Deep Research and Deep Thinking
Give your chatbot the ability to conduct multi-source research and show visible step-by-step reasoning.
Chipp apps support two advanced reasoning capabilities that go beyond standard chat responses: Deep Research for comprehensive multi-source investigation, and Deep Thinking for visible step-by-step reasoning. Both give your consumers more thorough, transparent answers to complex questions.
Deep Research and Deep Thinking require a Builder plan or higher. They are not available on the Free tier.
Deep Research
Deep Research spawns multiple parallel research agents that independently search the web, read pages, and compile findings. The results are synthesized into a single, comprehensive report with citations.
When the AI Uses Deep Research
The AI automatically decides to use Deep Research for questions that require broad investigation:
- Market research and competitive analysis
- Technical deep dives requiring multiple authoritative sources
- “Compare X vs Y vs Z” questions with multiple dimensions
- Industry reports and comprehensive analyses
- Multi-faceted questions where a single web search would be insufficient
It does not use Deep Research for simple factual questions, casual conversation, follow-up questions where context already exists, or questions about the consumer’s own data.
How It Works
Decomposition
The AI breaks the consumer’s question into 2-6 focused sub-questions. Each targets a distinct aspect of the topic. For example, “Compare React vs Vue vs Svelte for enterprise apps” might become sub-questions about performance, ecosystem, hiring, learning curve, and corporate backing.
Round 1: Parallel Investigation
Each sub-question gets its own research agent that independently searches the web, reads full pages, and follows links to primary sources. These agents run in parallel for speed.
Gap Analysis
After Round 1, an analysis step reviews all findings for gaps, contradictions, and areas needing deeper investigation. If the research is already comprehensive, it moves straight to synthesis.
Round 2: Follow-Up Research (if needed)
Up to 4 follow-up agents investigate gaps identified in the analysis. These agents have context from Round 1, so they can verify claims, cross-reference sources, and fill in missing information.
Synthesis
All findings from both rounds are synthesized into a single, well-structured report organized by theme (not by sub-question). Every claim includes inline citations with links to sources.
What the Consumer Sees
During Deep Research, the chat UI shows real-time progress:
- Plan — The sub-questions being investigated
- Research progress — Which agents are running, how many sources have been found
- Gap analysis — Whether follow-up research is needed
- Final report — A comprehensive response with inline source citations
A typical Deep Research operation takes 30-90 seconds depending on the complexity of the question and the number of follow-up investigations needed.
Example Prompts
- “Research the current state of AI regulation in the EU, US, and China”
- “Compare the top 5 CRM platforms for small businesses under 50 employees”
- “What are the latest developments in solid-state battery technology?”
- “Analyze the competitive landscape for AI code assistants”
Deep Thinking
Deep Thinking enables visible, structured reasoning before the AI answers. Instead of jumping directly to a response, the AI creates a step-by-step plan and works through it, showing its reasoning process to the consumer.
When the AI Uses Deep Thinking
The AI automatically uses Deep Thinking for questions that benefit from structured analysis:
- Comparing two or more options, approaches, or strategies
- Questions with multiple distinct parts that each need analysis
- Providing recommendations based on specific criteria
- Complex analysis requiring structured reasoning
- Cross-referencing multiple knowledge base sources
- Combining web research with knowledge base data
It does not use Deep Thinking for simple factual questions with a single clear answer, casual conversation, brief follow-ups, or yes/no questions.
How It Works
Plan Creation
The AI creates a reasoning plan with 2-8 steps. Each step maps to a specific action: searching knowledge, browsing the web, analyzing data, comparing options, or synthesizing conclusions.
Step Execution
The AI works through each step in order, using the appropriate tools (knowledge search, web browse, code execution, etc.) as needed.
Comprehensive Response
After completing all steps, the AI produces a thorough response informed by the structured reasoning process.
What the Consumer Sees
During Deep Thinking, the chat UI shows the reasoning steps as the AI works through them. Each step has a label (e.g., “Searching knowledge base for pricing data”, “Comparing feature sets”, “Synthesizing recommendation”) and updates in real time as the AI progresses.
This transparency helps consumers understand how the AI arrived at its answer and builds trust in complex recommendations.
Example Prompts
- “Should I use PostgreSQL or MongoDB for my e-commerce app?”
- “Review my business plan and identify the three biggest risks”
- “What’s the best marketing strategy for a B2B SaaS launch?”
- “Analyze our Q3 sales data and recommend focus areas for Q4”
Deep Research vs. Deep Thinking
| Deep Research | Deep Thinking | |
|---|---|---|
| Purpose | Gather information from many external sources | Structure reasoning about available information |
| Speed | 30-90 seconds | 10-30 seconds |
| Sources | Parallel web search agents | Knowledge base, web, code execution |
| Output | Cited research report | Structured analysis with visible reasoning |
| Best for | ”What is the current state of X?" | "What should I do about X?” |
| Agents | 2-6 parallel sub-agents + follow-up agents | Single agent with multi-step plan |
In practice, the AI often combines both: using Deep Research to gather information and Deep Thinking to analyze it. The AI decides which capability to use (or both) based on the nature of the question.
Tier Requirements
Both capabilities require a Builder plan or higher:
| Tier | Deep Thinking | Deep Research |
|---|---|---|
| Free | No | No |
| Builder | Yes | Yes |
| Studio | Yes | Yes |
| Studio Pro | Yes | Yes |
| Enterprise | Yes | Yes |
Billing
Both Deep Research and Deep Thinking consume tokens through your organization’s Stripe Token Billing balance. Deep Research is more expensive because it runs multiple parallel LLM calls (one per sub-question, plus gap analysis and synthesis). A typical Deep Research operation uses 5-15x more tokens than a standard chat response.
Deep Thinking has modest overhead since it uses a single model with structured planning — typically 2-3x a standard response.
If your app primarily serves simple Q&A use cases, consumers will rarely trigger these capabilities. The AI only activates them for questions that genuinely benefit from structured reasoning or multi-source research.