Building an AI-Powered Internal Search Engine in Under an Hour

Internal tools are the unsung heroes of a company's operations. They power customer support, manage inventory, and provide critical business insights. Yet, building them is often a thankless task, relegated to the bottom of the backlog. One of the most common and frustrating features to build is search. Crafting complex SQL, handling edge cases, and making it user-friendly can take days, if not weeks.

What if you could build a powerful, intelligent search engine for your internal applications in less time than it takes to get lunch?

With database.do, you can. This tutorial will walk you through creating a smart internal search tool that understands user intent, requires zero complex query logic, and can be deployed in under an hour.

The Pain of Traditional Internal Search

If you've ever built search for an internal dashboard, you know the headaches:

• Complex Queries: You're hand-writing SELECT statements with multiple JOINs, LIKE clauses with wildcards, and complex WHERE conditions. It's brittle and hard to maintain.
• Rigid and Unforgiving: A traditional search for "John Smith" won't find "Jon Smith" or "John S." Users need to know the exact data structure to get results.
• Slow Development Cycles: Every new filter or searchable field means another round of backend development, testing, and deployment.
• Data Silos: What if you need to search for a user and their corresponding support tickets, which live in different databases? The complexity skyrockates.

This is where an AI-native approach changes everything. Instead of telling the database how to find something, you simply tell an AI agent what you want to find.

Meet database.do: Your AI-Native Data Agent

database.do is an AI-powered agentic workflow platform that provides a unified, intelligent API for interacting with your databases. Think of it as a brilliant data assistant who understands your database schema and can perform complex operations on your behalf.

By connecting your data sources to database.do, you gain access to:

• Intelligent Search: Use natural language or simple objects to perform context-aware searches.
• Unified CRUD API: A consistent way to create, read, update, and delete records, regardless of whether you're using Postgres, MongoDB, or another database.
• Database as Code: Manage and interact with your data using simple, readable function calls within your application code.

Let's stop talking about it and start building.

Step-by-Step: Build Your Intelligent Search

For this tutorial, we'll imagine we have a users table in our database and we want to build a UI for our support team to quickly find customer information.

Step 1: Connect Your Database

First things first, you need to grant database.do access to your data.

1. Sign up at database.do.
2. Navigate to the "Data Sources" section and click "Add New."
3. Choose your database type (e.g., PostgreSQL, MySQL, MongoDB) and provide your connection credentials.

Security is paramount. database.do ensures all connections are encrypted and provides robust credential management, so your data remains secure and under your control.

Step 2: Install the SDK and Initialize the Client

Now, let's bring the power of database.do into your application. We'll use the Node.js SDK for this example.

First, install the package:

npm install @do-sdk/core

Next, initialize the client in your project. You'll need your API key from the database.do dashboard.

// src/lib/doClient.ts
import { createDo } from '@do-sdk/core';

// Initialize the .do client with your API key from an environment variable
const doClient = createDo(process.env.DO_API_KEY);

export default doClient;

Step 3: Implement the Search Logic

This is where the magic happens. Instead of writing a massive SQL query, we'll write a single, intuitive function call.

Let's create an API endpoint that will take a search query from our frontend.

// src/pages/api/search-users.ts
import doClient from '../../lib/doClient';

export async function POST(request: Request) {
  const { query } = await request.json();

  if (!query) {
    return new Response(JSON.stringify({ error: 'Query is required' }), { status: 400 });
  }

  try {
    // This is it. This is the entire backend search logic.
    const users = await doClient.database.search('users', {
      query: query,
      limit: 10, // Optional: limit the number of results
    });

    return new Response(JSON.stringify({ users }), { status: 200 });

  } catch (error) {
    console.error('Search failed:', error);
    return new Response(JSON.stringify({ error: 'Failed to search for users' }), { status: 500 });
  }
}

Notice what we're doing here with doClient.database.search. We're not specifying columns or writing LIKE clauses. We can pass a query like "active user from California named John" and the AI agent will:

1. Parse the natural language query.
2. Identify the key intents: a user status (active), a location (California), and a name (John).
3. Map these intents to the appropriate columns in your users table (e.g., status, state, first_name).
4. Construct the optimal, performant database query behind the scenes.
5. Return the most relevant results.

Your frontend can now call this endpoint with the user's input from a search bar, instantly creating a powerful and intuitive user experience.

Step 4: Beyond Search with Intelligent CRUD

The power of database.do isn't limited to search. The entire suite of CRUD (Create, Read, Update, Delete) operations is simplified.

Need to find a specific user by their email?

import doClient from '@do-sdk/core';

async function findUserByEmail(email: string) {
  const user = await doClient.database.findUnique('users', {
    where: { email },
  });

  console.log('Found user:', user);
  return user;
}

Need to update a user's status?

async function updateUserStatus(userId: number, newStatus: string) {
  const updatedUser = await doClient.database.update('users', {
    where: { id: userId },
    data: { status: newStatus },
  });

  console.log('User updated:', updatedUser);
  return updatedUser;
}

The unified API remains the same, abstracting away the underlying database syntax and complexity. This dramatically speeds up development and makes your codebase cleaner and easier to maintain.

Your Internal Tools, Reimagined

In under an hour, we've designed a secure, intelligent, and scalable search functionality that would have traditionally taken a significant engineering effort. By leveraging an AI-native data access layer, you can stop wasting time on boilerplate data logic and start focusing on building features that truly empower your team.

Ready to interact with your data like never before?

Get started with database.do today and build your first AI-powered workflow.