Answers by Context.dev

Give it a research task + the JSON shape you want back.

Developer ToolsArtificial IntelligenceAPI

▲ 134 votes2 commentsLaunched Sep 20, 2026

Daily #3Weekly #47

Answers by Context.dev turns web research into a single API call. Define a task and your desired JSON structure. Answers finds sources, researches the web, and returns structured data with source URLs. Build enrichment pipelines, comparison tools, and research agents without stitching together search, scraping, and LLM calls.

AI Analysis

📝 Summary

Answers by Context.dev is an API tool that simplifies web research into one call. Users provide a research task and desired JSON schema; it handles searching, scraping, reasoning, and returns structured data with source URLs. Core features include custom output shapes, automatic source finding, and integrated LLM processing. USP: Eliminates complex stitching of search, scraping, and LLM APIs. It solves key pain points for developers like time-consuming pipeline building for data enrichment, competitive analysis, and research agents. Value proposition: Enables fast creation of reliable AI-powered research tools and agents.

📈 Market Timing

In 2025-2026, with exploding adoption of AI agents, autonomous research tools, and LLM-powered applications, demand for simplified web data extraction is surging. Technology for reliable scraping and structured LLM outputs is maturing, while user needs shift toward no-code/low-code research pipelines amid growing data privacy and efficiency demands. Economic push for AI productivity tools supports this. It is an Excellent Timing as the market moves from fragmented solutions to integrated APIs.

✅ Feasibility

Technically feasible using existing search engines, LLMs, and scraping frameworks, though web scraping involves compliance risks (legal, robots.txt, IP blocking). Development costs are moderate for an experienced AI team; operational costs depend on API usage and LLM calls. Strong scalability via cloud infrastructure. Overall High feasibility with manageable risks if compliance is prioritized. Key reasons: leverages mature AI tech, focused scope reduces complexity.

🎯 Target Market

Main targets: Software developers, AI/ML engineers, indie hackers, and product teams building AI agents or automation tools. Industries: Developer tools, SaaS, data analytics, e-commerce (for enrichment). Primarily US and Europe-based tech professionals. TAM: Part of the $20B+ AI infrastructure/API market; SAM ~$2B for research/search APIs; SOM ~$100M for structured research tools. Core pains: Manual integration of multiple services for web research. High willingness to pay for reliable, time-saving API (likely usage-based pricing).

⚔️ Competition

Medium. Direct competitors: 1. Tavily (tavily.com) - AI search API for LLMs. 2. Exa (exa.ai) - Semantic web search and extraction. 3. Perplexity API (perplexity.ai) - Research-focused AI answers. 4. Serper (serper.dev) - Google SERP API with parsing. 5. Firecrawl (mendable.ai/firecrawl) - Web scraping to LLM-ready data. Advantages: Precise JSON schema output and end-to-end research in one call, strong for structured pipelines. Disadvantages: Newer player, potentially higher costs or less battle-tested than established search APIs; limited brand recognition compared to larger competitors.

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