Ninja Deep Research: The AI Agent Everyone Can Actually Start Using Now | HackerNoon

2025 is heralded as the year of AI agents by venture capitalists, tech giants, and even Time Magazine.

Unlike chatbots, AI agents can autonomously complete tasks for you and identify when they’re needed. They use a combination of prompted instructions, reasoning capabilities, and real-time data from the apps and services that you use every day.

Imagine you’re preparing for a meeting. With Ninja AI’s Deep Research, for example, you could have it automatically gather your company’s competitor news and generate an in-depth report on key insights and actionable recommendations. You could even enhance this with industry analysis and expert opinions drawn from diverse sources, including files, video, and audio.

Sequoia Capital predicted that this year we can expect to see swarms of agents augmenting—rather than cutting out—professionals in these ways. They’re already seeing instances where agents working alongside sales have tripled performance compared to when team members weren’t involved in the process.

YC believes that agents will replace SaaS, just as SaaS replaced on-premise software. They see demand for agents following a similar pattern—where individual use for general purposes, like chat, will drive more businesses to adopt them too.

With any innovation in its early stages, it can often take time for users to experience practical, everyday benefits. In contrast, applications like Ninja’s Deep Research are already proving they can deliver immediate value across a wide range of needs.

What Is Deep Research and How Does It Work?

Generally, Deep Research creates and executes a plan to search, analyze, and synthesize hundreds of online sources to answer a question. What would take a human hours can instead be completed in tens of minutes.

There’s only a handful of options on the market from Ninja, Open AI, Google, Perplexity, Anthropic, and Monica. Each varies when it comes to usage, reliability, and cost. Some cap queries at different amounts, range between $20-$200/mo, or take 30 minutes or more to return an answer. Others can miss information easily found online, have high hallucination rates, and be inconsistent with citations—or do not provide them at all.

How Ninja is addressing limitations in the market is by consistently improving its speed, performance, and affordability. Powered by custom, next-generation chips, our Deep Research can handle unlimited queries, delivers instant, expert-level results, and offers a flat-rate membership at $15/mo. It also sets a new standard in the industry with its superior capabilities:

• Refine or Choose from Curated Prompts: Deep Research helps you craft optimal prompts for the best results. The platform includes a prompt improver that automatically enhances your queries by adding key details and expanding context. Additionally, it offers a curated selection of prompts, tailored to fields such as academia, development, finance, marketing, PR/communications, and personal topics.

• Analyzes Multiple Web and File Sources: Once you’ve set up your prompt, Deep Research creates a multi-step plan and dives into hundreds of sources across the internet and files, including video and audio content (e.g., YouTube or podcasts). It can even convert results into custom reports, summaries, itineraries, and more—all in your preferred format.

• Validate Insights with Confidence: Deep Research makes it easy to verify information with citations and direct links to sources. The platform also shows its reasoning for every step of its plan, whereas a new study found that other models fail to do so 80% of the time.

  

• Proven to Surpass Leading AI Models: For every request, Deep Research generates code to precisely locate information,significantly improving accuracy and reducing hallucinations. In fact, it outperformed leading models in the field at minimizing hallucinations, achieving 91.2% accuracy on the SimpleQA test—one of the best proxies for assessing hallucination levels.

How Millions of Users Are Leveraging Ninja’s Deep Research

Curious about how millions of people are leveraging our Deep Research? Here are just some of the many examples of its diverse use cases:

• Technical: Analyzes system specifications, external documentation, and industry best practices to recommend solutions for software integration, API development, and security protocol enhancements.

  Try it in Ninja: Evaluate the system specifications and external documentation for [insert software/product], and recommend best practices for integrating [insert software/technology].

• Marketing: Assesses market trends, competitor strategies, key search terms, and content gaps to inform and optimize campaigns.

  Try it in Ninja: Analyze market trends for [insert product/industry], assess competitor strategies, and identify key search terms. Highlight content gaps and suggest improvements to optimize a campaign targeting [insert target audience].

• Product: Synthesizes user interviews, surveys, focus groups, and analytics to create reports on customer demographics, behaviors, motivations, and challenges.

  Try it in Ninja: Combine insights from user interviews, surveys, and focus groups to create a report on customer [insert characteristics]. Suggest product improvements for [insert product] based on feedback.

• Sales: Identifies high-potential leads, prospects’ pain points and needs, and competitor solutions and pricing to improve outreach and close more deals.

  Try it in Ninja: Analyze the [insert industry/sector], identifying high-potential leads and key company pain points and needs. Assess competitor solutions and pricing, then recommend strategies for initiating outreach to engage prospects for [insert product/service].

• Skill Advancement: Improves in specific programming languages, frameworks, or technologies by generating in-depth tutorials, coding exercises, and summaries.

  Try it in Ninja: Create a detailed learning path for advancing in [insert programming language/framework/technology]. Provide clear tutorials, practical coding exercises, and concise summaries to reinforce key concepts and skills.

• Product Comparison: Compares features, specifications, performance, and pricing to receive hyper-personalized recommendations.

  Try it in Ninja: Compare the features, specifications, performance, and pricing of [insert product 1] and [insert product 2]. Provide a recommendation based on [insert specific needs or criteria], such as [insert feature preference or use case].

• Traveling: Generate itineraries for destinations, accommodations, attractions, activities, or visa requirements based on timeframe, budget, and preferences.

  Try it in Ninja: Suggest the best travel dates for a trip to [insert destination] in [insert month], along with four-star accommodation options within a budget range of [insert price range]. Identify visa requirements for [insert nationality] and provide a list of recommended attractions and activities.

  The Future of AI Agents: How Ninja’s Paving the Way

So far, there have already been many developments supporting the idea that 2025 will be the year of AI agents.

This month, Google announced the Agent2Agent (A2A) protocol, a new system that allows agents to communicate with each other, securely exchange information, and coordinate actions across enterprise platforms and services. The initiative is backed by over 50 major tech companies, including Salesforce, PayPal, Workday, and Cohere.

How could this benefit Deep Research use cases? A developer could leverage it to build and optimize applications using collaborative AI agents. One agent could serve as a coding assistant, analyzing and pulling relevant code patterns, while another could access Salesforce’s APIs to automatically sync customer data, update records, or trigger workflows. A third agent could review the implementation, debug the code for errors, and suggest specific fixes.

As for whether or not agents will replace SaaS, that question remains. What we do know is that they’re already transforming how we access, learn about, and share information. According to McKinsey, agents can produce high-quality content that reduces review cycle times by 20 to 60%. And unlike previous waves of innovation, you no longer have to wait or pay high prices to take advantage of the tools that power them through Ninja.

Beyond Ninja providing access to the world’s best models, we’ve just released APIs for Deep Research and our other SuperAgents. They offer a flexible and cost-effective way to join us in building powerful products with the best performance at the lowest cost. Join Ninja today and experience how it can transform your day-to-day work and applications.