Weaviate integration for ecommerce AI and automation

Refresh frequency depends on catalogue volatility and embedding model choice. Fast-moving catalogues may refresh daily via scheduled batch jobs; slower-moving ones may refresh weekly or on explicit product-update triggers. iWeb configures the refresh cadence to balance freshness against embedding-service cost and latency.

Without pre-planned fallback, searches that depend on real-time embedding will fail. iWeb builds fallback paths: batch-pre-computed embeddings for known queries, keyword search as secondary path, or cached embeddings from before the outage. The storefront continues to respond, even if recommendations degrade gracefully.

Weaviate retrieves semantically similar products, but filtering by stock, pricing and business rules happens at the application layer before display. iWeb implements that filtering logic so customers see only buyable recommendations.

Yes. iWeb builds targeted refresh pipelines that re-embed only changed products (detected from ERP/PIM change feeds) and update Weaviate incrementally. Batch refresh runs at off-peak times; real-time product updates trigger immediate re-embedding for critical changes.

iWeb sets up monitoring on embedding freshness (how long since last re-compute), vector-search latency, and recommendation quality metrics (click-through rate, dwell time, conversion lift). Alerts fire when embeddings exceed their refresh SLA or quality metrics drop unexpectedly.

Third-party services (OpenAI, Cohere) offer quality but incur per-call costs and API latency. Local models (Hugging Face, Sentence Transformers) offer cost control and privacy but need GPU capacity and retraining logic. iWeb helps assess your latency, cost and compliance needs and picks the right model infrastructure for your estate.

Weaviate provides semantic similarity and behaviour-based recommendations. CRM and CDP tools provide segment and consent-based personalisation. iWeb orchestrates how signals combine: Weaviate may retrieve candidates, but CRM suppression rules and CDP audience filters apply before display.

Yes, if embeddings are computed from combined inputs. iWeb sets up pipelines that vectorise product images and text together so semantic search understands visual and textual similarity. This requires multi-modal embedding models (CLIP, etc.) and coordinated inference.

Old vectors become obsolete and must be recomputed. iWeb coordinates a blue-green refresh: new vectors are computed in a separate Weaviate index while the old one serves traffic, then traffic is switched. This avoids downtime and ensures search quality during the transition.

Weaviate returns semantically similar products, not filtered by sales or margin. Bias occurs if training data is skewed. iWeb applies explicit diversity rules at the application layer (ensure at least 20% niche products, rotate slow-movers into recommendations, apply margin constraints) to balance business intent with relevance.

Yes. iWeb implements feature flags and staged rollout: a small percentage of traffic is routed to Weaviate-powered recommendations while the rest use existing logic. Metrics (conversion, AOV, CTR) are compared before full deployment.

iWeb monitors ERP/PIM change feeds for deleted products and removes or marks their vectors as inactive in Weaviate. Search and recommendation queries filter out inactive vectors so discontinued products never appear in results.

If customer behaviour (browse, search queries, purchase history) is vectorised and sent to a third-party embedding service, PII may be exposed. iWeb applies pseudonymisation and limits what customer signals are sent to external APIs. Local embedding models avoid external calls but require infrastructure.

Weaviate complements keyword search; it does not replace it. iWeb designs blended results where Weaviate provides semantic candidates and keyword search handles exact matches. Merchandising rules (promotions, forced placements) are applied on top of both ranking signals.