PROJECT STUDY
PrototypeAI Customer Service
From a useful answer to a connected human handoff.
A support demo connecting cited AI answers, policy-checked tools, human handoff, an agent CRM, and a versioned knowledge CMS.
Explore the build
At a glance
- Stage
- Prototype
- Industry
- Retail and food delivery
- Context
- Internal product
- Capabilities
- 6
- Technologies
- 12
- Year
- 2026
The challenge
A support assistant needs more than fluent replies.
It needs the current policy, the relevant order facts, permission to use a tool, and a clear path to a person when judgment or approval is required.
This internal demo explores that connected workflow through fictional shopping and food-delivery scenarios. It brings the customer conversation, the human agent workspace, and the knowledge behind an answer into the same system.
What we built
The implemented demonstration connects four working areas:
Customer experience
Fictional marketplace and food-delivery catalogs, orders, status updates, complaints, and a help conversation provide realistic workflow context. Commerce and payments are simulated
Grounded AI support
The assistant uses retrieved, approved knowledge and structured order information to prepare cited answers. A deterministic offline fallback supports the demo when the AI provider is unavailable
Policy-checked tools
A backend executor checks ownership, policy, risk, limits, confirmations, and idempotency before recording a simulated refund, cancellation, or other action. The model cannot directly change the database
Human handoff and CRM
Staff can work from a queue, take over a conversation, reply, review suggested answers, and route actions requiring supervisor approval
Versioned knowledge CMS
Documents pass through ingestion, extraction, chunking, review, approval, and publishing. Retrieval uses the versions appropriate to the conversation's access context
Operational activity
Metrics and audit views make conversation, tool, and retrieval activity visible without pretending those views establish a business improvement
Customer experience
Fictional marketplace and food-delivery catalogs, orders, status updates, complaints, and a help conversation provide realistic workflow context. Commerce and payments are simulated
Grounded AI support
The assistant uses retrieved, approved knowledge and structured order information to prepare cited answers. A deterministic offline fallback supports the demo when the AI provider is unavailable
Policy-checked tools
A backend executor checks ownership, policy, risk, limits, confirmations, and idempotency before recording a simulated refund, cancellation, or other action. The model cannot directly change the database
Human handoff and CRM
Staff can work from a queue, take over a conversation, reply, review suggested answers, and route actions requiring supervisor approval
Versioned knowledge CMS
Documents pass through ingestion, extraction, chunking, review, approval, and publishing. Retrieval uses the versions appropriate to the conversation's access context
Operational activity
Metrics and audit views make conversation, tool, and retrieval activity visible without pretending those views establish a business improvement
Realtime connections keep customer and staff workspaces aligned as a conversation moves between the assistant and a human agent.
How it works
React interfaces, FastAPI backend
React and TypeScript provide the customer and staff interfaces. A Python FastAPI backend owns the source records, permissions, domain rules, and action executor.
A guarded conversation runtime
The conversation runtime combines structured understanding, knowledge retrieval, workflow tools, and response checks. Hybrid retrieval uses vector and keyword signals. A response guard prevents the assistant from claiming an action succeeded without a confirmed tool result.
Local data, documented deployment paths
The current demo uses SQLAlchemy and a local SQLite database, with a NumPy-based vector index and FastEmbed. PostgreSQL, pgvector, and Redis have documented deployment paths; no production-scale distributed deployment is claimed.
Current stage
Prototype
- Research
- Prototype
- Pilot
- Production
This is an implemented internal demonstration with synthetic data and simulated commerce actions. It does not move live customer funds and is not an integration with a named marketplace or payment provider.
Read the full status
The repository contains backend policy, security, retrieval, handoff, and scenario tests, plus frontend and browser-test tooling. Those suites were not rerun during this portfolio update. Gemini and scanned-document OCR require configured provider access for separate live validation.
No real support client, public production deployment, resolution rate, customer-satisfaction improvement, or cost reduction is claimed.
Where this fits
Support becomes more useful when answers, records, and human decisions connect. We build AI solutions around your knowledge and workflows, and pair them with business systems and web applications that help your team act. Discuss your support workflow with Weaveward.
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Let’s connect the dots
Let's put these ideas to work.
Share a process, product, or problem you want to improve. We will work through the possibilities together.