Phase P–R: AI-Powered Services
Google AI integration documentation is already present in the repo, giving a head start here .
P — AI Content Generation: Surface Gemini/GPT integrations into the page builder and dashboard for AI-assisted copywriting, image generation, and data summarization. The GOOGLE_AI_INTEGRATION.md doc already outlines the Gemini Studio setup

Phase P on your shared cPanel stack should deliver **practical Gemini-powered helpers** embedded in the page builder and dashboard, backed by a single Node-based AI service that calls the Gemini API with your API key from GOOGLE_AI_INTEGRATION.md’s setup.[^1][^2]

***

## 1. Overall architecture

- One **AI service module** inside your Node backend:
    - Wraps the Gemini API (via `@google/genai` or HTTP) with functions like `generateCopy`, `summarizeMetrics`, `suggestLayout`.[^3][^1]
    - Reads `GEMINI_API_KEY` from env (cPanel), never from the client.
- Two main UX surfaces:
    - **Page builder (Phase H)**: “Write copy for this section”, “Refine headline”, “Generate section from brief”.
    - **Dashboard**: “Summarize this dashboard”, “Explain these KPIs”, “Draft email/report from current view”.

Gemini’s text-generation API directly supports this pattern: you send a task prompt plus context, and receive generated text back.[^4][^1][^3]

***

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

Create an `ai` module in your Node app:

- Install Google AI client: `npm install @google/genai`.[^1][^3]
- Initialize:

```ts
import { GoogleGenAI } from '@google/genai';

const ai = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY });

async function generateText(prompt: string, systemInstruction?: string) {
  const res = await ai.models.generateContent({
    model: 'gemini-2.0-flash',
    contents: [{ role: 'user', parts: [{ text: prompt }] }],
  });
  return res.response.text();
}
```

Gemini examples show this exact pattern for Node: create a client with `apiKey`, then call `generateContent` or `generateContentStream` for text generation.[^5][^3][^1]

Wrap that with **task-specific helpers**:

- `generateSectionCopy({ blockType, inputs })`
- `summarizeDashboard({ metrics, period })`
- `rewriteCopy({ tone, text })`

Each helper crafts a more structured prompt so the model stays on-spec.

Expose API endpoints:

- `POST /api/ai/generate-copy`
- `POST /api/ai/rewrite-copy`
- `POST /api/ai/summarize-dashboard`

All require auth and use tenant context; none expose the raw Gemini key.

***

## 3. Page builder integration

Inside the GrapesJS-based builder (Phase H):

- For any text block (hero, paragraph, CTA), add an **“AI assist”** button:
    - “Generate copy from brief”
    - “Rewrite for clarity”
    - “Shorten/Expand”
- Flow:

1. User selects a block, opens an “AI” side panel.
2. Enters a short brief or tone (“Professional, focused on automation savings”).
3. Frontend calls `POST /api/ai/generate-copy` with:
        - `blockType` (e.g. `hero-primary`, `pricing-table`)
        - `tenantId` (from auth)
        - current block props (if rewriting).
4. Backend calls Gemini with a prompt that includes:
        - Block type description.
        - The user’s brief.
        - Optional existing text for rewrite.
5. Response text is returned and inserted into the block’s props (user can accept/edit).

Gemini’s text generation examples show simple text prompts; you extend them by adding structured instructions and field names.[^3][^1]

For **bulk generation**, add a “Generate full page draft” button:

- Backend builds a prompt like:
    - “Generate content for a landing page with hero, features, pricing, testimonials for [business description]”.
- It returns JSON describing each section’s text fields; the builder maps that into your block schema.

***

## 4. Dashboard AI summarization

In the dashboard (overview, automations, processes):

- Add a persistent **“Ask AI about this dashboard”** panel:
    - “Summarize current performance for executives.”
    - “Explain why automation rate changed this month.”
- Flow:

1. Frontend gathers current metrics and context (from your existing dashboard API responses).
2. Sends to `POST /api/ai/summarize-dashboard`:
        - A structured JSON: KPIs, trends, alerts, time range.
3. Backend calls Gemini with a prompt like:
        - “You are an analytics assistant. Summarize these metrics in 3–5 bullet points for a business stakeholder. Input JSON: …”
4. Return plain text or markdown string to show in the UI.

Gemini’s multimodal and summarization docs show the same pattern: send structured content and a task description, get a concise natural-language summary.[^6][^7][^1]

Later, you can add **scheduled AI reports** (Phase Q) by reusing the same summarization endpoint inside your automation engine.

***

## 5. Shared-server and multi-tenant considerations

- Config:
    - Store `GEMINI_API_KEY` in cPanel environment variables.
- Limits:
    - Add simple per-tenant rate limiting on AI endpoints to control costs (e.g. 100 calls/day for Starter, more for Pro/Enterprise).[^8][^9]
- Isolation:
    - Never send tenant-identifying secrets to Gemini; only send high-level business descriptors and aggregated metrics, not raw PII.
- RBAC:
    - Gate AI features with permissions: `ai.use_builder`, `ai.use_dashboard`.

***

## 6. Milestones

1. **P1 – Backend AI service**
    - Implement Node Gemini client, `generateText`, and basic error handling.[^2][^1][^3]
    - Add `POST /api/ai/generate-copy` (generic copy generation).
2. **P2 – Builder integration**
    - Add “AI assist” to at least hero and paragraph blocks.
    - Support generate + rewrite flows.
3. **P3 – Dashboard summarization**
    - Add “Summarize dashboard” button.
    - Implement `POST /api/ai/summarize-dashboard` using current metrics.
4. **P4 – Guardrails \& limits**
    - Add per-tenant rate limiting and logging.
    - Adjust prompts to enforce brand tone and style.

Once this is in, you can extend into image generation or more advanced function-calling automations in Q/R using the same Gemini API foundation.[^10][^11][^1]

Do you want the AI in the builder to focus first on headlines/hero sections, or on full multi-section page drafts?
<span style="display:none">[^12][^13][^14][^15][^16][^17][^18][^19][^20][^21][^22]</span>

<div align="center">⁂</div>

[^1]: https://ai.google.dev/api/generate-content

[^2]: https://ai.google.dev/gemini-api/docs

[^3]: https://ai.google.dev/gemini-api/docs/text-generation

[^4]: https://ai.google.dev/aistudio

[^5]: https://github.com/google-gemini/api-examples/blob/6779d2884a5e011173d827626a2c66d947c73cb9/javascript/text_generation.js

[^6]: https://firebase.google.com/docs/ai-logic/generate-text

[^7]: https://docs.cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference

[^8]: https://dev.to/hamzakhan/api-rate-limiting-in-nodejs-strategies-and-best-practices-3gef

[^9]: https://github.com/express-rate-limit/express-rate-limit

[^10]: https://github.com/GoogleCloudPlatform/vertex-ai-creative-studio

[^11]: https://ai.google.dev/gemini-api/docs/function-calling

[^12]: https://gist.github.com/PetroIvaniuk/69091e79a332121d912e9b76a1dbc01b

[^13]: https://github.com/AbiramiSukumaran/GeminiFunctionCalling

[^14]: https://github.com/deepset-ai/haystack-integrations/blob/main/integrations/google-vertex-ai.md

[^15]: https://github.com/google-gemini/api-examples/blob/0d35c672a03be147b83251874b61a8aabce1d0bc/javascript/text_generation.js

[^16]: https://gist.github.com/tanaikech/061cca4b9af67abe8c4244c03750ea30

[^17]: https://github.com/kishoredm/CREWAI-Project-with-Google-Generative-AI-Integration/blob/main/README.md

[^18]: https://github.com/ankit-tejwan/Google_Ai_Studio_Generative-AI

[^19]: https://github.com/googleapis/googleapis/blob/master/google/ai/generativelanguage/v1beta/generative_service.proto

[^20]: https://firebase.google.com/docs/ai-logic/function-calling

[^21]: https://medcitynews.com/2023/09/google-meditech-generative-ai-technology-healthcare/

[^22]: https://ehr.meditech.com/news/meditech-announces-new-ai-use-cases-at-customer-leadership-event

