Build a Pay-As-You-Go Deep Research Assistant Using OpenAI API for Under $5 per Report

Scale AI has received a significant investment from Meta, valuing the startup at $29 billion. The investment comes as Meta aims to enhance its AI capabilities, particularly in the development of large language models (LLMs) and generative AI. Scale AI, known for producing and labeling data essential for training these AI models, has seen increasing demand for its services as companies like OpenAI, Google, and Anthropic accelerate their AI initiatives. Key Details of the Investment Amount Invested: Approximately $14.3 billion for a 49% stake in Scale AI. Valuation: Scale AI is now valued at $29 billion. Leadership Change: Alexandr Wang, Scale AI’s co-founder and CEO, is stepping down to join Meta, focusing on superintelligence efforts. Interim CEO: Jason Droege, Scale’s Chief Strategy Officer, will take over as interim CEO. Continued Independence: Despite the investment, Scale AI will remain an independent entity, with Wang serving as a director on its board. Use of Funds: The investment will be used to pay shareholders and support future growth, including hiring more highly skilled professionals. Strategic Impact for Meta This investment underscores Meta’s strategic push to bolster its AI development. The social media giant has been lagging behind competitors in the AI race, and acquiring a substantial stake in Scale AI provides access to high-quality, specialized data crucial for training advanced AI models. Last year, Meta lost 4.3% of its top talent to other AI labs, further intensifying the pressure to strengthen its AI team. Industry Insights Industry insiders view this move as a critical step for Meta to stay competitive in the rapidly evolving AI landscape. The reliance on data labeling and high-quality training data is becoming increasingly important, and Scale AI’s expertise in these areas will be invaluable. Company Profile: - Scale AI: Founded by Alexandr Wang, the company specializes in providing data labeling services that are essential for training AI models. It has previously raised $1 billion from investors including Amazon and Meta, reaching a valuation of $13.8 billion last year. - Meta: Formerly Facebook, Meta is a leading technology company focusing on social media and emerging technologies. It is aggressively investing in AI to keep pace with advancements by other tech giants and startups. Practical Guide to Deep Research Using OpenAI’s API For those seeking to perform deep research without committing to expensive subscription plans, OpenAI’s API offers a flexible and cost-effective alternative. As of July 2025, the API allows access to deep research models like "o3-deep-research" and "o4-mini-deep-research." Basic Workflow Single Call to Responses API: Use the API to run a deep research query. Configure Web Search: Ensure the model is set up to perform web searches. Use Sandboxed Code Interpreter: Optionally, include a code interpreter to handle computational tasks. Iterative Improvement Loop: Implement a hybrid workflow where the initial research is critiqued, and necessary adjustments are made. Example Code ```python import openai Define the system message and user query system_message = """ You are a professional researcher preparing a structured, data-driven report on behalf of a global health economics team. Your task is to analyze the health question the user poses. Be analytical, avoid generalities, and ensure that each section supports data-backed reasoning that could inform healthcare policy or financial modeling. """ user_query = "Research impact of semaglutide on global healthcare systems." Create the response response = openai.Responses.create( model="o3-deep-research", input=[ { "role": "developer", "content": [ { "type": "input_text", "text": system_message, } ] }, { "role": "user", "content": [ { "type": "input_text", "text": user_query, } ] }, ], reasoning={ "summary": "auto" }, tools=[ { "type": "web_search_preview" }, { "type": "code_interpreter", "container": { "type": "auto", "file_ids": [] } } ] ) ``` Handling Model Limitations Web Search Requirement: The deep research model requires a web search to function. Limited Tooling: Only basic tools like web search and a code interpreter are supported. More complex functionalities must be handled externally via additional scripts. Architecture Research Agent: Runs the deep research model and generates an initial report. Critique Agent: Evaluates the initial report and may request additional research runs if necessary. Report Agent: Consolidates the research and critique into a final document. Each agent operates in a structured pipeline, with transitions enabling fine-tuned critiques and reporting without re-running queries. Cost Estimation To estimate usage costs, the Report Agent can: 1. Extract model names and token counts from the reasoning summary. 2. Fetch pricing data from OpenAI’s website. 3. Calculate the total cost using a code interpreter. However, this method is approximate and should not be used in production due to potential inaccuracies and price volatility. Configuration and Failure Modes Custom Toolkit: Add tools specific to your use case, such as URL verification functions. Prompt Modifications: Adjust prompt instructions to ensure the model uses the available tools correctly. Handle Errors: Implement error handling to manage API failures and ensure robust operation. Implementation Highlights python def create_agents(): return [ Agent( name="ResearchAgent", instructions=research_instructions, model="o4-mini-deep-research", tools=[WebSearchTool(), CodeInterpreterTool()], handoffs=[] ), Agent( name="CritiqueAgent", instructions=critique_instructions, model="o3-pro", tools=[WebSearchTool(), verify_url], handoffs=[{"name": "ResearchAgent", "type": "programmatic"}] ), Agent( name="FinalReportAgent", instructions=final_report_instructions, model="o4-mini", tools=[WebSearchTool(), CodeInterpreterTool()] ) ] Use Examples Full App Run: Set up the environment and run the app with the desired query. The app will cycle through web searches and reasoning summaries, taking 2-10 minutes to complete. Intermediate results are saved and can be reviewed or reused. Critique Phase: The Critique Agent evaluates the research, ensuring factual accuracy and completeness. Tools like URL verification and MCP servers are used to validate sources and gather additional information. Final Report: The Report Agent compiles the final output, including a cost analysis. Example cost breakdown for a deep research query: o4-mini-deep-research: $1.1853 o4-mini: $0.3679 Final Report (o4-mini): $0.0462 Grand Total: $1.5994 This setup allows for efficient and accurate deep research while maintaining cost control, making it an attractive solution for those needing advanced AI research capabilities without the constraints of fixed subscription plans. Conclusion The integration of Meta’s investment with Scale AI’s data-labeling expertise is a strategic move to enhance Meta’s AI capabilities. Meanwhile, leveraging OpenAI’s API for deep research provides a flexible and cost-effective alternative for individuals and organizations, demonstrating the growing importance of customizable AI solutions in the tech industry.