Data, productized.
Most data never makes it into the product. We turn it into one.
We design the pipelines, warehouses, and AI-ready foundations that turn raw, scattered data into products that decide, personalize, and scale, built on AWS by senior engineers.
Data, productized.
Most companies are data-rich and decision-poor.
The data exists. It’s just scattered, inconsistent, and stuck in tools that don’t talk to each other. Rootstrap builds the foundation (ingestion, warehousing, quality, modeling) and the layer on top that turns it into analytics, personalization, and production AI. Not a dashboard nobody opens. Data that ships inside the product.
What we actually deliver
Data architecture & warehousing
Cloud data platforms done right on AWS (Snowflake, Redshift, or BigQuery) with modeling, governance, and a semantic layer so every number means the same thing.
ETL/ELT & big-data processing
Reliable batch and streaming pipelines (Spark, Kafka, Airflow, dbt, and AWS Glue/Kinesis) built to scale and instrumented so you know the moment something breaks.
Analytics, enrichment & ML features
Dashboards people actually use, data quality and enrichment, reverse-ETL back into the tools teams work in, and feature pipelines that power personalization and AI.
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We build the pipeline, not just the dashboard.
A dashboard is the easy part. The hard part is the foundation underneath it: clean, governed, real-time data your team and your models can trust. That’s what we engineer first, so everything you build on top of it actually holds.
The Data Discovery Sprint
Every data engagement starts with discovery, a structured engagement where we map your data and design the warehouse, pipelines, quality, and governance before anyone builds a thing.
Data readiness map
What data you have, where it lives, what’s missing, and what must be cleaned before it can drive decisions or models.
Architecture recommendation
Warehouse, pipeline, and orchestration choices (Snowflake vs Redshift vs BigQuery, batch vs streaming) with the engineering rationale, on AWS.
Pipeline & quality plan
The ETL/ELT design, data-quality tests, and the modeling and semantic layer, so analytics and AI run on trustworthy data.
Governance & compliance
Access controls, lineage, and GDPR/HIPAA-aware handling, designed before the build, not bolted on after.
Governance, Quality & Compliance
Modeling, access controls, and a semantic layer so the numbers mean the same thing everywhere.
Lineage, freshness checks, and alerting, so you know when a pipeline breaks before the dashboard lies.
GDPR- and HIPAA-aware data handling, in SOC 2 certified environments.
Automated data-quality tests and reconciliation so analytics and AI run on data you can rely on.
The data stack we build on
AWS-first, with the modern data tools your team already knows.
Where we’ve shipped data in production
EdTech
We migrated Emeritus off a legacy Salesforce-based system onto higher-performing data and web platforms, with SSO and a scalable CI/CD pipeline as the team grew to 40+.
Health-tech
For EXI we built wearable-data flows (Apple Watch, FitBit) feeding clinician dashboards through a secure, penetration-tested, GDPR/HIPAA-compliant data portal.
eCommerce & Retail
For The Farmer’s Dog we engineered data flows (Segment, SplitIO) and order/shipment platforms on AWS that held up through record Super Bowl traffic.
Logistics & Operations
For Kinimatic we turned warehouse data into natural-language insights and AI-driven recommendations across multiple sites.
Ready to turn your data into a product advantage?
Start with a Data Discovery Sprint, a structured engagement that produces a data-readiness map, an AWS-first warehouse and pipeline architecture, a quality plan, and a governance design. A real plan before the build.