How to Build Your Customer Intelligence Agent
Building the agent isn't a development project — it's four decisions. Here's exactly how it works.
Pick the platform you already use, add your CRM data, and connect our MCP. After that, the agent returns a live tier list with dollar figures — who's slipping, who's worth chasing, and what a call today is worth — not a CRM snapshot you have to interpret yourself.
Step 1: Find an Agent Builder
Pick the platform you already use, or the one your team is standardizing on. Common choices:
- Microsoft Copilot Studio
- AWS Bedrock
- Google Gemini Enterprise
- Salesforce Agentforce
- Cursor — the walkthrough below uses Cursor; Claude Desktop works the same way
- More platforms being added as they gain adoption
You're not locked in — the same underlying intelligence connects to whichever builder your organization runs on.
Step 2: Add a Data Source
Every agent builder needs to know where your customer data lives. There are two ways to connect it:
CSV / Excel
Export the CSV your CRM already produces — customer IDs, purchase dates, amounts — and upload it. Fastest way to get started, no setup required. If your CRM, Tally, or Excel sheet can export a spreadsheet, you already have everything the agent needs.
MCP
Connect through a standard protocol built for secure, structured access. The agent queries live calculated intelligence instead of a file that went stale the moment you exported it.
What Is MCP, and How Does It Work?
MCP (Model Context Protocol) is an open standard that lets an AI agent connect directly to a live source — instead of you exporting a file and hoping it's still accurate by the time someone reads it.
In plain terms:
- Your agent builder (Copilot Studio, Bedrock, Gemini Enterprise, Agentforce, Cursor, etc.) acts as the client.
- Our MCP acts as the server — it exposes customer intelligence (live A / B / C / D tiers, recency / frequency / spend signals, dollar-at-risk, ranked who-to-call) in a structured, secure format the agent can query directly.
- When your agent needs an answer — "who should I call today, and what's at risk if I don't?" — it asks the MCP server directly, instead of working off a static file someone uploaded weeks ago.
- Because it's a standard protocol, not a custom integration, it works the same way across every agent builder — connect once per platform, and every query after that pulls current, calculated numbers.
The result: your agent isn't reading a snapshot. It's asking a live, governed source of truth every time — which is what keeps the tier list accurate and the access controlled.
Step 3: Choose MCP Where Possible
CSV upload works, but MCP is the preferable path — it's the secure, standardized way for an agent to access live data, rather than a static file that goes stale the moment you export it.
Step 4: Connect Our MCP
This is the step that turns a generic agent builder into a Customer Intelligence Agent. Connecting our MCP is what makes the data behind every answer deterministic — not a guess dressed up as an insight, a calculated briefing you can trust twice.
Pick your platform below. Connecting our MCP is the same idea on each one — you add it once, then sign in with your Saifs AI account.
Steps to Enable MCP on AI Platforms
Open Claude and reveal the main menu
Launch Claude Desktop. In the top-left corner, click the three-dot menu to open the app's main menu.
Go to Settings
From the three-dot menu, hover over File. Click Settings (or press Ctrl + Comma).
Open the Developer config editor
In Settings, select the Developer tab. Click Edit Config at the bottom of the panel.
Locate the config file
A File Explorer window will open at the Claude config folder. Click to select claude_desktop_config.json.
Paste the Customer Intelligence MCP
With the config file open, add this server — not the generic Saifs AI MCP. This one is Customer Intelligence only. Then restart Claude.
{
"mcpServers": {
"customer-intelligence": {
"command": "npx",
"args": [
"-y",
"mcp-remote",
"https://customer-tiering-mcp.saifs.ai/mcp"
]
}
}
}
Launch Cursor
Open the Cursor application on your desktop. On the home screen, choose Open project, Clone repo, or Connect via SSH to begin working on a project.
Settings → Tools & Integrations
Click the gear icon (top right) or go to File → Settings. In the left panel, click Tools & Integrations. Scroll to MCP Tools and click Add Custom MCP.
Add the Customer Intelligence MCP
After clicking Add Custom MCP, Cursor opens the MCP config file. Add this server — not the generic Saifs AI MCP. This one is Customer Intelligence only.
{
"mcpServers": {
"customer-intelligence": {
"url": "https://customer-tiering-mcp.saifs.ai/mcp"
}
}
}
Click Connect
Click Connect next to the new MCP server under MCP Tools to start the integration.
Allow extension access
When prompted, select Open so Cursor can process the URI and start the login flow.
Choose a login method
On the authentication page, sign in with Google, Microsoft, Apple, or Facebook — the same Saifs AI account you already use.
Complete authentication
After you sign in, a prompt will ask to finish the connection. Click Open to complete the MCP integration in Cursor.
Verify the agent is on
The toggle should be ON. That confirms the Customer Intelligence Agent is connected.
Open ChatGPT Settings
From the ChatGPT home page, go to Settings.
Open Connectors
Click Connectors in the settings menu.
Create a connector
Click Create in the browse connectors section.
Add the Customer Intelligence MCP
Enter this server — not the generic Saifs AI MCP. This one is Customer Intelligence only. Then click Save.
Server URL: https://customer-tiering-mcp.saifs.ai/mcp
Name: Customer Intelligence
Save and start asking
Click Save. You can now ask who is slipping, what's at risk, and who to call today in ChatGPT.
Install an MCP-compatible client
Open the VS Code marketplace and install an MCP-compatible client.
Open the command palette
Open the command palette, type MCP: add, and select MCP: Add Server to start the wizard.
Choose HTTP server
Search MCP: Add Server and choose HTTP (HTTP or Server-Sent Events) to connect to a remote MCP server.
Enter the Customer Intelligence MCP URL
Input this server URL — not the generic Saifs AI MCP. This one is Customer Intelligence only. Press Enter to confirm.
https://customer-tiering-mcp.saifs.ai/mcp
Choose installation scope
When prompted, choose Global (all workspaces) or Workspace (this workspace only).
Allow authentication
Allow the server to authenticate so VS Code can talk to the Customer Intelligence MCP.
Verify the server is running
The MCP server should show as Running. That confirms the Customer Intelligence Agent is connected in VS Code.
Open Windsurf
Launch Windsurf, then open an existing project folder or create a new one from the welcome screen.
Configure the MCP server
Open or create mcp_config.json in your project or settings directory and add the Customer Intelligence server.
Access MCP server settings
Open Windsurf settings and go to Cascade or MCP Servers. Click Manage MCPs or Open MCP store.
Add the Customer Intelligence MCP
Use Manage MCP servers or View raw config. Add this block — not the generic Saifs AI MCP. This one is Customer Intelligence only.
{
"mcpServers": {
"customer-intelligence": {
"command": "npx",
"args": [
"-y",
"mcp-remote",
"https://customer-tiering-mcp.saifs.ai/mcp"
]
}
}
}
Verify the agent is on
The toggle next to the server should be ON. That confirms the Customer Intelligence Agent is connected in Windsurf.
What Our AI Agent Can Actually Do — A Walkthrough
Once connected, our AI Agent isn't answering from a static file — it's querying live, calculated intelligence every time. Here's what that looks like in practice.
Say you manage a book of 200 accounts. You export the CSV your CRM already produces. Every night, without anyone rebuilding a dashboard, the MCP scores the book:
- Every customer gets a live tier — A, B, C, or D. Not a snapshot from last quarter's QBR, and not a gut feeling. The standing is based on actual value and behavior — where every account stands today.
- Our AI Agent flags what a static CRM list would miss. Recency collapsed, frequency dropped, spend pattern shifted — the account is about to slip a tier. Nobody complained. The pipeline still looks busy. By the time your revenue chart shows the drop, that moment is gone.
- Our AI Agent converts that signal into a dollar figure. Not "this account is At-Risk," but "$18K at risk, $12K–$24K range, 90% confidence." Numbers your finance team will actually trust, because they come with a confidence range, not a bluff.
- You ask our AI Agent a plain question. In Cursor, Copilot Studio, or wherever the agent lives, you type: "Who should I call today?" Our AI Agent queries the MCP in real time and answers: "Call Acme today — $18,000 at risk if you don't. An hour with Beta Ltd could unlock $12,000 in upgrade value."
- Our AI Agent explains itself when asked. You follow up: "Why Acme?" It doesn't deflect — it shows the why: recency collapsed, frequency dropped, spend pattern shifted, and exactly which signals moved the score.
- Your team acts — same day, not next quarter. A call prioritized by our AI Agent instead of guessed from Monday's CRM list is the difference between finding out an account left after the revenue chart shows it, and calling them three weeks before they were ever going to leave.
That's the shift: from a customer list you have to hunt through, to a to-do list with a dollar sign next to every name — so you know exactly where an hour of your time is worth the most.
"Here's our CRM export — who should I call today, and what's at risk if I don't?"
"Which accounts are about to slip from B to C, and what's that worth?"
"Give me the book-level number: how much is at risk across neglected accounts this week?"
Why Connect Our MCP
Deterministic data
Same input, same output, every time. The numbers you get are calculated by proven statistical models, not generated as a plausible-sounding guess.
Privacy
Full SOC 2 compliance. Your customer transaction history stays where it belongs — no black-box vendor holding a copy you can't audit, and no data leaving your premises.
Less token usage
Purpose-built models do the heavy lifting instead of an LLM re-processing your entire export on every query — cheap enough to run nightly on every account.
Battle-tested
Built on statistical models with decades of published, peer-reviewed use — the same category of customer-value math used since the 1930s, not a new algorithm hoping it works on your book.
Connect MCP for Creating a Customer Intelligence Agent
You've seen how it works — deterministic, private, battle-tested, and ready to run on the platform you already use. Connecting takes minutes, and every briefing after that tells you who's slipping, what it's worth, and who to call today.
Connect Our MCP →No credit card. No data science team. Just your CSV and a few minutes.
