E-Commerce & Retail. Six workloads, one endpoint.
Marketplaces, D2C & omnichannel retail — the workloads below need more than one data model at once, which is exactly why they stall on a stitched stack.
Marketplaces, D2C brands and omnichannel retailers with 100K+ SKUs or 1M+ monthly active shoppers, facing festive-peak concurrency.
The forcing function.
Personalisation, conversational commerce and festive-peak scale are table stakes, and each requires retrieval that spans catalogue, behaviour, reviews and policy simultaneously. Keyword search misses intent; vector-only search misses inventory truth and business rules. The retailers converting best are the ones whose relevance layer joins semantic similarity to live stock and margin in a single query — during the peak, not after.
The abuse graph — collusion, fake accounts and coupon stacking are network shapes invisible to flat per-account rules.
annual GenAI value potential in retail and consumer goods — McKinsey Global Institute
retrieval-accuracy lift from hybrid vector + keyword + rerank over dense-only search
relevance, live inventory and business rules joined at festive-peak concurrency
Six workloads, five query shapes.
Almost no workload here needs only one data model — which is why single-model databases deliver it only with a second system and a sync problem.
Vector similarity over catalogue, browsing and purchase history powers next-best-product, bundles and ‘you may also like’ — joined live with inventory so nothing recommended is out of stock.
Attach rate and AOV lift, without the recommend-then-disappoint failure mode.
Understand shopper intent, not just keywords. Hybrid vector plus full-text relevance handles typos, synonyms, vernacular and natural-language queries.
Search conversion lift and a measurable fall in zero-result queries.
Model accounts, devices and payments as a live graph to catch collusion, fake accounts, coupon stacking and return/refund abuse invisible to flat rules.
Promo and returns leakage recovered — usually a larger number than expected.
Orders, browsing, support and loyalty in one queryable view; graph plus semantic search drive segmentation, churn prediction and next-best-action.
Higher repeat rate and lifetime value per acquired customer.
Auto-generate descriptions, dedupe SKUs, map attributes and surface near-duplicates with managed embeddings across the entire catalogue.
Catalogue quality at scale; listing turnaround for new sellers cut sharply.
Shoppers and staff ask in plain language; the answer blends catalogue, orders, reviews and policies behind one natural-language endpoint with entitlement enforced in-query.
Assisted-sale conversion plus deflection of routine service contacts.
Impact statements are directional targets referenced to published industry research — calibrate against your own baseline.
The stack this replaces in E-Commerce & Retail.
Five licences, five sync jobs, five security perimeters — collapsed into one atomic store.
Relevance, inventory truth and abuse detection in one engine — so personalisation holds up at peak instead of degrading exactly when it matters.