Industry · Retail
A temporal-tensor transformer pre-trained self-supervised over years of multi-SKU multi-channel transaction tensors. Downstream heads fine-tune to assortment, channel mix, and store cluster — and agents close the loop from forecast to replenishment to channel execution.
01 · SharpAI
Predictive models
11 models · foundational + downstream
02 · AUTONOMY
Agents
20 agent patterns in this vertical
03 · FDE
Engineering services
3 anonymized engagements
01 · SharpAI · Retail
A time-series transformer pre-trained self-supervised over years of multi-SKU multi-channel transaction tensors. Hierarchical embeddings at SKU, region, channel, and store-cluster level feed downstream heads that fine-tune to each retailer's assortment, calendar, and network.
Foundation · what the backbone tokenizes
Billions of SKU-store-week records across years of history. Each dot is a raw signal the Temporal Demand Transformer tokenizes — hover or tap to see what it is.
Downstream · what the head consumes
The long-horizon forecast head reads the frozen TDT embeddings plus a focused set of decision-time features. Hover any node — these inputs drive the SKU-region-channel forecast and its confidence band.
Foundation models · self-supervised pre-training, frozen backbone reused downstream
Backbone for retail and distribution forecasting. Treats multi-SKU multi-channel transactions as a temporal tensor; attention runs across time, SKU, and channel axes simultaneously. Hierarchical pooling produces store-cluster and region-level embeddings that downstream heads consume.
Temporal Fusion Transformer family · cross-axis attention (time × SKU × channel) · 256-dim hierarchical embeddings · static covariates (calendar, promo, region) injected as conditioning tokens
Pre-training objectives
Corpus · Years of transaction tensors across supermarket chains, specialty retail, and distribution networks · weekly + daily granularity · billions of SKU-store-week records · multi-retailer joint pre-training
Downstream heads · fine-tuned on the frozen foundation embedding per institution and use case
Long-horizon demand forecast tuned to retailer assortment and promo calendar.
Multi-horizon quantile head over TDT embeddings
Flags stock-out risk 1/2/4 weeks out from velocity, lead-time variance, and inventory positions.
Survival head conditioned on inventory state
Clusters stores or trade areas by behavior, mix, and growth pattern; surfaces differentiating drivers.
Clustering over store-pooled embeddings + driver attribution
Optimizes product mix across regions and store clusters with margin + velocity weighting.
Ranking head · margin-weighted
Order quantity, timing, and source per node — calibrated for service level, working capital, obsolescence.
Multi-echelon optimization over forecast distribution
Distributor scoring + ideal-offer prediction; turns channel coverage into a revenue lever.
Hierarchical head over distributor-pooled embeddings
Allocates promotional budget across SKUs and accounts; monitors lift vs. baseline in real time.
Uplift head with synthetic-control baseline
Cluster-level assortment recommendation with substitution and cannibalization modeling.
Cross-SKU attention with cannibalization terms
Demand and lift modeling for new-product launches with rapid recalibration as transactions arrive.
Few-shot head · similar-SKU retrieval from TDT embedding
Markdown depth and timing recommendation; competitor-price elasticity bounded by policy.
Counterfactual elasticity head · policy-gated
300%
Forecast accuracy vs. industry baseline · top global beverages
90%
Minimum prediction precision · top Brazilian supermarket chain
+21%
Distributor revenue lift · top 10 chemical company
14-month
Forecast horizon with dynamic frequency adjustment
02 · AUTONOMY · Retail
Retail moves fast and decisions land in operations every hour — replenishment, pricing, store ops, distributor coverage. These agents close the loop from forecast to action.
20 agent patterns · ordered by deployment maturity
Predicts SKU-region-channel demand at weekly to 14-month horizons; recommends procurement quantities and timing to ops.
Trigger · Daily refresh + event
↳ 90% min precision · top Brazilian supermarket chain
Detects emerging stock-out risk from transaction velocity and lead-time variance — opens replenishment tickets before the shortage.
Trigger · Risk threshold crossed
↳ Pre-emptive replenishment
Recommends order quantity, timing, and source per node — calibrated for service level, working capital, and obsolescence.
Trigger · Order cadence + signal
↳ Multi-echelon optimization
Predicts the ideal offer per distributor and routes campaign execution; surfaces under-performing accounts to the sales lead.
Trigger · Channel cycle + score
↳ +21% revenue · top 10 chemical company
Analytics + LLM + Site + Observer working together — continuous competitor pricing, news, and demand monitoring with proactive alerts.
Trigger · Continuous
↳ 300% forecast accuracy lift · top global beverages
Drafts retailer-specific campaigns, allocates promotional budget across SKUs and accounts, monitors lift in real time.
Trigger · Promo planning window
Monitors supplier financial health, news, and delivery patterns — alerts procurement when risk crosses policy threshold.
Trigger · Continuous + risk event
Multi-echelon rebalancing across warehouses and stores — accounting for in-transit, seasonality, and store cluster.
Trigger · Weekly + threshold event
Recommends cluster-level assortment with substitution and cannibalization modeling; surfaces underperforming SKUs to category management.
Trigger · Assortment review cycle
Recommends markdown depth and timing per SKU and store; monitors sell-through and adjusts mid-cycle.
Trigger · Aging inventory + season end
Sizes demand for new-product launches and recalibrates rapidly as early transactions arrive; surfaces over/under-stock risks.
Trigger · Launch event
Surfaces winning patterns across store clusters to district managers; drafts coaching content for under-performers.
Trigger · Weekly performance window
Monitors competitor prices continuously; recommends or executes price moves bounded by elasticity and policy.
Trigger · Price-change signal
Personalized product recommendations across digital surfaces — tuned per customer cluster and channel context.
Trigger · Customer session
Detects abandoned carts and runs recovery cadences (email, SMS, push) calibrated to the customer's response history.
Trigger · Cart abandonment + decay
Inside the CRM — surfaces next-best-offer, next-best-content, and lifecycle stage cues to the marketing team.
Trigger · Campaign / lifecycle
Reads return reasons, classifies severity, and routes resolution — refund, exchange, escalate to product quality.
Trigger · Return event
Negotiates routine PO confirmations, ETA queries, and exception flags with vendors over email/portal.
Trigger · PO event / vendor inbound
Tier-1 store-ops resolution — pricing exceptions, inventory discrepancies, system tickets — across the network.
Trigger · Store inquiry / ticket
Coordinates forecast + replenishment + promo + price + market-intelligence agents on a quarterly commercial plan — one audit trail.
Trigger · Quarterly commercial cycle
↳ Cross-function coordination
300%
Forecast accuracy uplift · top global beverages
90%
Min precision · top Brazilian supermarket chain
+21%
Distributor revenue lift · top 10 chemical company
16% / 21%
Sales mix + total uplift · top Brazilian paint manufacturer
03 · FDE · Retail
Senior engineers embedded with the retailer's merchandising, supply chain, store ops, and digital teams — integrating with ERP, WMS, retail systems, POS, and digital surfaces against the operational SLAs the business runs on.
Pre-built connectors and direct database access into ERP (SAP / Oracle / proprietary), WMS, retail systems, and POS — feeding the forecast and reading positions in near real time.
Insight lands in the distributor portal, the sales rep CRM, and the commercial planning surface — not in a separate analytics tool.
Continuous monitoring of competitor pricing, news, and consumer sentiment — feeding pricing decisions and surfacing aggressive competitor moves.
Decisions land where store and regional teams already work — store-ops apps, district-manager dashboards, replenishment screens.
Rapid-recalibration patterns for new-product launches — early transaction signal feeds the model and updates the forecast within hours.
Drift detection, scheduled retraining, and rollback procedures tuned to retail seasonality — peak readiness is a planned event, not an improvisation.
Anonymized engagements · same engineering team end-to-end
Top global beverages · S&P 500
Multi-channel beverages company
Multi-channel demand forecasting with social-sentiment integration; market-intelligence multi-agent system across competitor pricing and consumer signal.
300% forecast accuracy uplift vs. industry baseline · 50+ surfaced charts and reports · billions in projected revenue lift.
Top Brazilian supermarket chain · 29th ABRAS
Multi-format supermarket chain
14-month forecasting with dynamic frequency adjustment across the assortment; replenishment and stock-out sentinel deployments.
90% minimum prediction precision; pre-emptive replenishment replacing reactive firefighting.
Top 10 global chemical company
Industrial distribution network
Distributor channel optimization, ideal-offer prediction, and competitive-price agents across the distribution network.
+21% distributor revenue · 16% from higher volume · 5% from improved product mix.
Team shape — typical FDE composition for this vertical
Embedded senior engineers, predictive models in production, and agents that execute on every score.
