Work & Scenarios

Typical Engagements

Anonymized delivery patterns

These scenarios describe how we typically design and ship AI, software, and commerce systems. They are representative patterns—not named clients unless explicitly authorized.

Client names and logos are shared only with written permission. Until then, we publish scenario-based work samples.

Support Agent

Cross-border commerce customer service

Client type

Mid-size cross-border retail · anonymized

Challenge

High ticket volume, repetitive order and shipping questions, and product knowledge scattered across docs and spreadsheets.

Solution

Deployed a multi-step support agent with order lookup tools, a RAG product knowledge base, and human escalation when confidence is low.

Technologies

LLM · RAG · Order API · Ticketing / chat integration

Outcome

Faster first response on common questions and fewer repetitive tickets routed to the human team.

RAG Copilot

Internal knowledge assistant

Client type

Operations-heavy SaaS team · anonymized

Challenge

Policies, runbooks, and product docs lived in multiple drives—search was slow and answers were inconsistent.

Solution

Built a document Q&A copilot with access control, citation snippets, and summarization for long manuals.

Technologies

Vector DB · RAG pipeline · SSO · Admin review queue

Outcome

Teams spend less time hunting files and more time acting on consistent, sourced answers.

Commerce Stack

Unified cross-border + WeChat mini program

Client type

Growing DTC brand · anonymized

Challenge

Web storefront, mini program, and back-office orders were disconnected—duplicate catalog work and slow iteration.

Solution

Single backend for catalog, inventory, and orders; WeChat mini program for member and purchase flows; shared customer chat entry points.

Technologies

FastAPI · MySQL · WeChat Mini Program · Payment APIs

Outcome

One team maintains one stack across web and super-app channels.

Ops Copilot

Sales & operations copilot

Client type

B2B sales organization · anonymized

Challenge

Reps spent hours pulling CRM notes, drafting follow-ups, and preparing meeting briefs manually.

Solution

Copilot connected to CRM and internal metrics—drafts emails, summarizes accounts, and suggests next steps with human approval.

Technologies

LLM · CRM API · Role-based access · Audit logs

Outcome

Less prep time before calls and more consistent follow-up quality across the team.

Live Commerce AI

Live-selling assist & post-stream insights

Client type

Live commerce merchant · anonymized

Challenge

Hosts needed faster product talking points, on-air FAQ handling, and usable summaries after each stream.

Solution

AI-generated scripts from catalog data, live-session chat assist with guardrails, and post-stream highlight summaries.

Technologies

LLM · Catalog API · Live chat hooks · Analytics dashboard

Outcome

Shorter prep cycles and more consistent messaging during high-traffic live sessions.

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