The Automated Processing Engine That CPG Teams Trust
Our platform does the heavy lifting — running hundreds of automated correction rules, AI-driven harmonization, and a generative AI-powered semantic layer that turns raw outlet data from any retail source into a single, analytics-ready truth.
Core Technology Capabilities
Three integrated layers — collection, harmonization, and distribution — built specifically for the complexity of CPG outlet data.
POS Data Collection
Proven experience across every retail segment and POS platform type
We have worked with POS data across the full spectrum of retail — national chains, regional grocers, convenience stores, drug, mass, specialty retailers, and independents. There is no retail format or POS platform type our team hasn't encountered and solved for. That depth of hands-on experience means faster onboarding, fewer surprises, and a data foundation your team can trust from day one.
- National chains, c-stores, grocery, drug, mass, specialty, and independent outlets
- All major POS platforms and data formats — EDI X12, EDIFACT, CSV, direct API, and SFTP
- Experience with direct-store-delivery, distributor feeds, and wholesaler data sources
- Consistent, reliable data regardless of retailer size, format, or technical maturity
Key Metrics
All metrics based on real-world production deployments. Performance benchmarks reflect typical CPG data environments.
Data Harmonization Engine
Automated normalization, standardization, and deduplication across retailer formats
No two retailers report data the same way. Our harmonization engine runs hundreds of automated correction rules on every ingested record — aligning file structures, resolving missing values, eliminating duplicates, and detecting outliers and anomalies. A generative AI-powered semantic layer then provides consistent field definitions and business logic across all data sources, enabling reliable cross-retailer analytics at any scale.
- Hundreds of automated correction rules: file alignment, missing data, duplicates, anomalies
- Semantic layer for consistent field definitions and business logic across all retail sources
- Cross-retailer entity matching: product codes, store IDs, names, timelines, and hierarchies
- AI agents continuously monitor cross-store consistency, flagging drift and triggering auto-corrections
- Store-to-store harmonization — syndicating data from one retail source to another in a unified model
Key Metrics
All metrics based on real-world production deployments. Performance benchmarks reflect typical CPG data environments.
Warehouse Connectors
Pre-built connectors to Snowflake, Databricks, BigQuery, and Redshift
Once data has been processed and harmonized, it lands directly in your cloud data warehouse — clean, validated, and enriched. Our platform handles the full delivery layer with no custom pipelines or ETL scripts to maintain, so your team consumes analytics-ready data from the moment it arrives.
- Delivery to Snowflake, Databricks, BigQuery, and Redshift out of the box
- Configurable push cadence from real-time streaming to nightly batch
- Auto-generated dbt models and column-level lineage metadata
- Incremental loads with change-data-capture for cost-efficient warehousing
Key Metrics
All metrics based on real-world production deployments. Performance benchmarks reflect typical CPG data environments.
Built for Speed and Reliability
Every feature engineered around the realities of CPG data operations.
Real-Time Sync
Sub-5-minute end-to-end latency from outlet scan to analytics layer.
All Retail Segments
National chains, c-stores, grocery, drug, mass, specialty, and independents — every format we've worked with, built into our approach.
AI-Driven Normalization
Generative AI-powered field mapping resolves product codes, store IDs, entity names, and date formats — with agent-based exception handling for anything outside normal patterns.
Historical Restatement
Automatic detection and reconciliation of restated historical figures.
Technical Advantages That Translate to Business Outcomes
Our clients don't just get cleaner data — they reclaim significant analyst capacity and eliminate an entire category of operational overhead.
"CPG companies waste an average of 3–4 days per week reconciling outlet data — our platform eliminates that entirely."— Product Team, CPG Data Operations Platform
Reduction in manual data prep time
Teams reclaim 2–3 days per week previously lost to spreadsheet reconciliation and format conversion.
Single source of truth across all outlets
Every retailer, distributor, and direct account feeds the same unified outlet master — no more conflicting dashboards.
Automated correction rules per run
AI agents execute hundreds of rules on every ingestion — file alignment, missing data, anomaly detection, and deduplication — before data ever reaches your analysts.
Data completeness SLA
Contractually backed completeness guarantee with automated gap detection and proactive alerts before reporting deadlines.
What You Get Out of the Box
See the Technology in Your Environment
Our engineering team will walk you through a hands-on proof of concept using your actual data sources — so you see real impact before you commit.