Global Cybersecurity Vendor

ML Pipeline Processes Terabytes of Cybersecurity Data Daily

Terabytes

Of data processed daily

Reduced

Analyst investigation workload

The Challenge

Security Analysts Were Overwhelmed by the Volume of Raw Threat Data Requiring Manual Review

The client needed to reduce the volume of cyber threats that security analysts must manually review and investigate. The sheer scale of data made manual review impossible without automation.

Our Approach

Scalable Unsupervised ML Pipeline Detects Anomalies in Time-Series Security Data Automatically

Blindspot delivered scalable machine learning pipeline algorithms focused on unsupervised anomaly detection applied to time-series data. The approach identifies unusual patterns without requiring labelled training data.

Results

Terabytes Processed Daily — Analysts Focus on Investigation, Not Raw Data Triage

The platform processes terabytes of data on a daily basis and significantly reduces the investigation workload on human analysts. Suspicious activities are automatically flagged so analysts can focus on high-value investigation tasks.

AI Agents Deliver Personalized Night Radio Broadcasts, Cutting Costs by Up to 60%

Seznam deployed a multi-agent AI system where one agent produces time-optimised playlists and selects news, while another writes engaging scripts and applies text-to-speech using real presenter voices — fully automating overnight radio broadcasts.

40–60% Operational cost reduction

Expert ML Team Enhances DFLabs' Cybersecurity Product

DFLabs required specialised AI and ML expertise to strengthen their cybersecurity product. Blindspot provided a dedicated expert team who implemented machine learning capabilities directly into the product offering.

Enhanced ML capabilities in product

Network Monitoring Provider

Real-Time Network Anomaly Detection Deployed Across Client Base

A network performance monitoring provider needed a scalable ML solution deployable across their entire customer base. Blindspot designed a pipeline that processes diverse network indicator data through multiple anomaly detectors, enabling real-time problem identification.

Real-time Anomaly detection

Next Steps

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