JING Tea is a premium single-garden tea brand founded in London in 2004, operating across direct e-commerce, wholesale trade to hotels and restaurants, and international retail. Onetribe has been the analytical layer behind their financial and operational reporting for more than three years — across two different tech stacks.
At the start of the engagement, JING Tea’s reporting had to be stitched together from several sources: Sage for accounting and inventory, additional inventory inputs coming from multiple manual sources, and manually assembled budget data. Nothing tied them together for management, so every cross-cut question — profitability by channel, stock versus plan, trade performance by product family — required someone to build the answer by hand. Onetribe consolidated these sources into a single reporting layer and replaced the manual work with automated daily updates.
Last year the business decided to change its operating stack — Shopify for sales, Xero for accounting, CIN7 for inventory, warehousing, and operations. The standard route would have required migrating years of historical data into the new systems. Instead, Onetribe’s Governed Data Layer kept old and new systems stitched together as they were: Sage history stayed on Sage, new operational data flowed from Shopify, Xero, and CIN7, and the reporting layer reconciled both into a single model. No historical data migration. No reporting blackout. Plan versus actuals and drill-down from P&L to individual sales order available continuously across both eras.
The same layer is now being extended to bring in marketing data — connecting the full journey from initial marketing touchpoint to booked revenue and gross margin. The data model is AI-ready, with Claude AI integrated for ad-hoc natural-language analysis against governed, reconciled figures. The multi-system reporting layer that absorbed one full tech stack change without disruption is the same layer that will absorb the next.
Context
- Premium single-garden tea brand founded in London in 2004
- Multi-channel operations: direct e-commerce, wholesale trade to hotels and restaurants, international retail
- Sources single-garden teas from China, India, Sri Lanka, Japan, and Taiwan
- Onetribe engaged 3+ years ago to build full financial and operational management reporting across multiple data sources
- Initial stack: Sage for accounting and inventory, with additional manual inventory inputs and manually assembled budgets
- Migrated last year to a new tech stack: Shopify (sales), Xero (accounting), CIN7 (inventory, warehousing, operations)
- Reporting layer currently being extended with a marketing data feed
- Data model is AI-ready; Claude AI integration live for ad-hoc analysis
Challenge
- Reporting spread across multiple disconnected sources — Sage accounting, Sage inventory, manual inventory inputs, manual budgets — with no unified layer to tie them together
- Cross-cut management questions required manual consolidation before an answer was possible
- Decision to migrate the operating stack to Shopify + Xero + CIN7 risked breaking reporting continuity
- Standard migration route would have required rebuilding three-plus years of historical data in the new systems
- Finance and operations needed to continue reconciling performance across old and new during and after the transition
- Forward roadmap required adding marketing data and AI-assisted analysis without further system sprawl
What Was Implemented
- Direct integrations with all source systems, old and new: Sage (accounting + inventory), manual inventory inputs, manual budgets, then Shopify, Xero, and CIN7
- Proprietary Governed Data Layer as the single reporting substrate unifying legacy and modern systems
- Standardised chart of accounts, product and SKU master, customer hierarchies, and channel taxonomy across both stacks
- Automated daily data extraction replacing manual inventory and budget consolidation
- Plan versus actuals reconciled in one model — variance analysis from group P&L down to individual sales order
- Multi-channel view combining direct e-commerce, wholesale trade, and retail in a single consolidated layer
- Phased migration support: old and new systems ran in parallel inside the reporting layer during cutover
- Historical data preserved on source systems — no historical data migration required
- Marketing data layer currently being added — extending the model from marketing touchpoint through to revenue and margin
- Claude AI integrated for ad-hoc natural-language analysis against governed, reconciled data
Outcomes
- Full management reporting maintained across three-plus years of operations and a complete tech stack change
- Historical data kept intact on source systems — no historical data migration required
- Sage-era history and Shopify/Xero/CIN7-era current performance queryable side by side in one model
- Plan variance analysis and drill-down from financial headline to sales order available across both old and new data
- Multi-channel performance (e-commerce, wholesale, retail) unified into one consolidated view
- Reporting layer absorbed the tech stack change without additional rebuild cost or added complexity
- Marketing layer being added on the same substrate — future end-to-end view from marketing spend to financial impact
- AI-ready data model supporting Claude AI integration for natural-language analysis
- Infrastructure proven as a stable analytical layer across underlying system changes — Onetribe positioned as the standard multi-system reporting layer for the business