Strengthen and optimize cyber operations

Accelerate defensive and offensive cyber strategies by automating repetitive tasks, improving reconnaissance, and scaling testing to stay ahead of evolving cyber threats.

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Problem

Traditional penetration testing can’t keep up with the growing complexity, speed, and sophistication of modern cyber threats.

System complexity

Cloud, IoT, and microservices architectures expand attack surfaces and are difficult to map

Evolving threats

Adversaries now use AI and automation, shrinking the window to detect vulnerabilities

Time and resource drain

Manual testing is slow, expensive, manual and often incomplete

Skills shortage

Security talent gaps and staffing shortages limit how often and how deeply tests can be performed

Tedious reporting

Documentation consumes valuable time and reduces focus on higher-impact testing

Solution

Seekr transforms penetration testing with an agentic AI co-pilot that automates security operations and penetration testing, for faster, more accurate and scalable red-teaming, without consultants.

Automation

Offloads repetitive scanning, data collection, and reporting tasks to simultaneously analyze multiple threats

Speed

Accelerates reconnaissance and vulnerability identification

Adaptivity

Continuously learns from new threats and updates test logic automatically

Accuracy

Enhances detection with pattern recognition and data-driven intelligence

Efficiency

Reduces costs and improves scalability without sacrificing depth or hiring additional resources

How it works

Seekr’s specialized cybersecurity AI agents work together to automate, scan, map threats and take action. Multiple specialized AI agents work together to red-team, creating and executing penetration testing from start to finish.

Project Manager AI Agent

Develops a plan by analyzing scans (or evaluation of authenticated PCAP)

Cyber Analyst AI Agent

Analyzes scan results and results of enumeration and exploitation attempts and identifies opportunities and priorities

Researcher AI Agent

Uses document RAG against CVE/CPE documentation, and other sources, to identify potential threat vectors

QA AI Agent

The Code Assessor evaluates the results of code execution, and confirms/denies success, making recommendations to improve code

Built on SeekrFlow

Edge-Compatible Data Engine

Parses scan logs, label files, and routing data from decentralized systems

Agent Framework

Planner and Evaluator agents coordinate multi-step investigations

LLM-RAG

Generates grounded, natural language explanations for suspicious label behavior

Rapid deployment. Reliable performance.

0x

more accurate model responses

0x

faster to prepare a dataset vs. others

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minutes or less to build a production-grade LLM

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See it in action

See how this AI solution works for your team. Request a live walkthrough with one of our experts and explore how it can adapt to your unique workflows and data.

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