AI Agent

Agente IA de agregación de contenidos

Stop manually collecting content from dozens of scattered sources

15 hours/week gathering content across platforms. Zero hours with AI that monitors, filters, and curates automatically.

Benefits

Never Manually Hunt for Content Again

Content teams waste hours daily scanning RSS feeds, social media, news sites, and industry publications. Critical insights are missed. Manual curation is inconsistent. This agent aggregates everything automatically.

90%

Time saved on curation

99%

Source coverage

3x

More insights captured

Agentic Flows

Automated Content Curation Workflow

Complete automation: source monitoring across platforms, AI-powered relevance filtering, duplicate detection and removal, topic categorization and tagging, content summary generation, distribution to teams.

How it works

Intelligent Content Discovery and Curation

The agent monitors 1,000+ sources including RSS feeds, social media platforms, news outlets, blogs, and publications. It filters by relevance using AI, deduplicates content, and organizes by topic automatically.

Self-Learning Agents

The agent improves with every task, adapting to outcomes, applying feedback, and self-correcting using Constitutional AI.

Upto 98% Accuracy

As a result of constant feedback loops, Beam AI Agents refine their approach with every cycle, leading to 98% accuracy across flows.

Smart Model Switching

We call it ModelMesh. Each agent selects the right model for the task, balancing speed, accuracy, and cost in real time.

Extraiga el número de pedido, la información del vendedor, la información de entrega y los detalles de pago de los formularios de pedido cargados.

Completado

ID-0E48

Extraiga el número de pedido, la información del vendedor, la información de entrega y los detalles de pago de los formularios de pedido cargados.

Completado

ID-0E48

Extraiga el número de pedido, la información del vendedor, la información de entrega y los detalles de pago de los formularios de pedido cargados.

Completado

ID-0E48

Self-Learning Agents

The agent improves with every task, adapting to outcomes, applying feedback, and self-correcting using Constitutional AI.

Upto 98% Accuracy

As a result of constant feedback loops, Beam AI Agents refine their approach with every cycle, leading to 98% accuracy across flows.

Smart Model Switching

We call it ModelMesh. Each agent selects the right model for the task, balancing speed, accuracy, and cost in real time.

Extraiga el número de pedido, la información del vendedor, la información de entrega y los detalles de pago de los formularios de pedido cargados.

Completado

ID-0E48

Extraiga el número de pedido, la información del vendedor, la información de entrega y los detalles de pago de los formularios de pedido cargados.

Completado

ID-0E48

Extraiga el número de pedido, la información del vendedor, la información de entrega y los detalles de pago de los formularios de pedido cargados.

Completado

ID-0E48

Integrations

Monitors All Your Content Sources

Pulls from RSS/Atom feeds, Twitter, LinkedIn, Reddit, YouTube, Medium, Substack, and thousands of news sites. Exports to Slack, email digests, Notion, Airtable, WordPress, and content management systems.

Implementation

Curating Content in 48 Hours

Configure your content sources and define topic filters. Set relevance thresholds and distribution preferences. Most teams have automated content feeds running within 2 days of setup.

Step 1

Agent Discovery

Conduct workshop(s) with relevant stakeholders to identify and prioritize use cases and map the requirements

Step 1

Agent Discovery

Conduct workshop(s) with relevant stakeholders to identify and prioritize use cases and map the requirements

Step 2

Agent Setup

Develop and launch your first agent with basic logic and integration (process scoping and recording, test dataset of 30-50 examples, baseline agent running and testing with target output mapping)

Step 2

Agent Setup

Develop and launch your first agent with basic logic and integration (process scoping and recording, test dataset of 30-50 examples, baseline agent running and testing with target output mapping)

Step 3

Agent Training

Test performance and gather feedback from process users (agent optimization up to 80% against expected output, variable-level accuracy measurement and optimization, integrations setup, and feedback API interfacing)

Step 3

Agent Training

Test performance and gather feedback from process users (agent optimization up to 80% against expected output, variable-level accuracy measurement and optimization, integrations setup, and feedback API interfacing)

Step 4

Agent Live

Extend to more workflows and client teams (live and continuous monitoring, human-in-the loop interfacing, node auto-tuning for automated prompt enhancements, >90% accuracy improvement, weekly business logic improvements)

Step 4

Agent Live

Extend to more workflows and client teams (live and continuous monitoring, human-in-the loop interfacing, node auto-tuning for automated prompt enhancements, >90% accuracy improvement, weekly business logic improvements)

Key Features

Smart Content Aggregation

Multi-Source Monitoring

Tracks RSS, social media, news sites, forums, and publications. One central feed.

Relevance Filtering

AI learns your topics and filters out noise. Only surfaces content that matters to you.

Auto-Categorization

Organizes content by topic, source type, and priority. Tag and label automatically.

Demo

See Content Aggregation in Action

Watch the agent monitor 500 sources over 24 hours, filter 2,000 articles down to 50 relevant pieces, categorize by topic, generate summaries, and deliver a curated digest to your team.