Monitor input URLs, extract semi-structured content from Markdown and HTML, identify trends by location and category, deliver structured JSON, notify via webhook, and persist data to disk.
The AI agent ingests a data source URL and uses Bright Data's Web Unlocker to fetch content from target sites. It parses the retrieved content into clean plaintext and applies Google Gemini to identify trends by location and category. It outputs structured JSON, notifies external systems via webhook, and saves the final data to disk for auditing.
Automates the end-to-end extraction, analysis, and delivery of structured insights from semi-structured web content.
Ingests URLs and fetches content using Bright Data Web Unlocker
Parses content into clean plaintext
Analyzes data with Google Gemini to identify trends by location and category
Extracts key topics and themes
Formats results as structured JSON
Notifies external systems via webhook and stores outputs on disk
Before the AI agent, teams manually extracted data, resulting in slow processing, inconsistent formats, and missed insights. After adopting the AI agent, extraction is automated and standardized, trends are detected in real time, and outputs are auditable and easily stored.
Three-step AI agent flow that non-technical users can follow.
Accepts a data source URL, fetches content with Bright Data's Web Unlocker, and passes raw text to the parser.
Parses content into plaintext, then Google Gemini analyzes trends by location and category and extracts topics.
Formats results as structured JSON, triggers webhook notifications to external systems, and saves outputs to disk.
One realistic scenario.
Scenario: A market research team needs to monitor 60 industry blog posts across the US and Europe. The AI agent fetches content from each URL, extracts topics and trends, and returns a single structured JSON payload with location-based insights. It then posts a Slack webhook with a concise summary and stores the results on local storage for audit.
Six roles that gain tangible workflow improvements.
Need to scale the extraction of insights from large sets of Markdown/HTML.
Require location- and category-based trend data to optimize content.
Need structured data inputs to train or evaluate models.
Must organize and mine large content libraries.
Track topic-level trends to inform campaigns.
Automate end-to-end data workflows without manual scraping.
One supporting sentence with short explanation.
Fetches content from target sites using authenticated requests.
Analyzes content to identify trends and topics and formats the response as JSON.
Receives structured JSON payloads in real time and routes them to downstream systems.
Saves final structured data for audits and future processing.
Six practical scenarios where the AI agent excels.
Common questions and detailed answers.
Yes. The AI agent is designed to scale by queuing and parallel processing of URLs. It leverages Bright Data's infrastructure to fetch content reliably while managing rate limits. The parsing and analysis steps operate on batches, producing a single, coherent output. You can configure concurrency and batching to balance speed and cost.
The AI agent outputs structured JSON that includes topics, trend scores, and locations. The JSON schema can be adjusted to align with your database or dashboard. Outputs can be persisted to disk and sent via webhook to downstream systems. Additional formats can be produced on request.
Webhooks are triggered immediately after the JSON payload is generated. They carry structured data suitable for dashboards, alerts, or automation workflows. You can configure retry behavior and destinations to ensure reliable delivery. For high-volume needs, batching options are available.
Bright Data requires an authentication token included in request headers. The AI agent manages token usage and rotates credentials as needed. Access is scoped to your Web Unlocker zone with defined permissions. Credentials are stored securely and not exposed in outputs.
Yes. All mined data stored on disk can be encrypted at rest and access-controlled. Retention policies are configurable, allowing you to keep data for audits or delete after a defined period. The agent logs operations in an immutable fashion for traceability. You can export data before deletion if required.
Yes. Structured outputs can be streamed to multiple endpoints such as Slack, Zapier, Make, or custom dashboards. Each destination can receive the same payload or a tailored subset. Notifications can be batched or sent in real time depending on requirements. You can add or remove destinations without changing the core workflow.
Yes. Gemini prompts can be tailored to focus on specific categories or regions, and the output schema can be adjusted to match your database. The AI agent supports schema mapping and field naming conventions to align with your data model. Changes can be deployed without affecting ongoing extractions. You can also specify limits on topic granularity and trend scoring.
Monitor input URLs, extract semi-structured content from Markdown and HTML, identify trends by location and category, deliver structured JSON, notify via webhook, and persist data to disk.
