Core data ingestion pipelines from critical source systems
•Automated deployment pipelines (CI/CD) for future development
•Monitoring and alerting framework for platform health

Iterative Build

We build the full platform in 2-week sprints with continuous stakeholder feedback. You see working software every two weeks—no surprises at the end.

•Production-ready data pipelines for all identified sources
•Transformed data models optimized for analytics and reporting
•Self-service analytics layer (BI tool, SQL interface, or API)
•Automated data quality monitoring and validation

Launch & Handoff

We deploy to production, train your team, and transfer operational ownership. You don't just get working software—you get a team that knows how to run it.

•Production deployment with cutover plan and rollback procedures
•Operations runbooks for common tasks and incident response
•Team training: platform architecture, troubleshooting, and maintenance
•Handoff documentation: architecture decisions, data dictionary, FAQ

What You Own at the End

Production-ready data platform deployed in your cloud environment
Automated data pipelines ingesting from all identified sources
Self-service analytics layer for business users
Monitoring and alerting infrastructure for platform health
Operations runbooks and troubleshooting guides
Team training and knowledge transfer sessions
Architecture documentation and data dictionary
All source code, infrastructure-as-code, and deployment scripts

Technology We Work With

Cloud Platforms

•AWS (Redshift, Glue, Lambda, S3, CloudWatch)
•Google Cloud (BigQuery, Dataflow, Cloud Functions, GCS)
•Azure (Synapse, Data Factory, Functions, Blob Storage)
•Snowflake (multi-cloud data warehouse)

Data Engineering

•dbt (data transformation and modeling)
•Fivetran / Airbyte (managed data ingestion)
•Apache Airflow (orchestration)
•Python / SQL (custom pipelines and transformations)

Analytics & BI

•Tableau / Power BI (enterprise visualization)
•Looker (embedded analytics)
•Metabase (lightweight self-service)
•Custom APIs for application integration

Governance & Observability

•Monte Carlo / Great Expectations (data quality)
•Alation / Atlan (data cataloging)
•Datadog / Grafana (infrastructure monitoring)
•Git / GitHub Actions (version control and CI/CD)

Frequently Asked Questions

How do you choose the technology stack?

We recommend technology based on three factors: (1) Your existing infrastructure and team skills, (2) Your budget constraints (build vs. buy trade-offs), and (3) Your specific use cases and scale requirements. We're vendor-agnostic—our goal is the right stack for your reality, not the hottest tools.

Do you train our team to maintain the platform after launch?

Yes. The final phase includes hands-on training for your engineering and analytics teams. We cover platform architecture, common operational tasks, troubleshooting, and how to extend the platform for new use cases. Most clients are self-sufficient within 30 days of handoff.

What if we need ongoing support after launch?

Many clients retain us for 3-6 months post-launch for on-call support during the stabilization period. Others transition to a Fractional CDO arrangement for ongoing strategic guidance. We can also help you hire and onboard a full-time data team.

How long does a typical engagement take?

Standard builds run 8-16 weeks depending on scope and complexity. Smaller projects (single data source, basic analytics) trend toward 8-10 weeks. Larger platforms (multiple sources, complex transformations, custom integrations) trend toward 12-16 weeks. The scoping call will define your timeline.

What if requirements change during the build?

We work in 2-week sprints with continuous stakeholder feedback. Minor changes are absorbed in the sprint. Significant scope changes (new data sources, major feature additions) are documented and scoped for future sprints or phase-2 work. You always know where you stand.

Ready to Build Your Platform?

Let's discuss your requirements and design a platform that delivers working software, trained teams, and documented processes.

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