Retail AI is high-volume, low-margin work where every percentage point matters. NKKTech ships AI for retail and e-commerce that moves conversion + AOV at the right architectural depth — semantic search that respects merchandising rules, recommendations that respect inventory, dynamic pricing with guardrails, customer support that knows your return policy. Shopify Plus, Magento, BigCommerce, headless commerce, custom marketplaces.
Six capability patterns we ship most often for retail + e-commerce clients.
Real-time recs based on session behavior + historical preferences + inventory + merchandising rules. Two-tower model with item embeddings; offline + online eval framework.
Replace keyword search with embedding-based + hybrid retrieval. Handles natural-language queries ("black dress for a fall wedding"), typos, synonyms. Conversion lift typically 8–15%.
RAG-backed bots that know your products, return policy, shipping rules. 60–80% deflection on Tier-1 tickets. Multi-channel (web, WhatsApp, Slack-for-internal).
Inventory-aware, competitor-aware, margin-aware pricing recommendations. Always with human-in-the-loop guardrails (no autonomous price changes without approval ranges).
Real-time scoring on orders, returns, account creation. Reduces chargebacks 30–50% with measured false-positive control. Plugs into Stripe Radar, Signifyd, or custom rules.
Product descriptions in brand voice, SEO-optimized variant titles, ad creative generation, customer-review summarization.
1 week. Review current funnel, identify highest-ROI AI bets, prioritize.
4–6 weeks. Build + ship one high-impact capability (usually search or recs). Measure lift.
4–8 weeks. Add adjacent capabilities; deepen platform integration; build eval framework.
Monthly retainer for iteration. Retail AI is never "done" — it iterates with seasons, catalog changes, and customer behavior.
Visual search (upload a photo, find similar), size-recommendation, dynamic outfit suggestion, return-prediction (size-based abandonment).
Skin-tone matching, ingredient Q&A, personalized routine building, AR try-on integration.
Room-design AI, dimension verification (does this fit your space?), AR placement, replacement-part identification from photos.
Recipe-to-cart, dietary-aware recs (vegan, gluten-free, allergen), substitution intelligence.
Trust + safety automation, listing quality scoring, fraud detection on sellers + buyers, dispute triage.
Churn-risk scoring, win-back automation, personalization of subscription boxes, taste-graph modeling.
No, and any AI that tries to is bad AI. The patterns that work in retail are merchandiser-in-the-loop: AI surfaces recommendations + draft pricing + ad copy, human merchandisers approve, override, and learn from the AI's misses. We've never shipped a retail AI system without human-in-the-loop controls.
Search and recs: 2–4 weeks from production launch to measurable lift. Fraud detection: 4–8 weeks (need volume of data to validate). Dynamic pricing: 8–12 weeks (slow rollout with guardrails to protect margin). We commit to eval-driven measurement on every project.
Hybrid embedding + collaborative-filtering models handle cold-start better than pure CF. For brand new products: content-based embedding (description + image + category). For brand new customers: session-based recs from first-touch behavior. We design for cold-start from day one.
USD 40K–120K for a pilot covering one capability. Scaled platform engagement: USD 200K–600K over 6–12 months. Recurring LLM/embedding cost typically $300–$3,000/month at mid-market e-comm scale; far less than your existing search infrastructure (Algolia/Coveo) in most cases.
Yes. About 40% of engagements augment existing platforms (Dynamic Yield, Bloomreach, Algolia) rather than replace. We add capabilities the vendor doesn't have, or improve specific use cases where the vendor's models underperform on your catalog.
NKKTech delivered our LLM document processing pipeline on time and exactly on budget. The tech lead was available on Slack daily. First offshore team that actually worked the way we expected.
Tony's team understood our legacy PHP system faster than our internal team. Zero downtime migration, exactly as promised. The bilingual PM made communication seamless.
We went from 15 hours/week of manual prospecting to fully automated lead gen in 8 weeks. ROI in 60 days as Tony promised.
NKKTech delivered our LLM document processing pipeline on time and exactly on budget. The tech lead was available on Slack daily. First offshore team that actually worked the way we expected.
Last updated: · Reviewed quarterly for accuracy.
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