Unlocking Structured Data from Unstructured Information with AI

In today's digital economy, data is the new oil. But there’s a catch: most of the world's most valuable information isn't in neat, orderly databases. It's scattered across a chaotic landscape of web pages, news articles, academic papers, and dense financial documents. This is the world of unstructured information, and for developers and data scientists, it represents a massive challenge. How do you feed raw, human-readable text into an application or AI model that expects clean, structured JSON?

Manually sifting through these sources is slow, expensive, and impossible to scale. Building custom scrapers for each source is a brittle, time-consuming nightmare. The core problem remains: transforming messy, heterogeneous information into actionable, structured data is a major bottleneck for innovation.

This is where research.do introduces a new paradigm. By leveraging autonomous AI agents, we provide an intelligence layer that turns the complex task of research into a simple API call. Our platform is designed to process diverse sources, understand their content, and deliver clean, structured JSON ready for your models and applications.

The Problem with Unstructured Data

Every developer who has tried to build data-driven products knows the pain. You need to gather market intelligence, track competitor mentions, or analyze scientific literature. But the data you need is locked away in formats built for human eyes, not machines.

Financial Filings (SEC): You want to extract key financial metrics and risk factors, but you're faced with thousands of pages of dense, legalistic text.
Academic Papers (arXiv, PubMed): You need to find the latest breakthroughs and experimental results, but the data is embedded in PDFs with complex layouts and specialized language.
News & Web Content: You want to monitor industry trends, but the information is spread across countless articles, blogs, and reports, each with its own structure and style.

The traditional approach of building and maintaining individual parsers for each source is a losing battle. A small change in a website's layout can break your entire data pipeline. This is where an agentic approach fundamentally changes the game.

From Human Task to Agentic Workflow

Instead of just fetching documents like a search engine, research.do deploys autonomous AI agents that perform the cognitive work of a human researcher. This is the core of our "Research-as-a-Service" model.

Here’s how it works:

Define Your Goal: You provide a high-level objective in natural language, like Generate a market analysis report for Quantum Computing in Finance.
Specify Your Sources: You tell the agents where to look—be it the public web, news APIs, academic archives, financial filings, or even your own private document repositories.
Execute and Synthesize: The AI agents create and execute a research plan. They navigate sources, read and comprehend the content, identify the most relevant information, and critically, synthesize findings from multiple documents.
Deliver Structured Insights: The final output isn't a list of links or a wall of text. It's a structured JSON object containing summaries, key findings, extracted data points, and fully cited sources.

From Messy Documents to Clean JSON: An Example

Let's see how you can programmatically turn a complex research query into structured data that's immediately usable in your application. With the research.do SDK, you can launch a comprehensive investigation with just a few lines of code.

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 this example, we’re not just scraping websites. We're tasking an AI workforce to:

Query multiple, disparate source types (web, news, sec-filings).
Understand the high-level prompt.
Perform a comprehensive analysis.
Structure the final output as json.

The report object this returns won't be a raw data dump. It will be a clean, predictable JSON payload, potentially including fields like:

summary: A synthesized executive summary of the findings.
key_players: An array of companies and institutions leading the field.
market_size_projections: Extracted numerical data points with their sources.
recent_developments: A list of key news and filings.
cited_sources: A complete list of all documents used for the analysis.

This structured data can now be directly ingested by other systems—powering a dashboard, fine-tuning an LLM, or triggering another step in an automated business workflow.

Power Your Applications with Actionable Data

By bridging the gap between the unstructured web and structured application data, research.do unlocks new possibilities.

Retrieval-Augmented Generation (RAG): Feed your LLMs with verified, up-to-date, and contextually relevant information from diverse sources to dramatically reduce hallucinations and improve response accuracy.
Automated Business Intelligence: Build systems that continuously monitor markets, competitors, and trends, delivering insights directly to stakeholders without manual intervention.
Data-Driven Workflows: Integrate programmatic research into your existing tools. Trigger a deep-dive analysis on a new sales lead, a potential investment, or a new technology breaking in your industry.

The future of data analysis isn't about building better scrapers. It's about deploying intelligent agents that can understand and synthesize information for you. It's about turning research from a manual chore into a powerful, scalable, and on-demand service.