Built for data-driven forecasting, intelligent inventory planning, and end-to-end supply chain visibility — powered by Google Cloud Platform.
Static forecasting models built on historical averages miss seasonal spikes, promotional lifts, and external market signals — leaving inventory decisions always one cycle behind reality.
Time-series ML models trained on sales history, seasonality, promotional calendars, and external signals using Vertex AI. Forecasts are updated continuously, surfacing SKU-level predictions with confidence intervals that drive smarter buying decisions.
Sales history, promotions, and external signals are ingested to BigQuery
Vertex AI trains and continuously retrains time-series models per SKU/category
Forecasts with confidence intervals are surfaced via dashboards and APIs
Demand signals automatically feed into inventory replenishment workflows
Static reorder points fail in volatile demand environments. Retailers end up simultaneously over-stocked in slow movers and stocked-out on high-velocity items — a working capital and revenue problem at the same time.
Predictive models that dynamically set reorder points based on real-time demand signals, supplier lead times, and service level targets. AI agents automatically trigger purchase orders via ERP integration when conditions are met — with no manual intervention required.
Real-time inventory positions sync from WMS and ERP to BigQuery
Demand forecasts feed into dynamic reorder point calculations per location
AI agents monitor thresholds and auto-trigger replenishment workflows
Dashboards track service levels, turns, and working capital freed
Generic keyword search fails shoppers who don't know exact product names. Poor product discovery leads to high rejection rates, reduced basket sizes, and customers buying from competitors who surface relevant results faster.
Google-quality AI search and recommendations embedded natively into digital storefronts using Vertex AI Search for Commerce. Advanced language models interpret intent — surfacing contextually relevant products with conversational shopping capabilities across web, mobile, and in-store.
Product catalog and customer behavior data are ingested and indexed
LLM models interpret shopper intent beyond keyword matching
Personalized results and recommendations are served in real-time
Conversational assistant guides customers to purchase across any channel
Supply chain disruptions are invisible until they hit shelves. Siloed supplier, logistics, and in-store data make it impossible to anticipate delays or reallocate stock before a stockout materialises.
A cloud-native supply chain analytics platform on BigQuery unifying supplier performance, in-transit tracking, DC throughput, and store-level data. AI models detect disruption signals early and surface recommended actions for proactive response.
Supplier feeds, logistics APIs, and DC data stream into BigQuery via Pub/Sub
ML models score supplier risk, flag delays, and detect demand-supply gaps
Disruption alerts surface recommended reallocation and re-routing actions
Looker dashboards give end-to-end visibility from supplier to shelf
Refrigeration failures, HVAC outages, and conveyor breakdowns happen without warning — destroying perishable inventory, disrupting DC throughput, and triggering costly emergency service calls.
BigQuery ML with Gemini monitors retail equipment telemetry to detect early failure signals and autonomously trigger workflows — triaging issues, checking parts availability, and dispatching technicians before any associate or customer is affected.
Equipment sensors stream telemetry data via Pub/Sub in real-time
BigQuery ML + Gemini models detect anomaly patterns and predict failures
AI agent checks parts inventory and schedules technician dispatch automatically
Resolved cases feed back into models — system gets smarter with each repair
Supplier onboarding, compliance documentation, purchase orders, and invoice reconciliation consume enormous manual effort — slowing supplier cycles and creating audit exposure across procurement and finance teams.
Document AI pipelines that automatically extract, validate, and route supplier documents, POs, and compliance certificates. ML models cross-reference against contract terms and compliance rules — automating straight-through processing for clean documents.
Supplier documents are ingested via email, portal, or EDI feeds
Document AI extracts structured data fields with high accuracy
ML models validate against contract terms, compliance rules, and ERP data
Clean documents auto-process; exceptions are routed to the right reviewer
Defective merchandise received from suppliers drives customer returns, markdown losses, and brand damage. Manual inspection at receiving docks is inconsistent and slow — letting quality issues slip through at scale.
Computer vision systems deployed at receiving docks and DCs that inspect incoming merchandise for defects, damage, and specification compliance in real-time. Models classify issue types, reject non-conforming units, and generate automated supplier quality scorecards.
Cameras at receiving stations capture product images as units arrive
Vision API and Vertex AI models assess defects against specification profiles
Non-conforming units are flagged for rejection or hold review in real-time
Supplier quality scorecards auto-generated and shared for accountability
Whether starting with demand forecasting or deploying a full agentic supply chain — our team is ready for enterprise and partner discussions.