Retail AI that sells, supports and learns from every conversation
Conversational commerce on WhatsApp, recommendation engines, catalogue intelligence and support agents that resolve order issues, all connected to Shopify, custom stores and payment providers.
Where AI pays off in E-commerce & Retail
Conversational commerce
Catalogue, cart, payments and abandoned-cart recovery inside WhatsApp.
Personalisation
Recommendation and ranking systems with experiments that prove the lift.
Order support agents
Status, returns and exchanges handled automatically with the store's APIs.
E-commerce & Retail at a glance
How we help
Problems we solve in E-commerce & Retail
Each maps to a reference architecture and to outcomes the industry has documented.
Support queues that never shrink
Tier-1 questions consume most of the team's day, answers vary by agent, and customers wait. Generic chatbots deflect badly because they do not know your policies or your systems.
How the system works
- Customer message
- Intent & policy check
- Retrieve from your docs & orders
- Answer or act via tools
- Escalate with context
Outcome: Klarna's assistant handled two thirds of customer chats in its first month and cut resolution time from 11 minutes to under two; Intercom reports Fin resolving 76% of conversations on average. Klarna press release
ArchitectureSales conversations stuck on personal phones
Orders, questions and promotions run through WhatsApp with no history, no automation and no way to see what drove revenue.
How the system works
- Ad or QR opens chat
- AI agent answers from catalogue
- Cart & payment in chat
- Order synced to store
- Campaign & follow-up
Outcome: 53% of retailers already automate messaging on the WhatsApp Business Platform (Infobip), and the same grounded-agent pattern turns that channel into a store, a support desk and a campaign tool with attribution. Infobip WhatsApp statistics
ArchitectureEvery question needs an analyst
Leaders wait days for a report, dashboards disagree with each other, and the data team is a queue. Raw data sits in the warehouse without becoming a decision.
How the system works
- Raw sources
- Lakehouse with partitions & quality checks
- Semantic layer of metrics
- Text-to-SQL agent
- Dashboards & alerts
Outcome: In dbt's own test, questions answered through a governed semantic layer reached 83% accuracy, against a 16.7% raw-SQL baseline in the data.world benchmark it replicated; vendors report time-to-insight falling by over 99% with 62% adoption within a year. dbt: the semantic layer as the data interface for LLMs
ArchitectureLet's build intelligent systems that drive growth
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