The age of simple search is behind us. For modern teams, research means more than asking questions and reading answers. It means conducting deep, ongoing investigations across multiple sources, tools, and domains. That’s why today’s most valuable AI research tools do more than summarize, they orchestrate, synthesize, and integrate.
In this space, Perplexity AI has gained popularity as a fast and factual AI answer engine. Dot, on the other hand, positions itself as a fully customizable research framework for teams. Both tools address key research needs, but take very different approaches.
In this post, we explore how they compare, especially when it comes to their “deep research” capabilities, and what to consider when choosing between individual assistants and team-ready AI research tools.
What Perplexity AI Offers
As one of the most popular AI research tools, Perplexity AI is often described as an AI-powered search engine. At its core, it delivers answers sourced from live web content, paired with citations. It is designed for individual users who want quick summaries, factual overviews, and verified information.
Where it excels:
- Fast, citation-backed answers
- User-friendly interface with no setup required
- Public web access for real-time information
- Search-optimized outputs for general knowledge and trending topics
As one of the fastest-growing AI research tools, Perplexity gives users a sense of reliability through its transparent source linking. It is especially useful for surface-level research and personal knowledge building.
Perplexity’s Deep Research Feature
Perplexity’s new Deep Research feature is its first major step beyond quick answers. Instead of responding to one question with a static answer, it:
- Performs iterative searches, adjusting queries to find the most relevant data
- Reads and analyzes multiple sources before answering
- Summarizes results into a comprehensive, longer-form report
- Provides source citations and structured output
Available in the Pro plan, Deep Research is designed to mimic how a human researcher might follow a thread across the web. It's a significant improvement but still web-only, non-customizable, and built solely for one-off tasks. There’s no long-term memory, no API access, no workflow integration.
For individuals or students, it’s one of the most accessible AI research tools. For teams and enterprises, it’s a limited starting point.
Dot’s Deep Research Agent: Designed for Systems, Not Just Sessions
While Perplexity’s Deep Research improves personal insights, Dot’s Deep Research Agent is built for operational depth. It is not a feature, it’s a workflow that integrates into your systems.
Powered by Gemini 1.5 Pro, Dot’s Deep Research Agent:
- Breaks research into multi-step tasks handled by separate agents
- Combines external and internal data (documents, tools, APIs, storage)
- Delivers structured outputs to Notion, Slack, Google Docs, or dashboards
- Maintains session memory for continuous research threads
- Provides full citation and traceability, including internal sources
- Can be deployed on-premise for secure environments
This makes Dot one of the few AI research tools that operates like an internal team that learns, evolves, and delivers insights that adapt to your business context.
You can create a Deep Research Agent using CrewAI and Deepseek but that is not as easy as creating an agent at Dot. In addition to that Dot already offers a Deep Research Agent that you can build an AI workflow around.
If you want to learn more about Dot and CrewAI comparison as multi agent AI systems, you can check our Dot vs. CrewAI: Multi Agent AI Systems for Business blog post.
Feature-by-Feature: Dot vs. Perplexity AI
Use Case Comparison: Researching a Competitive Landscape
Let’s say your team needs to gather intelligence on five competitors for a strategy brief.
Using Perplexity AI Deep Research:
You can run a Deep Research query on each competitor. You’ll get summaries with sources, quickly. But you’ll need to copy, organize, and combine them manually and there’s no way to pull in your own historical notes or compare results across queries.
Using Dot’s Deep Research Agent:
You assign an agent to:
- Gather public data from the web
- Pull past competitor notes from Notion and Drive
- Summarize market changes over the last 6 months
- Create a side-by-side competitor table
- Export it to your team’s workspace and alert the product lead
It’s not just a search. It’s a self-contained AI research tool workflow that integrates with your work.
Team Needs Are Different from Individual Needs
Most AI research tools are still built with individuals in mind. They answer questions well but fall short when research becomes a process that is shared, recurring, and embedded in team outputs.
Dot shifts the mindset:
- From answer generation to workflow orchestration
- From isolated users to collaborative research systems
- From web scraping to data-rich integration
For teams working on go-to-market research, product strategy, content planning, or knowledge management, Dot turns AI from a helper into infrastructure.
The Open-Source Advantage
Dot also supports open-source models like Mistral and DeepSeek, which makes it one of the more flexible AI research tools available today.
This means your team can:
- Run research workflows with different models depending on the task
- Keep data private and compliant by avoiding vendor lock-in
- Experiment and evolve your stack as the ecosystem changes
Most tools, including Perplexity, only offer fixed models. With Dot, you can choose and switch without breaking workflows.
Final Thoughts: Choosing the Right AI Research Tool
Both Perplexity and Dot offer value but for very different audiences.
- Choose Perplexity AI if you need fast, factual, citation-backed insights for individual use.
- Choose Dot if you want scalable, secure, customizable AI research tools that operate across your data, systems, and teams.
As AI becomes part of how companies think, the tools we choose will either support deeper insight or slow us down with surface-level answers.
Dot is built for the next generation of AI-enabled teams.
Frequently Asked Questions
What are AI research tools used for?
AI research tools help users gather, analyze, and summarize information from multiple sources to support faster, smarter decision-making.
Is Dot better than Perplexity AI for deep research?
Yes, Dot offers deeper workflows, model flexibility, and team collaboration, while Perplexity focuses on fast individual search results.
Can Dot be used with internal company data?
Yes, Dot integrates with internal tools, files, and APIs, making it ideal for teams needing private and secure research infrastructure.
