<img src="https://r2cdn.perplexity.ai/pplx-full-logo-primary-dark%402x.png" style="height:64px;margin-right:32px"/>

# Lets expand on this into a dev plan and for starters we are going to use a shared server, with c-panel and node.js app install capabilities, also has database capabilities and domain and dns management:

R — AI Business Assistant: Add a persistent AI chat sidebar to the dashboard where clients can query their own business data ("What were my top 5 leads this week?" or "Summarize my sales pipeline")

Treat the AI Business Assistant as a **chat layer over your existing APIs and databases**, implemented as one Node “chat” service plus a React sidebar UI. It doesn’t have to be full RAG on day one; you can start by wiring Gemini to your own metrics endpoints.[^1][^2][^3]

***

## 1. High-level behavior

On your shared cPanel stack:

- Add a **chat sidebar** to the main dashboard (persistent on all app routes).
- User asks: “What were my top 5 leads this week?” or “Summarize my sales pipeline.”
- Backend:
    - Interprets the question.
    - Calls your existing APIs / DB for relevant data (dashboards, automations, CRM once available).
    - Calls Gemini with the structured data and the question.
    - Returns a grounded answer and optionally follow-up suggestions.

This is the same pattern used by AI-powered dashboards and RAG-for-SaaS examples: query → retrieve from your data → LLM summarizes.[^4][^5][^6][^7][^1]

***

## 2. Backend chat service (Node on cPanel)

### Data model

Add:

- `ai_chat_sessions`
    - `id`
    - `tenant_id`
    - `user_id`
    - `title` (optional, from first question)
    - `created_at`, `updated_at`
- `ai_chat_messages`
    - `id`
    - `session_id`
    - `role` (`user`, `assistant`, `system`)
    - `content` (text/JSON)
    - `created_at`

This gives you multi-turn context and an audit trail per tenant/user, as recommended for SaaS AI assistants.[^5][^8][^6]

### API endpoints

- `POST /api/ai/chat`
    - Body: `{ sessionId?, message }`.
    - Creates session if needed, appends user message.
    - Runs the assistant pipeline (see below).
    - Stores assistant reply, returns `{ sessionId, messages }`.
- `GET /api/ai/chat/sessions`
- `GET /api/ai/chat/:sessionId/messages`


### Integration with Gemini

Reuse your Phase P Gemini integration, but in **chat mode**:

- Maintain a message history (user/assistant) for the session.
- Build a prompt:
    - System: “You are an AI business assistant for AIISTECH. You can only answer about the tenant’s own data using summaries provided in context.”
    - Context: structured JSON with recent metrics / sales pipeline / leads, based on routing logic (below).
    - Messages: last N turns from `ai_chat_messages`.

Gemini’s chat guides support multi-turn conversations with streaming; you can start with non-streaming, then add streaming for nicer UX later.[^9][^2][^3][^10]

***

## 3. Routing questions to data (v1, no heavy RAG yet)

On shared hosting, start with **heuristics + your APIs**, not a full vector-store RAG stack:

1. **Classify the question**:
    - Simple rules or a small LLM call:
        - “leads”, “pipeline” → CRM / sales endpoints.
        - “automation”, “bot”, “process” → operations dashboard.
        - “billing”, “MRR”, “revenue” → billing/Stripe data.
    - You can either:
        - Use a tiny local rule set, or
        - Call Gemini with a short classification prompt to choose a data source.
2. **Fetch data** based on classification:
    - Reuse your existing backend services:
        - Dashboard metrics (`/api/dashboard/:tenantId/summary`, `trends`, `processes`, `alerts`).
        - Automations/operations APIs.
        - Billing and CRM once you add them.
    - Build a compact JSON context: top N rows, aggregated stats, date range.
3. **Ask Gemini**:
    - Prompt: “Given this data (JSON) and the user’s question, answer using only the data. If something is unknown, say you don’t know.”
    - Include safeguards to avoid hallucinating cross-tenant or unsupported data.

This is a light-weight “retrieval + generation” pattern (a small RAG) using your SQL + APIs as the retrieval layer. You can layer a vector store later if you want free-form document Q\&A, following RAG patterns like in the SaaS examples.[^6][^7][^4][^5]

***

## 4. Sidebar UX in the dashboard

In your React dashboard:

- Add a **chat icon** fixed to the right; clicking opens a drawer/sidebar.
- UI:
    - Conversation view with messages.
    - Input box with “Ask about your data…” placeholder.
    - Optional “starter prompts” like:
        - “Summarize this dashboard.”
        - “What changed compared to last week?”
        - “List my top 5 processes by volume.”
- For extra context:
    - Send current route and filters (e.g., which dashboard tab, date range) along with the user question, so the backend can prioritize matching data.

This matches patterns in AI-assisted dashboards where chat lives alongside visuals and can “see” the current view.[^11][^12][^13][^14]

***

## 5. Multi-tenant, security, and limits

- **Tenant isolation**:
    - Every chat request uses `tenant_id` from auth middleware.
    - Data retrieval functions **always** filter by `tenant_id`.
    - Never send identifiers from other tenants to the model.
- **Rate limits**:
    - Per-tenant + per-user limits on `/api/ai/chat`, similar to Phase P, to control cost.[^15][^16]
- **RBAC**:
    - Permission like `ai.assistant_use`; maybe restrict pipeline/billing queries to certain roles.
- **PII handling**:
    - Start by only sending metrics and aggregated data to Gemini, not raw PII fields like full contact details.

***

## 6. Milestones

1. **R1 – Core chat backend**
    - `ai_chat_sessions` and `ai_chat_messages` tables.
    - `/api/ai/chat` endpoint with Gemini chat call (no data retrieval yet).
2. **R2 – Sidebar UI**
    - Dashboard chat sidebar with multi-turn history.
    - Basic “general assistant” answers (grounded mostly in prompts).
3. **R3 – Data-aware assistant v1**
    - Simple classifier → calls your dashboard APIs.
    - Answers such as:
        - “Summarize our automation KPIs for the last 30 days.”
        - “Highlight unusual alerts.”
4. **R4 – Deeper sources \& refinement**
    - Add CRM/billing/automations as data sources.
    - Add shortcuts/buttons that pre-fill queries from the current dashboard context.

On your current shared-server AIISTECH path, R1–R3 are achievable without extra infra beyond your Node app + DB + Gemini key, and they turn your existing analytics into a conversational interface rather than yet another AI toy.

Would you prefer the assistant’s first version to be **read-only analytics** (summaries \& explanations) or to also trigger actions (like starting automations) from chat?
<span style="display:none">[^17][^18][^19][^20][^21][^22][^23]</span>

<div align="center">⁂</div>

[^1]: https://github.com/munas-git/AI-powered-sales-dashboard

[^2]: https://firebase.google.com/docs/ai-logic/chat

[^3]: https://github.com/google-gemini/example-chat-app

[^4]: https://github.com/sagaruprety/rag_for_saas

[^5]: https://github.com/sagaruprety/rag_for_saas/blob/main/README.md

[^6]: https://www.thenile.dev/blog/building_code_assistant

[^7]: https://arsenaltech.com/blog/introduction-to-rag-retrievalaugmented-generation-for-saas-applications

[^8]: https://www.doc-e.ai/post/mcp-and-rag-the-new-standards-for-saas-ai-architecture

[^9]: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/samples/generativeaionvertexai-gemini-chat-completions-streaming

[^10]: https://ai.google.dev/gemini-api/docs/text-generation

[^11]: https://blog.com.bot/ai-business-chat-dashboard/

[^12]: https://www.cleed.ai/platform-dashboard

[^13]: https://www.prototypr.ai/dashboards

[^14]: https://www.databricks.com/product/business-intelligence

[^15]: https://dev.to/hamzakhan/api-rate-limiting-in-nodejs-strategies-and-best-practices-3gef

[^16]: https://github.com/express-rate-limit/express-rate-limit

[^17]: https://github.com/jddev273/streamed-chatgpt-api

[^18]: https://github.com/aws-samples/sample-amazon-q-business-productivity-tools

[^19]: https://github.com/patryk-ku/gemini-node-cli

[^20]: https://github.com/Nytheon-ai

[^21]: https://github.com/google-gemini/example-chat-app/blob/main/README.md

[^22]: https://github.com/adithya-s-k/RAG-SaaS?tab=readme-ov-file

[^23]: https://github.com/nytheon-ai-bot

