Answers by Context.dev

Product Introduction

1. Definition: Answers by Context.dev is a specialized API endpoint (POST /web/answers) that functions as an autonomous web research agent. It belongs to the technical category of AI-powered data extraction and enrichment APIs, designed to automate the process of sourcing, synthesizing, and structuring information from the live web.
2. Core Value Proposition: It exists to eliminate the complex, multi-step workflow of manually stitching together search engine APIs, web scrapers, and large language model (LLM) calls. Its primary value is turning a single, natural language research task into a structured JSON payload with verified source URLs, enabling developers to build data pipelines and applications that require real-time, sourced information from the internet.

Main Features

1. Structured JSON Output: The API's core functionality is defined by the json_format parameter. This is not a strict JSON Schema but an example object that dictates the exact structure and data types of the returned answer in json_content. This allows developers to pre-define complex nested objects and arrays (up to 8 levels, 500 values) to receive perfectly formatted data ready for their databases or applications.
2. Dual-Mode Research Engine: The system operates on two distinct credit tiers with different underlying models and research budgets. The fast mode (10 credits) uses a smaller model with a 30-second research budget for quick, cost-effective queries. The ultra mode (100 credits, default) employs deeper reasoning models with a 50-second budget for complex, multi-faceted research tasks requiring higher accuracy and evidence gathering.
3. Source-Attributed Results: Every successful API response includes a sources array, which lists the URLs that contributed search results or readable page content, presented in the order they were discovered. This provides essential provenance and allows for manual verification of the AI's findings, a critical feature for auditability and trust in automated research.
4. Configurable Timeout & Partial Results: The timeoutOpts object allows precise control over request deadlines (1-300 seconds). Crucially, setting behavior: "return-partial" instructs the agent to return any usable evidence collected before the deadline, marked with partial: true. This ensures billable credits are only consumed when a usable, even if incomplete, answer is provided, optimizing for reliability in production environments.
5. Zero Data Retention (ZDR): An enterprise-grade privacy feature. When enabled for an organization and set to zdr: "enabled", the request bypasses shared caches and its content is omitted from retained usage logs. Successful ZDR responses are confirmed with an X-Context-ZDR: true header, making it suitable for handling sensitive or proprietary research queries.

Problems Solved

1. Pain Point: The manual and fragile process of building web research workflows. Developers and data engineers typically need to integrate a search API (e.g., Google Programmable Search), a robust scraping service to bypass blocks and render JavaScript, and then an LLM API to parse and structure the scraped text. This chain is prone to breakage, requires constant maintenance, and lacks built-in source attribution.
2. Target Audience: The primary users are Software Engineers and Data Scientists building enrichment pipelines, competitive intelligence dashboards, market research tools, and AI agents that require real-time web data. Secondary users include Product Managers and Business Analysts who need to automate the collection of structured data on competitors, pricing, or market trends without writing complex code.
3. Use Cases:
   • Competitive Intelligence Pipeline: Automatically track and compare competitor pricing, feature lists, and launch announcements by defining a structured json_format and scheduling daily API calls.
   • Lead Enrichment Agent: Enhance CRM records by researching a company's funding, tech stack, or recent news based on their domain name, returning structured data for sales teams.
   • Fact-Checking & Research Assistant: Power a chatbot or internal tool that answers specific, sourced questions about current events, technical documentation, or market data, returning answers with citations.

Unique Advantages

1. Differentiation: Unlike general-purpose LLMs that lack live web access or simple scraping APIs that only return raw HTML, Answers by Context.dev combines autonomous research (finding sources), intelligent extraction (reading and comprehending pages), and structured output generation in a single, managed API call. It differs from other "AI search" tools by providing developer-first control over the output schema and guaranteed source URLs.
2. Key Innovation: The integration of a directed research agent with a schema-on-demand output system. The agent doesn't just fetch a single page; it performs a task-oriented search, evaluates and reads multiple sources, and reasons over the collected evidence to populate a user-defined JSON structure. The timeoutOpts and partial return mechanism represent a sophisticated approach to managing the unpredictability of web research in a production API context.

Frequently Asked Questions (FAQ)

1. How does Answers by Context.dev handle websites that block bots or require JavaScript? The service utilizes a robust infrastructure capable of rendering JavaScript and bypassing common anti-bot measures to access readable page content, similar to its core web scraping products. Unreadable pages are excluded from the final sources list.
2. What is the difference between the fast and ultra research modes? The fast mode (10 credits) uses a smaller, faster AI model with a shorter research budget (30s), ideal for simple fact-checking. The ultra mode (100 credits, default) uses more advanced models with a longer budget (50s) for complex tasks requiring synthesis of multiple sources and deeper reasoning. The API defaults to ultra to ensure high-quality results.
3. Can I map specific data points in the JSON answer back to their source URL? No, the sources array provides a list of all contributing URLs in the order they were accessed, but it does not provide field-level citation mapping. The system synthesizes information from all sources to generate the final structured answer.
4. What happens if the research cannot find an answer or times out? If no usable evidence is found, the API returns a 502 error and no credits are consumed. If a timeout occurs and timeoutOpts.behavior is set to "fail", it returns a 408 error (no charge). If set to "return-partial", it returns the best answer compiled from evidence gathered before the deadline, marked with "partial": true, and credits are consumed.
5. Is the json_format parameter a strict JSON Schema validator? No, it is an example object. The API uses it to infer the desired keys and the structure (objects, arrays, primitive types), but it does not enforce strict validation like "required" fields or pattern matching. Unknown values may be returned as null.