AI-powered platform for personalized product recommendation
Conversational model for smarter product discovery & purchase.
Project highlights
Solution
Business outcome
The upgraded assistant delivers a cleaner, more trustworthy discovery experience, translating into faster decisions and higher session completion. Users see duplicate-free, well-explained recommendations that match their intent, reducing choice overload and improving confidence at the moment of selection. Clarifying prompts and cluster-based presentation help shoppers converge on the right product without back-and-forth searching, shortening the path from browse to buy.
Operationally, a structured, de-duplicated catalog lowers maintenance overhead and stabilizes upstream data flow. Normalized attributes and consistent categories improve search and filtering, reduce manual corrections, and make it easier to onboard new inventory or vendors without rework. The hybrid retrieval approach (semantic + deterministic) provides predictability when precision matters (e.g., strict specs, exclusions), while preserving the flexibility needed for discovery.
From a product and analytics standpoint, observability is built in. Telemetry on retrieval quality, ranking behavior, and conversation flow supports continuous tuning of prompts, filters, and embeddings. Explanations are generated in plain language and tied to visible attributes, improving audibility and trust for both users and internal stakeholders. The system now supports A/B experimentation on dialog and ranking strategies, enabling data-driven iteration rather than one-off changes.
Commercially, the platform is positioned for scalable growth and new monetization paths. The componentized architecture supports white-label distribution and partner integrations without full re-architecture, and the improved explainability opens opportunities for sponsored placements that remain transparent and user-centric. Together, these outcomes align user experience, catalog integrity, and engineering scalability – supporting measurable gains in engagement and conversion while keeping long-term cost and complexity in check.
Timeline
- Stakeholder workshops to define success measures (relevance, conversion lift, time-to-choice) and guardrails (explainability, determinism, privacy)
- Current-state audit of catalog sources, scraping coverage, duplication patterns, and attribute gaps; draft the canonical product/variant model
- Technical spikes for vector store, rules/SQL path, and dialog manager; decide on service boundaries and integration points
Deliverables: Discovery report, canonical data model (v1), success metrics baseline, architecture blueprint & PoC plan
- Build normalization pipeline to consolidate variants (color/size) into canonical items; implement attribute schema and category hierarchy
- Stand up ingestion & enrichment services; establish quality gates (missing attributes, invalid ranges, dedupe thresholds)
- Backfill embeddings with improved signals; measure retrieval quality vs. pre-normalization baseline
Deliverables: Clean catalog (pilot categories), dedupe metrics, attribute completeness dashboard, embedding refresh (v1)
- Implement hybrid retrieval (semantic + deterministic filters) with clear fallbacks for strict/spec-driven queries
- Design conversational flows to capture intent (use, conditions, fit, budget) and present cluster-based results with short rationales
- Add explanation UX (plain-language “why recommended” tied to visible attributes) and quick refiners
Deliverables: Assistant MVP in staging, top-journey flows, explanation components, initial KPIs (relevance, abandonment, time-to-choice)
Team
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