In today's fast-paced business environment, the speed and quality of information directly impact the quality of decisions. Yet, for many organizations, the process of gathering, analyzing, and synthesizing information remains a frustratingly manual and slow-moving bottleneck. Teams spend countless hours sifting through search results, reading dense documents, and copy-pasting data into spreadsheets. This process isn't just inefficient; it's fragile, inconsistent, and impossible to scale.
What if you could treat your research workflows the same way you treat your software? What if you could define, execute, and automate market analysis or competitor tracking with the same rigor and scalability as a modern CI/CD pipeline? This is the core idea behind Business-as-Code, a transformative approach that is fundamentally changing how companies handle information retrieval.
Think about a typical research task, like generating a market analysis report. The traditional workflow looks something like this:
This manual process is plagued with problems: it's time-consuming, prone to human error, subject to individual bias, and incredibly difficult to reproduce consistently. Scaling this model means hiring more people, which only adds to the cost and complexity.
Business-as-Code proposes a radical solution: represent business processes programmatically. Instead of relying on manual steps and human--read documents, you define the workflow in code. This makes the process:
When applied to information retrieval, this approach turns research from a manual chore into a reliable, automated service.
This is where platforms like research.do come in. They provide the engine to execute these programmatic research workflows. Instead of hiring an army of analysts, you deploy autonomous AI agents to perform the heavy lifting.
With a simple API, you can define a complex research task, specify the sources you want to investigate, and let the AI agents take over. These agents don't just return a list of links like a search engine. They perform the cognitive work of reading, understanding, filtering, and synthesizing information from multiple sources to deliver a structured, actionable report.
Consider this simple code snippet:
import { Do } from '@do-inc/sdk';
const research = new Do('research');
async function getMarketAnalysis(topic: string) {
const report = await research.query({
prompt: `Generate a market analysis report for ${topic}.`,
sources: ['web', 'news', 'sec-filings'],
depth: 'comprehensive',
format: 'json'
});
console.log(report.summary);
return report;
}
getMarketAnalysis('Quantum Computing in Finance');
In just a few lines of code, we've defined a powerful agentic workflow. This code instructs AI agents to:
The result is not a document that someone needs to read. It's machine-readable data that can be directly integrated into a dashboard, fed into another AI model, or used to trigger other business workflows automatically. This is Research-as-a-Service.
Adopting a Business-as-Code approach for information retrieval unlocks several powerful advantages:
The shift from manual processes to code-driven automation is a defining feature of modern business. Just as Infrastructure-as-Code revolutionized IT operations, Business-as-Code is set to revolutionize everything else.
By treating information retrieval as a programmable service, companies can build more intelligent, resilient, and efficient systems. The era of slow, manual research is over. The future is about deploying autonomous agents to deliver critical insights on-demand, turning the overwhelming flood of information into a distinct competitive advantage.
Ready to turn your research into a service? Explore research.do and start building your first programmatic information pipeline today.