AI Development for eCommerce

LLM integrations, AI product recommendations, smart search, and conversational commerce. Divante builds AI features that ship to production - not just demos.

+26%

Avg. order value with AI (McKinsey)

3×

Conversion lift with AI search

Production

AI features, not just prototypes

18 yrs

eCommerce engineering expertise

AI that ships to production. Not just demos that win pitches.

Most AI integrations stall at the prototype stage. The gap between a compelling demo and a feature that runs reliably in production - handling real traffic, edge cases, and data quality issues - is exactly where projects fail. We close that gap.

We bring 18 years of eCommerce engineering to every AI project. That means we know the catalogue structures, integration patterns, and performance constraints of the platforms you're already running. Our AI work is grounded in engineering reality: we scope what's feasible, build to your stack, and measure outcomes against revenue - not just accuracy benchmarks.

Capabilities

AI built for commerce.

LLM Integration

Production-grade integration of large language models (OpenAI, Claude, Gemini, open-source) into your commerce platform. Prompt engineering, context management, rate limiting, cost control, and fallback handling - the plumbing your LLM feature actually needs.

AI Product Recommendations

Personalised "also bought", "complete the look", and cross-sell recommendations trained on your catalogue and purchase data. Integrates with your existing CDP, PIM, and commerce platform - delivering uplift in AOV from day one.

AI-Powered Smart Search

Semantic search that understands buyer intent, not just keywords. Vector embeddings, synonym handling, typo tolerance, and intent classification - returning relevant results even when customers search in natural language or use non-catalogue terms.

Conversational Commerce & AI Shopping Assistant

LLM-powered shopping assistants that guide customers through discovery, answer product questions, and handle post-purchase queries. Built on your product catalogue and connected to your order management system - not a generic chatbot bolted on top.

Predictive Analytics & Demand Forecasting

ML models trained on your historical order and inventory data to forecast demand, flag slow-moving stock, and surface re-order signals. Reduces overstock and stockout costs - outputs integrate directly with your ERP or OMS.

AI-Powered Personalisation

Real-time personalisation of homepage banners, category page ordering, email content, and promotional offers - driven by individual customer signals, cohort modelling, and session context. Consistently the highest-ROI AI investment in eCommerce.

How we build AI for production.

Feasibility & data audit

We assess your catalogue, order history, customer data, and platform capabilities. Not all AI use cases are viable on every stack - we tell you what will move the needle and what is premature given your current data maturity.

Architecture & integration design

We design the AI feature architecture against your existing platform - whether that's Shopify Plus, commercetools, Adobe Commerce, or a custom stack. We define the API contracts, data pipelines, fallback behaviour, and observability requirements before writing a line of model code.

Build & validate

We build iteratively, running offline evaluation and A/B tests against your real traffic. Every model or LLM feature ships with monitoring dashboards, latency budgets, and clear rollback criteria - so you can ship with confidence.

Deploy, measure & iterate

We deploy to production with full observability, run controlled experiments to measure revenue impact, and iterate based on results. AI is not a one-time project - we support you through the continuous improvement cycle that makes the difference between a feature and a competitive advantage.

Case Studies

AI in production.

Results from brands that shipped AI features with Divante.

AI SearchPersonalisation

meinestadt.de - 3× conversion with AI-powered commerce

We integrated semantic search and AI-driven product recommendations into meinestadt.de's local commerce platform. Semantic search replaced keyword-only queries across a 2M+ SKU catalogue - reducing zero-results pages by 68% and lifting search-to-purchase conversion 3× within 60 days of launch.

Read case study

RecommendationsPersonalisation

Fashion retailer - +26% average order value with personalised recommendations

We replaced a rules-based "also bought" engine with an ML recommendation model trained on 18 months of purchase history and real-time session signals. The model runs at <40ms p95 latency on commercetools. AOV increased 26% across recommendation-exposed sessions in the first quarter post-launch.

Read case study

Conversational CommerceLLM

B2B distributor - AI Shopping Assistant reduces support tickets by 44%

We built an LLM-powered shopping assistant connected to a 50,000-SKU technical catalogue and the client's OMS. The assistant handles product compatibility questions, alternative suggestions, and order status queries - resolving 44% of inbound support volume and freeing the sales team to focus on high-value accounts.

See AI Shopping Assistant

Frequently asked questions

Common questions about AI development for eCommerce.

How do we know if our data is good enough to start an AI project?

We start every AI engagement with a data audit. The minimum viable dataset varies by use case - recommendations typically need 6-12 months of purchase history with session context, while LLM integrations can start immediately with your product catalogue. We will tell you honestly what you have, what you need, and what is achievable now versus in 6 months. We do not take on AI projects where we cannot see a credible path to production outcomes.

Which LLMs or AI models do you work with?

We are model-agnostic and work with OpenAI (GPT-4o, o1), Anthropic (Claude 3.5 Sonnet), Google (Gemini 1.5 Pro), and open-source models (Llama 3, Mistral) deployed via cloud providers or on-premise. The right choice depends on your latency requirements, data residency constraints, cost targets, and the specific task. We benchmark options against your real use case before recommending a stack.

How long does it take to ship an AI feature to production?

A focused AI integration (e.g. LLM product Q&A over your catalogue) can reach production in 4-6 weeks. A full recommendation engine with A/B testing infrastructure typically takes 8-12 weeks. The data audit and architecture phase (weeks 1-2) are the most important investment - they prevent the expensive rework that happens when assumptions about data quality or platform constraints prove wrong mid-build.

How do you measure the ROI of AI features?

Every AI feature we build ships with a measurement framework agreed upfront: the primary KPI (AOV, conversion rate, support ticket deflection, zero-results rate), the A/B test design, the sample size required for statistical significance, and the timeline to a decision. We run controlled experiments, not just before/after comparisons - so the numbers are defensible and account for confounding factors like seasonality.

Can AI features be added to our existing platform without a full re-platform?

Yes. We specifically design AI integrations to work alongside your existing stack rather than requiring a platform change. Whether you're on Shopify Plus, Adobe Commerce, VTEX, SAP, or a custom build - we integrate via APIs, middleware, or edge functions depending on what your architecture allows. A re-platform is never a prerequisite for adding AI capabilities.

Start with an AI feasibility assessment.

We'll review your platform, data, and goals - and tell you exactly which AI use cases are viable today and what you'll need to unlock the rest.