In today's data-rich environment, finding the exact information you need, when you need it, can be a significant challenge. Data is scattered across databases, APIs, cloud storage, and various internal systems. This fragmentation makes comprehensive search and retrieval a complex task.
That's where Searches.do comes in. It's an intelligent data retrieval platform designed to help you quickly locate and retrieve the exact data you need from any connected system. Searches.do isn't just a search engine; it's a platform for defining and deploying "Agents" that can perform sophisticated, parameterized queries across your entire data ecosystem.
Imagine needing to look up a customer record scattered across your CRM, a support ticket system, and an order history database. Without a unified solution, this requires separate queries and potentially manual correlation. Searches.do simplifies this by allowing you to define a single "Find Customer" Agent that knows how to query all these sources and return a unified result.
This capability is crucial for making better, data-driven decisions. Accessing the right information quickly and efficiently empowers your teams, streamlines workflows, and ultimately leads to better business outcomes.
Searches.do focuses on empowering you to define intelligent search logic without requiring deep AI expertise. While it can leverage AI concepts in how it processes and structures data, the core is about connecting to your actual data sources and executing precise queries you define.
Think of it as giving your applications and users the ability to ask intelligent questions of your data, regardless of where it lives.
The core concept in Searches.do is the "Agent." An Agent represents a specific type of search or data retrieval task. You define what data the Agent needs (its parameters) and how it should go about getting that data (its handler).
Here's a simplified look at how you might define an Agent to find a customer by their email address:
import { Search } from 'searches.do';
const customerSearch = new Search({
name: 'Find Customer By Email',
description: 'Locates customer records by email address',
parameters: {
email: { type: 'string', format: 'email', required: true }
},
handler: async ({ email }) => {
// Implementation details - this is where you connect to your data source
const results = await queryDatabase({
collection: 'customers',
filter: { email },
limit: 1
});
return results[0] || null;
}
});
In this example:
Once defined, you deploy this Agent to the Searches.do platform. Your applications can then invoke this Agent via a simple API call, providing the email address as a parameter. Searches.do executes the handler and returns the results.
The flexibility of Searches.do lies in its ability to connect to virtually any data source. Your handler function is where you implement the logic to interact with your specific systems.
You can connect to:
Searches.do provides the framework; you provide the connection logic within your Agent's handler.
The ability to define parameters for your Agents is a key feature. This allows you to build highly specific and targeted searches. Instead of just searching for "customer," you can search for a customer by email address, by ID, by sign-up date range, or any other criteria relevant to your data.
This moves beyond simple keyword search and enables truly intelligent data querying.
Searches.do is built to be scalable, handling a high volume of search requests across numerous configured Agents. It provides a simple API and SDKs to easily integrate your deployed Agents into your existing applications, workflows, and user interfaces.
Searching across your data ecosystem shouldn't be a bottleneck. Searches.do provides the tools to define, deploy, and utilize intelligent search Agents that unlock the value hidden within your fragmented data.
Ready to build your first intelligent data query? Explore the Searches.do platform and see how easy it is to start defining your own search Agents.