A chatbot that invents answers costs you trust and sales. I build AI agents that actually help your customers: they answer from your real catalog and policies, guide shoppers to the right product, capture qualified leads, and hand off to a human when it matters. The orchestration runs on LangGraph as an explicit state machine, so every step is traceable instead of a black box, and each agent is confined to a small set of approved tools so it can never touch anything it should not. This is not a slide deck. A grounded sales agent already runs live on this site and inside my headless commerce demo.
Agent Capabilities
Grounded, Not Guessing
The agent answers only from your catalog, docs, and policies through a hybrid retriever, so it recommends real products and quotes real terms instead of hallucinating. When it does not know, it says so and offers a human.
LangGraph Orchestration
Multi-step flows (qualify, search, compare, capture) run as an explicit LangGraph state machine with a checkpointer. Every transition is inspectable and testable, which is what makes an agent safe to put in front of paying customers.
Least-Privilege Confinement
Each agent gets a whitelisted set of tools and nothing else. Architecture tests assert it cannot reach the filesystem, secrets, or shell, and the whole thing is red-teamed against the OWASP LLM Top 10 before it ships.
Lead Capture & Handoff
The agent qualifies intent and hands the conversation to you: a structured lead lands in Telegram and email with fixed recipients, so a late-night visitor becomes a real contact instead of a bounce.
Proven in Production
This is running, not theoretical. A grounded sales agent is live on samcheek.com and embedded in my headless commerce storefront, built with the same streaming UX, i18n, and guardrails I would ship for your store.
How I Build the Agent
- 1
Scoping - Defining the agent's job, its tools, and the questions it must answer from your data
- 2
Grounding - Building the retrieval pipeline over your catalog, policies, and docs
- 3
Orchestration - Modelling the flow as a LangGraph state machine with guardrails
- 4
Confinement - Whitelisting tools and adding red-team and architecture tests
- 5
Handoff - Wiring qualified leads to your inbox and chat, then streaming it into the UI
Agent Stack
Expected Business Outcomes
Delivery impact this service is designed to produce in production.
- Grounded, Not Guessing
- LangGraph Orchestration
- Least-Privilege Confinement
Related Resources
Use this content path to evaluate implementation depth before we start your project.