Project Proposal — Multi-Tenant AI Web Platform

01What We're Building

A secure, cloud-hosted web platform serving multiple client organizations (tenants) from one codebase. Each tenant gets an isolated workspace with three core capabilities:

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AI Chatbot

A conversational assistant that answers questions grounded in each tenant's own documents and data, with sources cited — no hallucinated answers.

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Live Dashboard

An interactive dashboard of KPIs, trends, and metrics with filters, date ranges, and drill-down — updated from the tenant's data.

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Data Analytics

Built-in analysis tools: trend detection, breakdowns, comparisons, and exportable reports surfaced automatically from the underlying data.

02Multi-Tenant Architecture

One platform, many isolated clients. Each tenant's data, users, and AI context are strictly separated — no client can see another's data.

Tenant-isolated data partitioning (row-level / schema-level separation)
Per-tenant user management & role-based access (Admin, Member, Viewer)
Per-tenant branding, settings, and data sources
Centralized super-admin console to onboard & manage tenants
Scoped AI retrieval — chatbot only sees the active tenant's data
Usage tracking per tenant (queries, storage, activity)

Effort Distribution by Area

03How It Works

Data flows from the client's sources, through an AI processing pipeline, into the chatbot and dashboards.

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1 · Ingest

Client uploads documents / connects data. System parses, cleans, and structures it automatically.

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2 · Index

Content is chunked, embedded, and stored in a searchable vector index, scoped per tenant.

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3 · Serve

Chatbot retrieves relevant data and answers; dashboard & analytics render live from the same source.

Security by design: encrypted storage, authenticated APIs, tenant-scoped queries enforced at the data layer, and audit logging.

04Scope & Effort Breakdown

Eight phases from kickoff to handover. Hours are AI-assisted estimates.

Phase	Key Deliverables	Hours
1. Discovery & Architecture	Requirements, data model, tech design, environment setup	12
2. Multi-Tenant Foundation	Auth, RBAC, tenant isolation, DB schema, app skeleton	28
3. Data Pipeline & Ingestion	Document parsing, chunking, embeddings, vector store	30
4. AI Chatbot (RAG)	Retrieval, grounded answers w/ citations, chat UI	36
5. Dashboard & Analytics	KPI widgets, charts, filters, drill-down, exports	32
6. Admin & Tenant Management	Super-admin console, onboarding, usage tracking	16
7. Testing, QA & Security	Test suite, tenant-isolation checks, hardening	20
8. Deployment & Handover	Cloud deploy, docs, walkthrough, support setup	12
Total Estimated Effort		186 h

Hours per Phase

Cumulative Build Progress

05Indicative Timeline

Roughly 6 weeks at a steady pace, with phases overlapping where practical.

DiscoveryWeek 1

Multi-Tenant CoreWeek 1–2

W1–2

Data PipelineWeek 2–3

W2–3

AI ChatbotWeek 3–4

W3–4

Dashboard & AnalyticsWeek 4–5

W4–5

Admin ConsoleWeek 5

QA & SecurityWeek 5–6

W5–6

Deploy & HandoverWeek 6

06Technology Stack

Proven, modern, open-source-first components — no vendor lock-in.

Frontend

React / Next.jsTypeScriptTailwindChart.js / Recharts

Backend

Node.js / PythonREST / API layerPostgreSQLAuth & RBAC

AI Layer

LLM (Open-Source / proprietary)RAG pipelineVector DB (Qdrant / pgvector)Hybrid retrieval

Infrastructure

Cloud hostingContainerizedCI/CDEncrypted storage

07Assumptions & Notes

Estimate covers an MVP with the scope defined above; net-new feature requests are quoted separately.
Client provides data/documents, sample content, and timely feedback during review cycles.
Third-party costs (LLM API usage, cloud hosting, domains) are billed at cost, separate from build hours.
One standard design language; heavy custom UI/UX design is out of scope unless added.
Hours assume AI-assisted development; a ±15% contingency is recommended for unknowns.
Post-launch support & maintenance available under a separate ongoing agreement.