Most ecommerce teams have tried an AI tool and found it impressive in a demo and hard to trust in production. The gap is rarely the model. It is the connection to real data, the rules about what the agent can do and the way people work with it day to day.
We start with the work, not the technology. We find the tasks that are repetitive, well defined and costly, then build agents that handle them inside the guardrails you set.
Where agents tend to pay off
- Customer support. Order status, returns and product questions answered from your own data, with clean handoff to a person.
- Merchandising. Drafting collections, checking product data and flagging stock or pricing issues.
- Operations. Reconciling orders, chasing exceptions and preparing reports across systems.
- Internal knowledge. Assistants that answer staff questions from your policies, playbooks and catalog.
How we build them
We connect agents to your platform, ERP and support tools through APIs, and where it fits, through an MCP server. We work with models from OpenAI, Anthropic and Google Gemini, chosen for the task rather than by default. Every agent ships with logging, limits and an evaluation plan, covered in more depth under agent governance and evaluation.
For why the underlying architecture matters, read why AI agents need a composable architecture.
