AI Agent

Agente IA de búsqueda de candidatos

Find specialized talent that isn't on LinkedIn

Hard-to-find roles filled. 50+ niche communities scanned. Hidden talent surfaced. That's deep-web sourcing on autopilot.

Benefits

Go Beyond the LinkedIn Search Bar

The best developers, designers, and specialists aren't updating their LinkedIn profiles. They're building on GitHub or posting on Dribbble. The Niche Talent Sourcer finds them where they actually work.

40%

Unique candidates

25%

Higher response rate

Zero

Competition

Agentic Flows

Specialized Sourcing Workflow

Discovery workflow: niche platform scanning, work sample analysis (code quality, design portfolio), skill inference, cross-platform identity matching, contact discovery, and personalized outreach generation.

How it works

Deep-Web Talent Discovery

The agent scans specialized platforms (GitHub, Behance, Kaggle, StackOverflow), analyzes code/portfolios to verify skills, infers contact info, and engages talent based on their actual work.

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

Connects to Niche Platforms

Scans GitHub, GitLab, StackOverflow, Dribbble, Behance, Kaggle, and Hugging Face. Syncs candidates to Greenhouse, Lever, and Gem. Enriches data with social footprints.

Implementation

Sourcing Niche Talent in 7 Days

Define your technical or creative requirements. Connect your ATS. The agent starts surfacing unique profiles from specialized communities within one week.

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

Technical Sourcing Tools

Code Analysis

Evaluates GitHub repos for language proficiency and code quality. Finds true senior engineers.

Portfolio Scoring

Analyzes design portfolios for style and quality. Matches creative talent to your brand aesthetic.

Community Reputation

Weighs forum contributions and peer recognition. Identifies respected experts.

Demo

See Niche Sourcing in Action

Watch the agent find a Machine Learning Engineer via a Hugging Face model contribution, verify their Python skills on GitHub, and send an email referencing their specific model architecture.