Tool category
AI Research Tools
Search and synthesis tools for cited decisions.
Use this category when the job is moving from open questions to source-backed notes, comparison tables, and decision memos.
Recommended tools
PerplexityAnswer engine for cited market, product, and tool research.TavilySearch API designed for AI agents and research workflows.ExaAI search API for grounded agents and research workflows.ClaudeLong-context assistant for writing, analysis, and coding workflows.GeminiGoogle model family for multimodal and workspace-aware AI.LlamaIndexData and RAG framework for knowledge-heavy AI applications.PineconeManaged vector database for RAG, semantic search, and AI assistants.WeaviateAI-native vector database with free cloud and deployment flexibility.CohereEnterprise AI platform for Command, Embed, Rerank, and RAG systems.FirecrawlWeb data API for search, scraping, crawling, and agent context.ApifyActor platform for web scraping, automation, and AI agent data.BrowserbaseBrowser infrastructure for web agents, automation, and data workflows.NotebookLMGoogle AI research and learning assistant grounded in user-provided sources.QdrantVector database and search engine for RAG, recommendation, and AI agents.
Related workflows
Research assistant workflowTurn open web research into source-backed notes, comparison tables, and a decision-ready recommendation.AI-assisted lead enrichment workflowCombine search, enrichment prompts, and automation glue to turn raw leads into reviewed outreach-ready records.Meeting to decision memo workflowTurn meeting audio or transcripts into source-aware decisions, owners, open questions, and a reusable operating memo.AI tool evaluation scorecard workflowEvaluate candidate AI tools with real tasks, source-backed facts, cost assumptions, risk notes, and a decision-ready scorecard.Customer feedback synthesis workflowConvert calls, tickets, surveys, and notes into source-backed product themes without flattening customer nuance into generic summaries.RAG knowledge base evaluation workflowEvaluate a RAG knowledge base by testing ingestion quality, source retrieval, answer faithfulness, and update ownership before scaling infrastructure.
Related guides
The Qidao AI tool selection frameworkA practical seven-part framework for choosing AI tools by task fit, workflow fit, quality, cost, privacy, replaceability, and automation readiness.How to write source-backed AI research memosA method for turning AI-assisted web research into cited claims, comparison tables, confidence notes, and decision-ready recommendations.How to turn meetings into decision memos with AIA workflow for converting meeting audio or transcripts into decisions, owners, open questions, and source-backed follow-up notes.How to judge whether an AI tool is worth paying forA practical framework covering replacement cost, reliability, privacy, team fit, and switching risk.How to choose AI research tools for source-backed decisionsA selection guide for choosing research assistants, search APIs, scraping tools, and synthesis models without confusing summaries with evidence.How small teams should choose a RAG stackA practical guide to choosing embeddings, vector search, retrieval evaluation, data ingestion, and model APIs for small-team RAG systems.