We helped transform Ruggable's fragmented financial data ecosystem into a unified, scalable, AI-ready architecture. Automated reconciliation and pipelines reduced month-end close by 71% while laying the foundation for ML-driven analytics.
SERVICES
Data Engineering
Product Management
TECHNOLOGY
Apache Airflow
Apache Spark
Python
AWS Redshift
Snowflake
Oracle
CLIENT
Ruggable revolutionized the home goods market with their patented machine-washable rug system. As the company scaled internationally- across multiple regions, production facilities, and retail channels- its finance and operations data grew equally complex.
Their accounting team now faced significant bottlenecks, needing to wait at least a week after period close before beginning general ledger work. Financial data had become fragmented with the rise of their global footprint:
Each data source had its own structure, currency logic, and transaction status definitions. This led to a heavily manual reconciliation process, inconsistent reporting, and limited executive visibility into real-time performance across their international operations.
Our dedicated data engineering team worked closely with Ruggable's analytics group to tackle this complex integration challenge.
Initial scoping explored custom NetSuite integrations. But we quickly identified technical limitations that would make the solution fragile and hard to scale. Instead, we used MHI’s platform—purpose-built for NetSuite—and focused on designing a data architecture that could scale with Ruggable’s business and analytics needs.
Getting a complete view of our financial data across all the different systems was very challenging. It took a long time to close the books, requiring heavy manual effort from our accounting and supply chain teams. Before Whitespectre, we didn’t have the support or technical expertise we needed to fully implement automated data integration.
We became subject matter experts on five major PSPs, each with its own schema and naming logic. We standardized and reconciled transaction data to deliver complete, dollar-accurate reporting across all sources—eliminating manual accounting workarounds.
Using DBT, Apache Airflow, and Apache Spark, we built scalable pipelines across production systems, returns, and partner channels. These pipelines processed high-volume, multi-currency data, enabling end-to-end traceability and efficient batch orchestration.
Manual, spreadsheet-based reconciliation was replaced with automated validation and consolidation. We implemented robust data checks and exception handling using AWS Redshift and Snowflake, allowing accounting teams to trust the data and focus on analysis, not triage.
Ruggable reduced their post-close delay from 7 days to 2, unlocking faster financial reporting and more responsive operational decision-making.
Executives now have fast access to consolidated financial and operational data—enabling agile planning, performance tracking, and revenue monitoring across countries and business units.
All payment and operational data is now reconciled, governed, and integrated into one source of truth—supporting consistent reporting and collaboration across teams.
With structured, high-granularity data available, Ruggable can now run complex analyses—connecting promotional spend to revenue outcomes, modeling product-level profitability, and tracking full-funnel customer journeys.
The collaboration with Whitespectre was very successful because they acted as a self-sufficient engineering and product pod. We had design and refinement conversations, but their leads made strong technical decisions and guided the architecture. That gave us confidence that the output was technically sound. Now, having this single source of truth has completely changed how our internal teams operate.
This project didn’t just solve for month-end reporting—it established the core building blocks for Ruggable’s long-term data and AI strategy. By delivering clean, governed, and extensible datasets, we’ve made possible:
The unified transaction and returns data provides ideal inputs for future ML models—like revenue forecasting, churn prediction, or fraud detection.
By integrating data across payments, production, returns, and logistics, Ruggable can now build complex behavioral datasets that support segmentation, LTV modeling, and journey analytics.
The architecture we implemented became the backbone for a broader data initiative—Ruggable’s emerging enterprise-wide common data model. This expands analytics capabilities beyond accounting to product, growth, and executive strategy.
The AI and machine learning use cases we’re most excited about center on financial forecasting. Our models have always been very manual, but with the foundation we’ve built with Whitespectre, we now have the ability to explore predictive modeling at scale. The work they did set a great example, one we’ll continue to champion.
Ruggable’s leadership now operates with confidence in their data—and with a foundation that supports long-term strategy. As their common data model evolves, they’re positioned to expand ML experimentation, improve forecasting accuracy, and unlock data-driven decision-making at scale.