Build Your Shopping Agent with Conscia's
Hybrid AI Orchestration Engine
Hey, I’m looking for a jacket — something stylish but not too heavy.
Got it — lightweight, stylish for city weather. Let me pull up a few options for you.
Ooh, I like the bomber style.
Excellent choice — bomber jackets never miss. Here are a few that hit that sweet spot between casual and cool.
Ask me a question
Vodafone (TOBi)
70%
of Vodafone’s ~1 million TOBi interactions each month are resolved on first contact.
Domino's Pizza
80%
of Domino’s orders are now handled by Dom, their AI voice assistant—which has already surpassed 500,000 orders in North America.
Verizon
40%
sales lift after Verizon rolled out GenAI assistants; reps can now answer 95% of queries with AI support.
Dr. Martens x Conscia:
Redefining Product Discovery Through Conversation
A 4-Day Proof of Concept That Brings the Future of Brand-Owned Shopping Experiences to Life
At MACH X, Dr. Martens and Conscia joined forces to demonstrate what the next generation of commerce experiences looks like: a Conversational Discovery Interface (CDI) that merges natural-language interaction with Dr. Martens’ visually rich, on-brand shopping experience.
Built in just four days and powered by Conscia’s Hybrid AI Orchestration Engine, this proof of concept connects data, context, LLMs, and business logic to deliver an intelligent, fully orchestrated, and brand-governed experience.
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Why You Need an Orchestration Layer for Conversational Experiences
An orchestration layer sits between your chat interface and your enterprise systems — coordinating every API call, data retrieval, and business rule in real time. It ensures that when the customer asks,
“Show me formal shoes under $150 that ship today,”
the agent can instantly:
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interpret intent through the LLM,
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retrieve real-time context and memory,
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query multiple systems for availability, pricing, and content,
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apply business rules,
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and respond conversationally — all within milliseconds.
Components of a Shopping Agent
1
Memory and Context Management
Keep every conversation grounded in real-time customer context. Conscia maintains short- and long-term memory of each interaction — from preferences and purchase history to active carts and recent searches — so your AI agent never starts from zero.
2
Retrieval-Augmented Generation (RAG)
Ground your AI’s responses in your organization’s own knowledge and data. Conscia’s RAG pipeline ensures every answer is backed by verified product, policy, and content data — delivering accuracy, compliance, and brand consistency across every interaction.
3
LLM Orchestration
Understand user intent and determine the best course of action with precision. Conscia orchestrates LLM-driven reasoning and deterministic workflows — enabling your shopping agent to know what the customer means and how to respond intelligently.
4
Action Execution
Don’t just retrieve information — take action. From updating inventory to placing orders or applying discounts, Conscia allows your AI agent to securely execute transactions across any connected system in real time.
5
Knowledge Graph & Semantic Index
Organize and model your organization’s knowledge in a graph-based datastore enriched with a native semantic index. This gives your AI agent a deep understanding of how products, content, and entities relate — enabling contextual recommendations and precise retrieval.
6
Business Logic & Guardrail Enforcement
Stay in control of every interaction. Conscia enforces business rules, compliance logic, and AI guardrails to ensure that every response, recommendation, and transaction aligns with your brand’s policies and governance standards.
FAQs about building a Shopping Agent
If you're like most brands looking to offer conversational capabilities on your website or mobile app, you probably have questions about how to go about doing so. Here are some we've been hearing lately:
Question:
We’re moving from an onsite chatbot to a conversational shopping agent—how do we make it actually sell, not just answer?
Question:
Why do we need an orchestration layer for a conversational shopping experience?
Answer:
Because AI agents and chat interfaces don’t talk to backend systems directly — they interact with capabilities. The orchestration layer transforms fragmented APIs across CMS, PIM, Commerce, and ERP into unified Experience APIs like /discoverProducts or /checkout. This abstraction ensures data is stitched, ranked, and contextualized before reaching the conversational UI.
Question:
How is an orchestration layer different from a traditional BFF (Backend-for-Frontend)?
Answer:
A BFF is custom-coded for one channel; Conscia’s orchestration layer is channel-agnostic. The same orchestration flow powers your web, mobile, and conversational channels — reducing duplication, speeding up delivery, and enforcing consistent business logic everywhere.
Ask me a question
Answer:
Great question. The difference comes down to orchestration. Most chatbots just surface data they’ll tell you what’s in the CMS or maybe return a static FAQ. But selling requires action. That means stitching together pricing from your ERP, inventory from OMS, promos from loyalty, and customer context from your CRM all in real time. That’s where Conscia comes in. We sit between your agent and those backend systems, unify the logic, and return one decisioned response. So instead of “let me check,” your agent can say, “Here’s the best in-stock bundle for your membership tier, and I’ve added it to your cart.”
53 % Customers Abandon Checkout When Questions Go Unanswered
Commerce is shifting from “search & click” to “ask & get.” Agentic AI is already driving the flow—handling discovery, bundling, inventory, and checkout in one step. Brands with orchestration-ready data will win. The rest will scramble as traffic bypasses traditional funnels.
Conscia’s Universal MCP Server turns your data and business logic into agent-ready APIs—enabling you to power conversational interfaces both onsite and third party agents such as ChatGPT, Perplexity and more.