Duvo vs. Databricks: Different Layers of the Retail AI Stack, Not Competitors

Key Takeaways

• Databricks and Duvo solve different problems and serve different teams.

• Even with Databricks execution remains manual. Duvo closes this gap with an AI workforce that executes cross‑system work.

• The winning retail architecture is Databricks for intelligence + Duvo for execution, creating a closed loop from insight to action and unlocking the full ROI of analytics investments.

Why Duvo and Databricks Get Compared

Retailers and FMCG manufacturers are hearing the same two phrases in almost every transformation conversation: “data platform” and “AI teammates.”
That is why Duvo and Databricks are often mentioned in the same breath.

In reality, they solve different problems:

• Databricks is a data and AI platform: it unifies data, governance, analytics, ML, and GenAI in one place for your technology and data teams.
  
  
• Duvo is an AI workforce for retail operations: AI teammates that log into your real systems (SAP, ERPs, supplier portals, spreadsheets, email) and execute end‑to‑end workflows with human approvals. 

  Duvo can also connect directly to Databricks to use the insights stored there or generate its own.

Different problems, different buyers

Databricks is for data and AI platform teams. It is bought and owned by:

• Chief Data, Digital and Technology Officers

• Heads of Data Engineering, Analytics, and ML

They use it to:

• Centralize data from ERP, POS, ecommerce, logistics, marketing, finance, and more
• Govern that data (schemas, access, lineage, quality)
• Build and run analytics, ML models, and GenAI/agentic applications on top of it

Its primary output is data products: tables, features, dashboards, models, and APIs that other systems and teams consume.

Duvo is for commercial, supply chain, and finance operations. It is bought and championed by:

• COO, CCO, CFO, Heads of Supply Chain and Category
• Leaders of operations, shared services, and commercial finance

They use it to:

• Take the manual, cross‑system work that teams do in SAP, spreadsheets, email, and portals
• Turn it into governed AI workflows that run continuously, with approvals where needed
• Free up 30–40% of repetitive work in category, supply chain, and finance teams within weeks

Its primary output is executed work: updated POs, changed prices, chased suppliers, reconciled invoices, completed onboarding files, and so on.

At a glance: Duvo vs. Databricks

They are complementary by design: Databricks makes your data and AI capabilities powerful; Duvo turns those capabilities into day‑to‑day operational execution.

What Databricks is (and is not)

From a retailer’s perspective, you can think of Databricks as:

• The central nervous system for your data and AI
  
• A place where data from SAP, WMS, TMS, POS, e-commerce, loyalty, marketing, and finance is modeled and governed
• The platform your data teams use to build dashboards and self‑service analytics, forecasting and optimization models, and RAG and agentic applications over governed data

What it is not designed for:

• Logging into SAP GUIs or supplier portals to execute thousands of small steps
• Handling messy UI work, email flows, or phone calls with vendors and stores
• Being used directly by hundreds of category managers or finance operators

Databricks is infrastructure: it underpins analytics and AI, but it does not replace the operational work that happens in your line‑of‑business tools.

What Duvo is (and is not)

Duvo was built by retail operators to solve a specific problem: Most of the work in commercial, supply chain, and finance functions is still manual, cross‑system execution.

What Duvo AI teammates actually do:

• Log into your systems

• • SAP and other ERPs

• • Supplier and customer portals

• • Spreadsheets on shared drives
  • Email inboxes and internal web tools
  • Generate insights based on data
• Execute end‑to‑end workflows such as:

• • Weekly margin and promo cockpit with ready‑to‑execute actions per category
  • Listing / delisting and replacements across ERP, PIM, e-commerce, and store tools

• • Purchase order proposals and approvals, especially for long tail / exception‑heavy SKUs

• • Inventory health actions (markdowns, supplier returns, donations)

• • Supplier onboarding, data quality, and document chase

1. • Cash collection, deductions management, and dispute clarification

• Make outbound calls when needed

1. • Chase missing invoices, credit notes, certificates, and onboarding documents

1. • Confirm deliveries, ETAs and slot changes with suppliers, carriers, DCs, and stores

1. • Remind partners of overdue payments or clarify invoice discrepancies

1. • Capture outcomes, update ERP and trackers, and escalate exceptions to humans

• Work under strict governance and security

• • Role‑based access, SSO, and scoped credentials
  • Ephemeral “enterprise browser” sandboxes that isolate sessions and credentials
  • Full audit trails of every action, approval, and change

What Duvo is not:

• Duvo is not a data warehouse or lakehouse. It will consume data from your ERP/DWH/Databricks or directly from UIs and files.
• Duvo is not a general‑purpose developer platform like a low‑code automation suite for every department. It is focused on retail and FMCG operations, with a growing catalogue of proven use cases.
  
  

How Duvo and Databricks work together in practice

In a modern retailer or FMCG manufacturer, a typical pattern looks like this:

Step 1: Databricks as the data and AI foundation

Your data team uses Databricks to:

• Consolidate sales, margin, stock, promo, and logistics data
• Build forecasts, risk scores, and opportunity lists (e.g., SKUs at risk of stockout, tail SKUs to delist, promo uplift predictions)
• Govern access to that data and expose it via SQL, APIs, or feature tables

Step 2: Duvo as the AI workforce that acts on insights

Duvo AI teammates then:

• Read the relevant data (from Databricks, ERP, or reports)
• Turn it into specific tasks and actions, such as:
  
  
  • “Generate next week’s margin pack for Confectionery and propose actions per supplier.”

• • “Prepare PO proposals for long tail SKUs following our policy, route for approval, then place POs in SAP and supplier portals.”

• • “Call suppliers who have not completed onboarding and collect missing documents.”
• Execute those tasks across SAP, portals, spreadsheets, and email, pausing for human approvals where required

Step 3: Closed loop

• Databricks measures the impact on margin, availability, write‑offs, and working capital.
• Duvo AI teammates continuously run the workflows that drive those metrics, learning and improving over time.

The result is a closed loop between insight and execution: Databricks tells you what to do; Duvo does the work.

How to decide where to start

You do not choose between Duvo and Databricks; you decide where your current bottleneck is.

Start with Databricks if:

• Your data is fragmented and unreliable
• Your analytics and AI teams lack a unified, governed platform
• You are still early in building basic reporting, forecasting, and ML capabilities

Start with Duvo if:

• You already have “enough” data and reporting to know what should happen next
• Your teams are drowning in manual execution in SAP, spreadsheets, email, and portals
• You need tangible FTE and error reduction in weeks, not a multi‑year platform rollout

If you already have Databricks

Duvo can sit cleanly on top:

• No need to re‑architect Databricks or your data model
• Duvo can consume outputs from Databricks as just another input (SQL views, files, reports)
• You keep Databricks as the strategic data and AI backbone, and Duvo as the operational AI workforce that executes day‑to‑day work

Summary

• Databricks: the data and AI backbone for your organization, owned by technology and data teams.
• Duvo: the AI workforce that takes work off your category, supply chain and finance teams’ plates by operating directly in your systems and on the phone.

For retailers and FMCG manufacturers, the winning architecture is not “Duvo or Databricks.” It is Databricks for data and models, Duvo for execution and workflows. Each doing the job it was built for.

Sources

• Forrester (2024) – The Forrester Wave™: Data Lakehouses, Q2 2024

• Fivetran (2022) — “Over 80 % of companies rely on stale data for decision-making” press release

• Grand View Research (2025) – Data Lakehouse Market Size & Share | Industry Report, 2033

• Global Market Insights (2025) – Data Lakehouse Market Size & Share | Growth Forecast 2025-2034