Vietnam-first AI chatbot SaaS for social commerce, customer support automation, ecommerce conversations, and knowledge base RAG.
Datazen-AI helps businesses answer customer messages 24/7 across social messaging channels, ground AI replies in approved business knowledge, recommend products from catalogs, and hand conversations to human staff when needed.
It is designed for ecommerce shops, F&B brands, clinics, education providers, local service businesses, and teams that sell or support customers through Facebook, Instagram, Zalo, and chat-first channels.
- Why Datazen-AI
- Who It Helps
- Product Capabilities
- Use Cases
- Architecture
- Tech Stack
- Deployment Shape
- Security Principles
- Status
- FAQ
- Keywords
Most small and mid-sized businesses lose time answering the same questions again and again: price, stock, size, booking, delivery, refund policy, service details, opening hours, and follow-up support.
Datazen-AI turns those repeated conversations into a supervised AI workflow:
| Problem | Datazen-AI approach |
|---|---|
| Customers expect fast replies | AI chatbot automation answers routine questions 24/7 |
| Staff repeat the same answers | Knowledge base RAG retrieves approved answers from documents and FAQs |
| Product questions need catalog context | Ecommerce product search grounds replies in product, variant, price, stock, and category data |
| AI should not handle every situation | Human takeover inbox lets staff step in, transfer, release, and continue conversations |
| SaaS operations need control | Multi-tenant isolation, subscriptions, quotas, usage tracking, audit logs, and token health workflows |
Datazen-AI helps shops answer questions about product availability, size, color, variant, shipping, warranty, and returns while keeping replies connected to product data.
Restaurants, coffee shops, salons, and local chains can share menu details, branch information, opening hours, booking intent, and service details without forcing staff to reply manually to every repeated question.
Service teams can explain packages, pre-qualify leads, collect follow-up details, and escalate sensitive or high-value conversations to staff.
Managers get an inbox, audit trail, usage visibility, and handoff workflow instead of a black-box chatbot.
Datazen-AI is built for social messaging workflows where customers already ask questions.
- Facebook Messenger chatbot automation
- Instagram DM automation
- Facebook comment reply automation
- Zalo OA messaging flows
- Telegram bridge and staff inbox workflows
- Webhook-driven tenant routing and platform delivery
The bot can answer from approved business knowledge instead of relying only on a generic prompt.
- Upload documents, FAQs, menus, policies, catalogs, and guides
- Extract, chunk, embed, and search knowledge
- Use pgvector-backed semantic retrieval
- Store files and rendered assets with S3-compatible storage such as Cloudflare R2
- Review document status, readiness, chunks, and knowledge audit events
Datazen-AI connects conversations to product data so replies can be useful for buying decisions.
- Product catalog, variants, categories, prices, and stock
- Product search function calling
- Shopping-oriented response templates
- Catalog-backed answers for fashion, cosmetics, F&B, retail, and service catalogs
AI can automate routine conversations, but staff keep control.
- Conversation inbox
- Customer profile sidebar
- AI context sidebar
- Takeover and release bot control
- Staff transfer and manual reply flows
- Read state, typing state, conversation events, and realtime updates
Datazen-AI includes a CRM foundation for customer-aware chatbot workflows.
- Customer records and channels
- Interactions and timelines
- Notes, tasks, tags, and segments
- Consent and audit models
- Encrypted PII support
- AI tool handler for saving user information during chat
The platform is built as a SaaS foundation, not a single-tenant automation script.
- Plans, features, subscriptions, quotas, payments, and addons
- OpenAI usage tracking
- Tenant-level quota checks
- Admin provisioning and operational dashboards
- Audit logs and token health workflows
| Industry | Common questions | Datazen-AI workflow |
|---|---|---|
| Fashion ecommerce | "Is this available in size M?", "Do you have this in black?", "How much is shipping?" | Product search + policy knowledge + human handoff for order details |
| F&B | "Can I see the menu?", "Are you open tonight?", "Can I book a table?" | Menu knowledge + branch info + lead capture |
| Clinics and spa | "How much is this service?", "Which package fits me?", "Can someone call me?" | Service FAQ + pre-qualification + CRM capture + staff handoff |
| Education | "What course should I take?", "What is the schedule?", "How much is tuition?" | Course knowledge + lead qualification + follow-up |
| Support teams | "Where is my order?", "How do I return this?", "I need a human" | Knowledge base answer + escalation workflow |
Datazen-AI combines a Laravel + Vue SaaS control plane with a NestJS AI runtime and a lightweight mobile staff inbox.
| Layer | Responsibility | Stack |
|---|---|---|
| SaaS control plane | Tenant management, onboarding, subscriptions, social connection OAuth, knowledge UI, bot config, inbox, billing, admin tools | Laravel, Vue 3, Inertia, Tailwind CSS, Reverb |
| AI runtime | Webhooks, queues, tenant routing, OpenAI orchestration, RAG, function calling, streaming responses, platform adapters | NestJS, TypeScript, Prisma, TypeORM, BullMQ, Redis |
| Mobile staff inbox | Staff inbox workflows and notification entry points | React, TypeScript, Vite, Telegram Mini App |
High-level runtime flow:
Customer message or comment
-> platform webhook
-> tenant routing
-> queue, debounce, and preprocessing
-> moderation and intent detection
-> agent runtime, prompt templates, and tools
-> OpenAI response or streaming response
-> knowledge, product, or CRM function calls when needed
-> platform delivery
-> dashboard, inbox, audit, quota, and notifications
Data model:
- Central database: tenants, subscriptions, platform metadata, capabilities, prompt templates, OpenAI model/project metadata, and shared configuration.
- Tenant databases: connected pages, bot configuration, conversations, knowledge documents, ecommerce data, CRM data, notifications, and audit events.
- Redis: queues, routing cache, quota checks, debounce windows, realtime coordination, and runtime cache.
| Area | Technology |
|---|---|
| SaaS app | Laravel, PHP, Vue 3, Inertia.js, Tailwind CSS |
| Runtime API | NestJS, TypeScript, Prisma, TypeORM |
| AI | OpenAI, prompt templates, function calling, streaming responses |
| Retrieval | PostgreSQL, pgvector, embeddings, RAG |
| Queue/cache | Redis, BullMQ |
| Realtime | Laravel Reverb |
| Storage | Cloudflare R2 / S3-compatible object storage |
| Staff mobile inbox | React, Vite, Telegram Mini App |
Typical production deployments use containerized services:
- SaaS web app
- Queue workers
- Scheduler
- Realtime server
- Message subscriber
- AI runtime instances
- Redis
- PostgreSQL with pgvector
- Reverse proxy and TLS termination
All production secrets, API keys, database credentials, and platform tokens should be configured through private environment variables. They should never be committed to GitHub.
- Tenant data isolation
- API-key protected internal services
- Encrypted platform tokens and sensitive customer data
- Webhook verification
- Usage and quota controls
- Audit-friendly operational records
Datazen-AI has implemented core foundations for multi-tenant chatbot SaaS, knowledge base/RAG, ecommerce search, social messaging, inbox operations, subscription/quota tracking, and AI runtime orchestration.
Some advanced areas, such as CRM automation depth, channel rollout scope, analytics breadth, and deployment automation, may vary by environment and should be validated for each production launch.
No. It is a SaaS platform around chatbot operations: onboarding, social connections, knowledge management, prompt/runtime configuration, inbox, audit logs, quota usage, and staff handoff.
Yes. Datazen-AI is designed as a knowledge base chatbot and RAG chatbot. Business documents, FAQs, policies, menus, catalogs, and guides can be processed into searchable knowledge for AI responses.
Yes. Human takeover is a core workflow. Staff can supervise conversations, take over, reply manually, transfer, and release the bot when appropriate.
Yes. Datazen-AI is Vietnam-first and built around social commerce workflows common to Vietnamese ecommerce, F&B, service, clinic, education, and local business teams.
No. This public repository is a product landing and GitHub SEO repository. The production application code is maintained separately.
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Proprietary. Contact Datazen for commercial use, partnership, and deployment inquiries.