Case Study: Generating a Market Analysis Report on Quantum Computing in 5 Minutes

Market analysis is the cornerstone of strategic business decisions. It informs product launches, investment strategies, and competitive positioning. But traditionally, it's a grueling, time-consuming process. It involves days, sometimes weeks, of scouring the web, news articles, academic papers, and financial filings, followed by the painstaking task of synthesizing that mountain of data into actionable insights.

What if you could automate that entire workflow? What if you could deploy an AI agent to perform comprehensive research and deliver a structured, data-rich report directly to your application in minutes?

This is the power of research.do. In this case study, we'll walk through a real-world example: generating a comprehensive market analysis report on "Quantum Computing in Finance" using a single API call.

The Challenge: The Manual Research Grind

Imagine you're tasked with understanding the impact of quantum computing on the financial sector. Your old workflow would look something like this:

1. Endless Searching: Spend hours on Google with queries like "quantum computing finance applications," "quantum market size," "major quantum computing companies," "investment in quantum fintech."
2. Information Overload: Open dozens of tabs containing news articles, analyst reports, dense academic papers from arXiv, and SEC filings from public companies.
3. Manual Synthesis: Read through everything, trying to identify key themes, separate signal from noise, and extract critical data points.
4. Report Compilation: Manually copy-paste quotes, stats, and summaries into a document, trying to build a coherent narrative while keeping track of all your sources.

This process is slow, inefficient, and prone to human error and bias.

The Solution: Research-as-a-Service with AI Agents

research.do transforms this manual process into a simple, programmatic function. We use the concept of an Agentic Workflow: you give a high-level goal to an autonomous AI agent, and it formulates and executes a multi-step plan to achieve it.

Instead of manually searching and reading, you write a few lines of code to define your research objective. Let's see it in action.

The Code: One Function to Rule Them All

Here is a simple TypeScript function that uses the research.do SDK to generate our market analysis report.

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}. Include key players, market size projections, primary applications, and recent investment trends.`,
    sources: ['web', 'news', 'sec-filings', 'arxiv'],
    depth: 'comprehensive',
    format: 'json'
  });

  console.log(report.summary);
  return report;
}

getMarketAnalysis('Quantum Computing in Finance');

This single function call replaces hours of manual labor. Let's break down how it works.

• prompt: This is your directive to the AI agent. We're not just asking a simple question; we're requesting a complete report with specific sections: key players, market size, applications, and investment trends.
• sources: This is where the magic happens. We've instructed the agent to look beyond a simple web search. It will simultaneously query web pages, the latest news APIs, financial data from SEC filings, and cutting-edge research from the arXiv academic archive. This ensures a multi-faceted and robust analysis.
• depth: 'comprehensive': We're telling the agent to go deep. It won't just skim the surface; it will follow links, cross-reference information, and perform the cognitive work required for a thorough report.
• format: 'json': This is a crucial differentiator. The agent doesn't return a messy block of text or a list of links. It delivers a clean, structured JSON object.

The Result: Structured Insights, On-Demand

Within minutes, the research.query() call completes. The report object it returns is not just data; it's intelligence. It's structured for immediate use in any application or workflow.

Unlike a search engine that gives you a list of documents to read, research.do has already done the reading for you. The JSON output would contain fields like:

• summary: A high-level, synthesized overview of the market landscape.
• key_players: An array of objects, each containing a company name, description, and relevant activities.
• market_size: A structured object with current valuations and future projections, including the source for each data point.
• applications: A categorized list of use-cases, such as risk modeling, portfolio optimization, and cryptographic security.
• recent_trends: An analysis of recent news and investments shaping the market.
• citations: A complete list of all sources used to generate the report, ensuring full transparency and traceability.

This structured output can be directly fed into a business intelligence dashboard, used to populate a report, or even trigger other automated workflows—a perfect example of Business-as-Code.

Why This Changes Everything

Automating research with research.do delivers a powerful competitive advantage.

• Speed: Go from question to insight in minutes, not weeks. Make faster, better-informed decisions.
• Efficiency: Free up your most valuable asset—your people—from low-level data gathering to focus on high-level strategy and analysis.
• Comprehensiveness: Leverage AI agents to analyze a wider range of sources than a human reasonably could, uncovering insights you might have missed.
• Integration: Embed dynamic, real-time research directly into your products and internal systems.

Ready to stop chasing information and start commanding insights? Turn your research into a service.

Explore the research.do API and get started today.