Multi-tenant SaaS core
Identity, tenancy, memberships, permissions, subscriptions, usage, and audits form the platform boundary.
- Tenant-scoped data
- Encrypted credentials
- Role-based access
- Usage and entitlement controls
Internal Product · Multi-Tenant SaaS
AI Customer Engagement Platform
A multi-tenant AI customer-engagement system for service businesses, designed to handle enquiries, qualify leads, support bookings, and maintain human oversight across digital channels.
Project details
Status
In Development
Year
2024–2026
Role
Product architect & full-stack engineering lead
Industry
Salons, Clinics, Gyms, Real Estate & Service Businesses
Overview
AI Customer Engagement Platform
Service businesses share a common problem: enquiries and booking requests arrive constantly, often outside working hours, while inconsistent follow-up costs revenue and customer trust.
The platform gives each business a separately configured AI assistant grounded in its approved services, policies, availability, and tone. It can answer routine questions, collect qualification details, recommend next steps, and support booking without pretending to be an unrestricted autonomous employee.
Each tenant's configuration, customer data, conversations, integrations, and reporting are isolated within a shared production platform.
Problem
Why this exists
- 01
Missed and delayed responses across websites and messaging channels.
- 02
Inconsistent qualification, appointment handling, and follow-up.
- 03
Generic chatbots that lack business context, governance, or tenant isolation.
Solution
How the system responds
- 01
Tenant-configured knowledge, services, tone, hours, qualification fields, and escalation rules.
- 02
Omnichannel conversation intake, lead capture, booking assistance, and hand-off.
- 03
Grounded retrieval and approval-controlled automation.
- 04
Conversation analytics, audit logs, roles, entitlements, and integration management.
Architecture
How it is designed
AI interaction layer
A retrieval and orchestration layer constrains responses to approved business knowledge and workflow tools.
- Knowledge retrieval
- Prompt and policy versioning
- Tool permissions
- Human hand-off and escalation
Channel and booking layer
Adapters connect web chat, messaging, CRM, calendars, and notifications while preserving idempotency and auditability.
- WhatsApp and web channels
- Calendar availability
- CRM synchronisation
- Reminder and follow-up jobs
Safeguards
Boundaries built into the project
- No cross-tenant data access or shared conversational memory.
- No unsupported medical, financial, legal, or contractual commitments.
- Sensitive or uncertain interactions escalate to authorised staff.
- Tool actions are permissioned, logged, and reversible where possible.
Roadmap
How the work progresses
- 01
Tenant foundation and configuration console.
- 02
Web and messaging channels with grounded knowledge retrieval.
- 03
Lead qualification, booking, CRM, and reporting.
- 04
Production assurance, analytics, vertical templates, and integration marketplace.
Capabilities
What was involved
- AI agent design
- Multi-tenant SaaS architecture
- Business automation
- Booking workflows
- API integrations
Stack
Technologies
- Next.js
- TypeScript
- FastAPI / Node.js
- PostgreSQL
- Redis
- LLM APIs
- WhatsApp Business API
Key Points
Highlights
- 01
Separate knowledge, policy, data, and integrations for every tenant.
- 02
Grounded assistants with explicit escalation rather than unrestricted automation.
- 03
Lead qualification and booking support across digital channels.
- 04
Designed for reusable industry templates without collapsing tenant isolation.
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