The modern business landscape is defined by an overwhelming flood of data. From market shifts and competitor moves to regulatory changes and scientific breakthroughs, the information that fuels critical decisions is scattered, unstructured, and relentless. For years, the only solution was to throw more human hours at the problem—analysts spending weeks sifting through documents, news articles, and financial reports.
But what if you could automate the entire research workflow? What if you could deploy autonomous AI agents to gather, synthesize, and analyze information from any source, delivering structured insights directly into your applications?
This is the promise of AI-powered information retrieval, a paradigm shift that turns research from a manual process into a programmable service. With platforms like research.do, businesses can now treat complex research tasks as simple API calls.
Here are the top five most impactful use cases for autonomous AI research that are transforming the finance and tech industries today.
The Challenge: Mergers and acquisitions (M&A) and investment due diligence are notoriously slow and labor-intensive. Analysts must manually review thousands of pages of financial filings, news archives, and legal documents to assess a target company's health, risks, and potential. This process is not only expensive but also susceptible to human error and oversight.
The AI Solution: An AI research agent can execute a comprehensive due diligence checklist in a fraction of the time. By defining a single query, you can task an agent to:
This agentic workflow doesn't just speed up the process; it provides a deeper, more data-driven layer of analysis, uncovering insights that might have been missed.
The Challenge: Staying ahead of the competition requires constant vigilance. Product leaders, marketers, and strategists need to know when a competitor launches a new feature, changes their pricing, or receives a new round of funding. Tracking this manually is a reactive and inefficient cat-and-mouse game.
The AI Solution: Deploy a recurring AI research agent to act as your dedicated competitive intelligence analyst. This agent can be programmed to continuously monitor:
The agent then delivers a synthesized daily or weekly briefing directly to your team's Slack channel or internal dashboard, transforming raw data into actionable strategic insights.
The Challenge: For tech companies, innovation is survival. R&D teams must stay on the cutting edge of scientific discovery by monitoring academic papers, clinical trials, and emerging patents. The sheer volume of information published on platforms like arXiv, PubMed, and patent offices makes this a monumental task.
The AI Solution: An AI agent can act as a tireless research assistant for your R&D department. You can task it to scan specific academic archives for breakthroughs related to your field, such as "quantum computing in finance" or "advancements in solid-state batteries." The agent reads, understands, and synthesizes the information, returning a report that highlights:
This frees up your most valuable engineers and scientists to focus on building, not just reading.
The Challenge: Financial markets react instantly to new information. While quantitative trading models are excellent at processing structured numerical data, they often miss the valuable signals hidden in unstructured text from news articles, earnings call transcripts, and corporate announcements.
The AI Solution: This is a prime example of Business-as-Code. An AI research agent can be integrated into a trading pipeline to perform real-time information retrieval and analysis. When a news story breaks about a company, the agent can:
This turns the world's unstructured news flow into a machine-readable source of alpha.
The Challenge: For financial institutions, staying compliant is a mission-critical, high-stakes endeavor. Regulations change constantly across different jurisdictions, and failing to adapt can result in massive fines and reputational damage.
The AI Solution: Deploy an AI agent to be your automated compliance officer. This agent can be configured to monitor the websites of regulatory bodies (like the SEC, FCA, or MAS) for any new rules, policy updates, or enforcement actions relevant to your business. By transforming regulatory monitoring into an automated workflow, you ensure timely awareness and significantly reduce the risk of non-compliance.
These advanced use cases are no longer theoretical. Platforms like research.do provide the tools to implement them today. Unlike a standard search engine that just returns a list of links, research.do performs the cognitive work of a human researcher.
You define what you need, and the AI agents handle the rest. With a simple API call, you can launch a comprehensive research task.
Here’s a glimpse of how you can turn research into a service with 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');
The agent executes a plan, consults the specified sources, and returns a structured, synthesized report—ready for immediate use in your application or workflow.
The ability to programmatically research, analyze, and retrieve information is a competitive superpower. By leveraging AI agents, businesses in finance and tech can move faster, make smarter decisions, and unlock new opportunities hidden within the world's data. The era of manual research is ending. The era of Research-as-a-Service has begun.
Ready to automate your research workflows? Explore research.do and deploy your first AI agent today.