Every developer knows the cycle. It starts with a simple request: "Can you pull a list of all active users from last month?" You write a quick SQL script, export a CSV, and you're done. A week later, another team needs the same data, but with a slight variation. So you copy, paste, and tweak the script. Soon, that same core logic—finding active users—is scattered across cron jobs, analytics dashboards, internal tools, and API endpoints.
Each copy is a point of failure. A tiny inconsistency in one version leads to conflicting reports. A database schema change means hunting down and fixing the same logic in a dozen different places. You're not just wasting time; you're building a brittle, unmanageable system.
It's time to stop the cycle. Instead of treating data retrieval as a series of one-off scripts, what if we treated it as a library of reusable, version-controlled functions? What if we could define our "business as code"?
The ad-hoc query is one of the most common productivity traps in software development. What begins as a temporary solution quickly becomes permanent, technical debt. This approach leads to several critical problems:
The root of the problem is that the query logic is tightly coupled to its implementation. We need to decouple what we're asking for from how the data is fetched.
The solution is to think of every data query not as a script, but as a function. A well-defined, documented, and reusable function that can be called from anywhere.
This is the core principle behind Searches.do, a Search as a Service platform that transforms your data retrieval logic into a library of intelligent, reusable APIs. Instead of a folder full of .sql files, you build a centralized registry of Search Agents.
A Search Agent encapsulates three key things:
Let's see what this looks like in practice.
Imagine you need to frequently look up users by their email address. Instead of writing db.collection('users').findOne({ email }); in multiple places, you define a Search Agent.
import { Search } from 'searches.do';
const findUserByEmail = new Search({
name: 'Find User By Email',
description: 'Retrieves a single user record by their email address.',
parameters: {
email: { type: 'string', required: true }
},
handler: async ({ email }) => {
// Your data retrieval logic goes here
// Connect to any database, API, or data warehouse
const user = await db.collection('users').findOne({ email });
return user;
}
});
// Now callable via a simple, secure API:
// POST /findUserByEmail
// { "email": "jane.doe@example.com" }
With this simple declaration, you've achieved something powerful:
By adopting this "API as a Service" model for your data, you stop firefighting brittle scripts and start building a robust, scalable data foundation. You move from a reactive state to a proactive one, where your data logic is as solid and reliable as your application code.
Ready to stop rewriting queries and start building your library of intelligent data APIs? Your Data, Unlocked.
What is Searches.do?
Searches.do is an agentic workflow platform that lets you define complex data retrieval logic as simple, reusable APIs. Instead of writing one-off query scripts, you build intelligent Search Agents that can be called from anywhere.
How is this different from a standard database query?
Searches.do abstracts the underlying data source and its logic. You define what you want to find (e.g., 'Find Active Users by Region'), and the Search Agent handles how it's done. This makes your searches reusable, versionable, and scalable as services.
What kind of data sources can I search?
You can connect your Search Agents to virtually any data source, including SQL/NoSQL databases, internal APIs, data warehouses, or even third-party services. The handler logic is just code, giving you full flexibility.
Does Searches.do provide AI capabilities?
Yes. You can easily integrate Large Language Models (LLMs) into your handler to perform semantic or natural language searches, allowing users to query data conversationally.