From Raw Data to Actionable Insights: Customizing Your API Output Format with research.do

In the age of information, data isn't the problem—it's the bottleneck. We're flooded with raw data from countless sources, but transforming it into a clean, usable format is a manual, time-consuming struggle. For developers building AI applications, analysts creating dashboards, or researchers writing papers, the real work often begins only after the information has been found. The tedious process of cleaning, parsing, and structuring data steals time away from innovation and analysis.

What if you could bypass that step entirely? Imagine an API that not only performs complex AI research for you but also delivers the results in the exact format your application needs. This isn't a futuristic concept; it's the core of what makes research.do a game-changer for any automated workflow.

The Tyranny of the One-Size-Fits-All Data Dump

Traditional information retrieval tools often dump a wall of text or a messy JSON object on you, leaving the hard work of interpretation to your code. This one-size-fits-all approach creates immediate friction.

• For a Business Intelligence Dashboard: You need concise bullet points or key metrics, not a ten-page report.
• For a Retrieval-Augmented Generation (RAG) System: You need clean, cited text chunks to provide as context to a language model, not just a high-level summary.
• For a Market Analysis Briefing: You need a structured report with clear sections and headings for easy executive consumption.

Each use case demands a different structure. Without the ability to specify the output format at the point of request, developers are forced to build and maintain fragile, complex parsers for every new data need.

The research.do Solution: Tailored Insights via a Simple API Call

research.do is built on a simple but powerful premise: Information Retrieval, Simplified. A key part of that simplification is giving you control over the shape of your data. By using the format parameter in your API call, you can instruct our AI agent to synthesize and structure information specifically for your needs.

Let’s look at a practical example. Say you want to generate a comprehensive report on the latest advancements in quantum computing. With research.do, the request is incredibly straightforward:

import { createDo } from '@do-sdk/client';

const research = createDo('research.do');

const report = await research.query({
  question: "What are the latest advancements in quantum computing and their potential impact on cryptography?",
  sources: ["arxiv", "google-scholar", "web"],
  depth: "comprehensive",
  format: "summary_report" // <-- The magic happens here!
});

console.log(report.summary);

In this case, format: "summary_report" tells our AI agent to do more than just fetch data. It actively synthesizes its findings from arXiv, Google Scholar, and the web into a coherent, well-structured report.

But what if you need something different? You can request a variety of output formats tailored to your workflow:

• summary_report: A detailed, multi-paragraph report ideal for deep dives and briefings.
• bullet_points: A concise list of key findings, perfect for dashboards, slide decks, or quick updates.
• raw_data: A collection of sourced facts and quotes with direct citations, designed for RAG systems or fact-checking applications.
• q_and_a: A list of questions and answers derived from the sources, great for generating FAQs or training a chatbot.

Real-World Use Cases for Formatted Outputs

This level of control unlocks new efficiencies for both business and AI workflows.

1. Powering Automated Competitive Analysis

A product manager wants to feed a real-time feed of competitor feature launches into a company dashboard. They can set up a recurring research.do job.

• Query: "What new features have our top 3 competitors launched in the last week?"
• Format: bullet_points
• Result: The API returns a clean, scannable list that can be directly piped into a Slack channel or a Geckoboard widget, no parsing required.

2. Building Smarter, Trustworthy AI Agents

A developer is building a customer support chatbot that needs to answer technical questions about its industry. Accuracy and transparency are paramount.

• Query: A customer's question, such as "What are the security implications of the new Log4j vulnerability?"
• Format: raw_data
• Result: The agent receives verified facts, each with a link back to the authoritative source. The chatbot can then use this context to construct an accurate answer and provide the source to the user, building trust and ensuring reliability.

3. Accelerating Academic Literature Reviews

An academic researcher is starting a literature review, a process that can take weeks. They use research.do to get a head start.

• Query: "Provide an overview of recent studies on protein folding using deep learning."
• Sources: ["arxiv", "google-scholar", "pubmed"]
• Format: summary_report
• Result: The researcher gets a comprehensive, cited summary that maps out the current state of the field, allowing them to focus on analysis and synthesis rather than manual data collection.

Stop Parsing, Start Analyzing

The ability to customize your output format is a crucial component of modern, efficient information retrieval. It transforms your API from a simple data firehose into a true research partner—one that understands not just what you're asking, but how you plan to use the answer.

By combining broad source access, powerful AI-driven synthesis, and flexible output formatting, research.do delivers a complete solution for automated analysis. Stop wrestling with data formats and start building with actionable insights.

Ready to streamline your research workflow? Explore the research.do API and get your key today.