Enterprise CPG operational intelligence. Not templates.
Unolabs engineers enterprise CPG operational intelligence ecosystems for modern commerce transformation — governed demand forecasting systems, retailer analytics frameworks, pricing intelligence architectures, and supply chain observability platforms that speed operational decisions across the full CPG value chain.
POS, ERP, ECOMMERCE, PROMOTIONS
Retail telemetry + operational demand signals
DOMAIN-AWARE DECISION SYSTEMS
Demand orchestration + pricing intelligence
PLANNING, ANALYTICS, AUTONOMY
Operational insights + automated execution
Commerce intelligence without a two-year build
Your commercial teams argue about numbers while competitors act on live retailer signals — and another bespoke analytics programme is not a pitch your CFO will fund. The CPG accelerator starts from governed industry domain models, so the first intelligence release lands in 8-12 weeks instead of a multi-year build. New brands and markets then onboard through configuration, which is how the economics stay defensible.
Why CPG transformation initiatives fail
Fragmented Retailer Intelligence
Disconnected retailer data systems that prevent unified sell-through, distribution, and inventory visibility across channels and geographies.
Disconnected Supply Chain Systems
Siloed supply and demand planning environments that operate without shared context, causing chronic forecast misalignment and inventory inefficiency.
Inconsistent Demand Forecasting
Fragmented forecasting models built in isolation prevent consensus forecasting and degrade operational responsiveness at scale.
Siloed Commerce Analytics
Disconnected category, channel, and trade analytics that prevent a unified view of pricing performance, promotion ROI, and margin optimisation.
Poor Operational Visibility
Absence of real-time operational cockpits means CPG leadership makes decisions on stale, aggregated reporting rather than live commerce intelligence.
Weak Governance and Standardisation
Without governed data definitions and standardised KPI taxonomies, commercial teams report conflicting numbers and waste cycles reconciling truth.
Business Outcomes
Demand Signals Are Scattered
POS, ecommerce, distributor, promotion, weather, inventory, and shipment data live in different systems with different grains.
Promotion Blindness
Teams cannot connect campaigns, pricing, stockouts, cannibalisation, and sell-through fast enough to adjust.
Slow Custom Modelling
Every CPG analytics build recreates product, customer, channel, geography, and time models from scratch.
How CPG Intelligence Accelerator delivery works
The view below shows how work moves through the delivery flow — from inputs, through governed controls, to operational outputs.
CPG and Retail Data Flow
Source Layer
POS, ERP, Ecommerce, Promo
Commercial and operational feeds arrive across channels, stores, SKUs, distributors, and campaigns via SAP, Snowflake, and cloud connectors.
Engineering Layer
Governed CPG Intelligence Domains
Product, customer, channel, geography, promotion, inventory, and time domains are standardised into governed enterprise intelligence templates.
Demand, Pricing & Promo Features
Features support consensus forecast, stockout detection, lift measurement, margin analysis, velocity scoring, and replenishment optimisation.
Activation Layer
Planner, BI, Agents
Demand planners, category managers, supply teams, and autonomous agents receive recommended actions via Power BI, Tableau, and MS Fabric.
POS, ERP, Ecommerce, Promo
Governed CPG Intelligence Domains -> Demand, Pricing & Promo Features
Planner, BI, Agents
Signals -> Model -> Intelligence -> Action
Access
Identity, RBAC, purpose, and least privilege.
Quality
Freshness, completeness, validity, and anomaly checks.
Lineage
Source, transformation, owner, and consumer traceability.
Operations
Monitoring, retry, alerting, runbooks, and evidence.
How Unolabs engineers CPG Intelligence Accelerator
Domain Model Fit
We map your product, customer, channel, store, distributor, promotion, and inventory entities to governed, reusable CPG intelligence templates.
Signal Integration
We combine POS, ERP, ecommerce, syndicated, warehouse, and external signals into standardised intelligence layers powered by Snowflake, Databricks, and SAP.
Demand and Supply Views
We publish forecast-ready features, stockout indicators, service level metrics, and replenishment signals for demand planners and supply teams.
Promotion Intelligence
We connect campaign calendars, pricing, volume, margin, lift, cannibalisation, and inventory outcomes into a single promotion observability framework.
Semantic BI Layer
We define governed measures for sell-in, sell-out, gross margin, availability, velocity, and forecast accuracy across Power BI and Tableau environments.
Operational KPI Governance
We establish standardised KPI taxonomies and data stewardship models that eliminate conflicting commercial reporting across functions.
Enterprise CPG Intelligence Maturity Model
Where does your organisation sit on the path to autonomous operations? Use this model to identify your current stage and the critical engineering gaps preventing progression.
Fragmented commerce reporting
Isolated, manually assembled sales and supply reports with no unified commerce intelligence or standardised data definitions.
Department-level retail analytics
Category and channel analytics exist but remain siloed, with limited cross-domain demand or supply visibility.
Governed enterprise CPG intelligence
Standardised demand, supply, and pricing models with governed KPI taxonomy and cross-functional operational visibility.
Real-time operational commerce visibility
Live inventory, promotion, and demand signals power operational decisions across planners, category managers, and supply teams.
Autonomous commerce intelligence ecosystems
Agentic supply chain orchestration and self-optimising commerce intelligence that adapts to real-time market and demand signals.
Industry Benchmarking
Transformation Progression
CPG Intelligence Audit
Assessment of current commerce data estate, demand signal coverage, retailer data gaps, and forecasting maturity.
Architecture Design
Designing the enterprise CPG domain model, demand intelligence layer, and supply chain observability framework.
Domain Model Deployment
Deploying standardised product, channel, geography, promotion, and inventory domains with governed KPI definitions.
Intelligence Activation
Publishing demand forecasting features, promotion analytics, and retailer performance cockpits for operational teams.
Autonomous Commerce Operations
Enabling agentic supply chain orchestration, automated replenishment signals, and self-optimising demand planning workflows.
CPG Intelligence Use Cases
Consensus demand planning with retailer POS and syndicated signal integration
Lift analysis, cannibalisation detection, and trade promotion ROI optimisation
Velocity-based portfolio optimisation and distribution performance intelligence
Real-time inventory, replenishment, and service level monitoring architecture
Sell-through, distribution, and on-shelf availability intelligence platforms
Gross-to-net pricing, category margin, and channel profitability analytics
What this means in practice
Templates Preserve Industry Logic
The CPG intelligence model includes industry-specific grains and relationships so teams do not waste months debating basic domain structures or KPI definitions.
From Reporting to Orchestration
The same governed data products power dashboards, promotion alerts, consensus forecast models, and agentic supply chain orchestration workflows.
Reusable Across Brands
Once the CPG intelligence architecture is established, new markets, brands, categories, and retail channels onboard through configuration rather than custom builds.
CPG and Retail Data Flow
The CPG intelligence flow shows how commercial, operational, and external signals converge into governed demand, supply, and pricing intelligence ecosystems.
POS, ERP, Ecommerce, Promo
Commercial and operational feeds arrive across channels, stores, SKUs, distributors, and campaigns via SAP, Snowflake, and cloud connectors.
Governed CPG Intelligence Domains
Product, customer, channel, geography, promotion, inventory, and time domains are standardised into governed enterprise intelligence templates.
Demand, Pricing & Promo Features
Features support consensus forecast, stockout detection, lift measurement, margin analysis, velocity scoring, and replenishment optimisation.
Planner, BI, Agents
Demand planners, category managers, supply teams, and autonomous agents receive recommended actions via Power BI, Tableau, and MS Fabric.
CPG Intelligence Accelerator: from input to operational asset
The flowchart turns the service into a delivery sequence so buyers can see the real work, not just the promise.
Business Input
Demand Signals Are Scattered
Architecture Decision
Domain Model Fit
Data Treatment
Governed CPG Intelligence Domains
Controls Applied
Demand, Pricing & Promo Features
Operational Output
Planner, BI, Agents
Visible work products, not vague advice
Each deliverable is designed to be used by executives, architects, engineers, data owners, and operations teams after the engagement ends.
The delivery path
Discover
Audit commerce data estate, demand signal coverage, retailer data gaps, forecasting models, and operational KPI conflicts.
Design
Define the enterprise CPG domain model, governance taxonomy, demand intelligence architecture, and supply chain observability framework.
Build
Deploy governed domain templates, demand features, promotion analytics, and operational dashboards across Snowflake, Databricks, and Power BI.
Scale
Expand the CPG intelligence architecture across new brands, markets, and channels through configuration rather than custom engineering.
What changes after the work
Faster demand forecasting cycles
Improved supply chain visibility
Better pricing and promotion intelligence
Increased inventory optimisation
Faster operational decision-making
Improved retailer performance visibility
Reduced operational inefficiencies
Enhanced commerce responsiveness
How an engagement starts
A 45-minute scoping call with a CPG data lead — bring your demand and promotion reporting pain points; leave with a read on template fit and a proposed first-release scope.
Bring your forecast and promo pain points. Leave with a first-release scope.
Related service pages
Data Engineering & Integration
Unolabs builds resilient, governed data engineering infrastructure: CDC-based ingestion, metadata-driven pipelines with contract-tested interfaces, and real-time processing systems that accelerate enterprise modernisation.
Semantic AI & Knowledge Graphs
We engineer the semantic foundations required for autonomous enterprise intelligence — ontologies, entity resolution, knowledge graphs, and GraphRAG retrieval architectures that give AI the relationship context vector search alone cannot provide.
Agentic AI & Autonomous Operations
Unolabs engineers governed enterprise autonomous operations and agentic ecosystems at scale: goal-directed AI systems that plan, call enterprise tools, and iterate under policy guardrails — reasoning over enterprise context to remediate operational bottlenecks, where a chatbot can only answer.