AI Shopping Assistant

Native conversational commerce for eCommerce. Divante builds AI shopping assistants that understand product catalogues, guide buyers and convert at checkout.

+35%
Avg. conversion lift
10x
Faster discovery
Native
Commerce integration
GPT-4
Powered
Trusted by

The next storefront interface is a conversation.

Shoppers with questions abandon. An AI assistant that answers - correctly, instantly, in context - converts. Divante builds AI shopping assistants natively integrated with your commerce platform: the assistant has real-time access to your live catalogue, pricing, stock and personalisation signals.

We use retrieval-augmented generation (RAG) grounded in your actual product data, so the assistant never invents products, prices or availability. Every response cites a real SKU in your catalogue.

Capabilities

AI assistant capabilities

Guided product discovery

Conversational Q&A that narrows a large catalogue to the right product based on requirements and constraints.

Product comparison

Side-by-side comparison of specs, pricing and availability - generated dynamically from live catalogue data.

Cart assistance

Suggest complementary items, flag compatibility issues and apply relevant promotions - all in the chat.

Factual grounding

RAG architecture ensures every statement references a real product in your live catalogue - zero hallucinations.

Personalisation

Purchase history, browsing behaviour and customer segment signals personalise every recommendation.

Evaluation & monitoring

Automated accuracy scoring, conversion tracking and continuous improvement based on real conversation data.

Our process.

01

Catalogue indexing

Build a vector index of your product catalogue, FAQs, buying guides and policy documents. The assistant retrieves real data for every response.

02

Conversational UX

Design the chat interface, entry points, escalation paths and human handoff triggers - embedded in PDP, PLP, cart and a floating widget.

03

Integration & personalisation

Wire the assistant to your commerce API for real-time stock, pricing and purchase history. Personalise recommendations by customer segment.

04

Evaluation & monitoring

Define evaluation metrics (BLEU, factual accuracy, conversion rate) and monitor continuously. Retrain on real conversations quarterly.

Technology

Technology foundation

The tools and frameworks powering production deployments.

Next.js
TypeScript
React
GraphQL
Docker
Redis
Case Studies

Selected client work.

AI-powered eCommerce platform transforming recruitment advertising

Custom AI-driven product matching, dynamic storefronts and checkout optimisation for Germany's largest local portal - rebuilt from the ground up in 14 months.

↑ 3x conversion rate uplift
Read case study →

Yelmo: digital order picking saves 4.7 million print-outs a year

Mobile-first SAP integration that replaced paper-based warehouse workflows across 3 distribution centres.

4.7M print-outs eliminated annually
Read case study →

myAPP reaches 12,000 employees across a global steel empire

Unified employee self-service portal deployed across 50 countries for one of Europe's largest steelmakers.

12,000+ daily active users
Read case study →
All case studies →

Frequently asked questions

Everything you need to know before we talk.

How do you prevent the assistant from inventing products or prices?
We use RAG - retrieval-augmented generation. Before answering, the assistant retrieves relevant products from your live catalogue using vector search. The LLM is instructed to only reference products returned by the retrieval step. We test factual accuracy as a first-class metric.
Which LLMs do you use?
We default to GPT-4o via the Azure OpenAI endpoint for enterprise data residency compliance. We can also deploy Claude 3.5 or a locally-hosted open-weight model depending on your security requirements.
How does it integrate with our commerce platform?
We build a real-time API connector to your commerce backend. The assistant has read access to live catalogue, stock, pricing and - with authentication - to order history and customer data for personalisation.

Still have questions?

Talk to our team - we reply within one business day.

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