Conversational AI for Industrial Commerce

Business context

Our client is a global leader in energy management and industrial automation, with a product catalog spanning hundreds of thousands of items: breakers, controllers, drives, software, and services, each with its own technical documentation, pricing rules, and account entitlements.

That catalog is a competitive strength, but only if customers can navigate it. Their buyers range from electricians and panel builders to procurement teams, and every one of them needs to reach the right product and a confident purchase decision quickly. The buying experience directly shapes revenue, customer loyalty, and the cost of supporting sales.

The problem

For customers, the breadth of the catalog had become the barrier. Finding the right product meant searching a sprawling website, decoding part-number conventions, cross-referencing spec sheets, and moving between search, support, and commerce systems that did not work together.

The result was friction at exactly the moment that mattered: customers struggled to get from a need to a confident order, support teams fielded avoidable questions, and self-service fell short of what buyers expected. The client wanted a simpler promise. A customer should be able to ask, in plain language, and be carried all the way from a question to a placed order.

Our solution

We owned the problem end to end, from the first discovery workshop to the live system. Discovery mapped the real journeys behind the catalog and produced a clear thesis: the experience should be agentic, not a chatbot bolted onto search. Each customer need maps to a specialized capability, and an orchestration layer decides which capability answers a given question.

We treated this as a commerce system, not a demo. It quotes real, entitlement-aware prices, checks live availability, manages a cart, and creates real orders, with trust and authorization built in from the start.

• A natural-language assistant that handles product discovery, comparison, pricing, availability, ordering, and support in one continuous conversation.
• Guided journeys that gather requirements conversationally for complex product families, much like a knowledgeable salesperson would.
• Live integration with the client's existing search, commerce, and identity systems, so answers reflect real catalog, pricing, and account data.
• A graceful handoff to human experts whenever a request goes beyond what the assistant should answer on its own.

The outcome

We delivered a single conversational entry point that turns a complex catalog into a guided, natural-language experience, proven end to end against the client's real enterprise systems.

Because the design is modular, the client can extend it safely over time. New capabilities and integrations can be added without disturbing what already works, giving them a durable foundation to grow from a focused capability into a broad customer-facing one.

Technology

We built the system to operate as a real part of the client's enterprise landscape, not alongside it, with the qualities a Fortune Global 500 environment demands:

• Data and integration: the assistant works against live enterprise data, integrating with the client's existing catalog, commerce, pricing, and identity systems so that every answer reflects authoritative, account-specific information rather than a separate copy.
• Cloud and infrastructure: containerized services run on Kubernetes within the client's Microsoft Azure environment, behind TLS and a secrets-managed configuration model fit for a large enterprise network.
• Scalability: a modular, multi-agent design (orchestrated with LangGraph) lets capacity and capabilities grow independently, scaling with demand on Azure.
• Performance and cost: a provider-agnostic model layer routes each task to the most suitable model from Anthropic or OpenAI, with lighter models for high-volume steps and stronger models for complex reasoning, tuning the cost and quality curve as traffic grows.
• Reliability: durable conversation state across PostgreSQL and Redis keeps multi-turn context fast and consistent, so the experience holds up under real usage.
• Security and trust: authentication, entitlement checks, and on-domain guardrails are built in, because the system quotes real prices and creates real orders.
• Core stack: Python and FastAPI services, a fast TypeScript and SvelteKit web client, and an explainable, individually testable processing pipeline.