What’s the difference? DeepSeek vs. ChatGPT vs. Agolo

Artificial intelligence has revolutionized how businesses interact with information and customers. Whether you're looking for a chatbot, content generation tool, or an AI-powered research assistant, choosing the right model can significantly impact efficiency and accuracy.

Today, we compare three advanced AI tools: ChatGPT, DeepSeek, and Agolo. Each offers unique capabilities for businesses and developers. We’ll explore their strengths, weaknesses, and ideal use cases to help you determine which AI best suits your needs.

Overview of DeepSeek, ChatGPT, and Agolo

ChatGPT

Developed by OpenAI, ChatGPT is one of the most well-known conversational AI models. It is widely used for general knowledge, basic customer service, content creation, brainstorming, and general-purpose chat applications. For many, it replaces Google as the first place to research a broad range of questions. ChatGPT is one of the most versatile AI models, with regular updates and fine-tuning.

DeepSeek

DeepSeek has recently gained popularity. It provides an AI model designed for complex reasoning and problem-solving. It excels in mathematics, programming, and scientific reasoning, making it a powerful tool for technical professionals, students, and researchers.

Agolo

Agolo is a domain-specific AI platform that answers complex product questions more accurately and efficiently. It does so with a GraphRAG (Retrieval-Augmented Generation) and an LLM that processes unstructured data from multiple sources, including private sources inaccessible to ChatGPT or DeepSeek. This makes it an ideal solution for product and technical support, offering businesses a way to extract, summarize, and deliver relevant insights from their internal documentation.

The Role of Chain of Thought Reasoning

What is Chain of Thought (CoT) Reasoning?

Chain of Thought (CoT) reasoning is an AI technique where models break down problems into step-by-step logical sequences to improve accuracy and transparency. Instead of jumping to conclusions, CoT models show their work, much like humans do when solving a problem.

Without CoT, AI jumps to quick-fix solutions without understanding the context. With CoT, AI follows logical steps, retrieving information, considering possibilities, and providing a well-reasoned answer.

For technical and product support, structured reasoning—like Agolo’s GraphRAG pipeline—ensures that AI thinks like a human expert rather than regurgitating generic advice. This is analogous to a technical support representative, who “thinks out loud” when diagnosing a problem with a customer, enabling the customer to validate and correct the problem.

To better illustrate how Chain of Thought (CoT) impacts AI reasoning, let’s compare responses from a non-CoT model (ChatGPT without prompting for step-by-step reasoning) to those from a CoT-based model (DeepSeek for logical reasoning or Agolo’s multi-step retrieval approach).

Scenario: Troubleshooting a Product Issue

Imagine a customer is experiencing issues with a software product that frequently crashes when loading large files. They ask an AI-powered assistant for help.

Non-CoT Response (Basic AI Reasoning - ChatGPT without CoT prompting)

"If your software crashes when loading large files, try restarting your device, updating the software, or reinstalling the application. If the issue persists, contact support."

Problems with this response:

• It jumps to a conclusion without diagnosing the issue.
• The advice is generic and lacks deeper reasoning.
• It doesn’t consider previous troubleshooting steps or product-specific conditions.

CoT Response (Step-by-Step AI Reasoning - Agolo’s GraphRAG Pipeline or DeepSeek for Logical Breakdown)

1. Identify the problem context:
   
   • "The software crashes when loading large files, indicating a potential memory overflow, file format incompatibility, or software bug."
2. Retrieve relevant documentation:
   
   • (Agolo) The AI scans internal knowledge bases for similar product issues and known resolutions.
3. Break down possible causes:
   
   • "Common reasons for crashes include:
     a) The system lacks enough RAM to process large files.
     b) The file format is incompatible with the software version.
     c) There is a known bug in the current software release."
4. Provide step-by-step resolution:
   
   • "To resolve this issue, try the following steps:
     a) Check your available system memory and close unnecessary applications.
     b) Convert the file to a recommended format (e.g., .csv instead of .xlsx).
     c) Update the software to the latest version and check the patch notes for bug fixes."
5. Contextual follow-up:
   
   • "If the issue persists, please provide error logs or check for updates in our knowledge base."

Why this response is better:

• Structured reasoning – It identifies root causes before providing solutions.
• Uses past knowledge – Pulls from internal documentation for accuracy.
• Avoids generic troubleshooting steps – Instead, it offers relevant and technical resolutions.
• Mimics human problem-solving – Just like an expert support agent would.
• Built-in validation - Enables the customer to validate or correct the diagnosis.

How Do These AI Models Use Chain of Thought?

DeepSeek’s Chain of Thought

DeepSeek naturally follows step-by-step problem-solving methods, making it highly effective in mathematical reasoning, structured logic, and technical domains. When given a math problem, DeepSeek will explain each calculation, leading to the final result.

ChatGPT’s Chain of Thought

While ChatGPT does not inherently break problems into structured steps, users can explicitly prompt it to follow CoT reasoning. For example, by asking, "Explain your reasoning step by step," ChatGPT will attempt a CoT-like breakdown. However, it is not as rigidly structured as DeepSeek.

Agolo’s Chain of Thought: The GraphRAG Pipeline

Agolo’s GraphRAG-powered approach follows a multi-step reasoning pipeline, making a strong case for chain-of-thought reasoning in a business and technical support context. Here’s how it works:

1. Extracts key entities (e.g., products).
2. Identifies relevant support issues and solutions (e.g., situations).
3. Retrieves supporting documentation from internal knowledge bases (i.e. GraphRAG)
4. Synthesizes a response using the LLM, ensuring accuracy based on company-specific data.

This structured, multi-step reasoning ensures that Agolo doesn’t just generate answers—it builds them logically, making it a trustworthy AI for technical and product support.

Comparing Key Features

Final Thoughts

The right AI model depends on your business goals. If you need technical problem-solving, DeepSeek is a solid choice. ChatGPT remains one of the best options for broad customer engagement and AI-driven content. However, if your organization deals with complex internal documentation and technical support, Agolo provides a tailored AI-powered knowledge retrieval system with chain-of-thought reasoning.