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Bolster AI Tech Stack
Real-time phishing and brand impersonation detection across web, social, and dark web
Bolster operates a multi-channel threat detection platform built on Python, Rust, Node.js, and a distributed data layer (PostgreSQL, Scylla, Cassandra, Elasticsearch). The stack reveals a company optimized for real-time ingest and analysis at scale—reinforced by heavy use of message queues (SQS, RabbitMQ) and caching (Redis, Memcached). Current hiring is security-heavy with emerging sales and marketing push: 6 security roles against 1 engineering and 1 marketing hire signals a shift from pure detection capability toward go-to-market operations, particularly ABM and demand generation at SMB/mid-market.
Tech Stack 23 technologies
Core StackNode.js Python Rust PostgreSQL Elasticsearch Cassandra MySQL TypeScript RabbitMQ Kubernetes Docker AWS Redis CloudFront Akamai Slack Zoom Scylla AWS SQS GCP Azure Memcached Varnish
What Bolster AI Is Building
◆Challenges
- Protecting brands from phishing and scam sites
- Improving threat-hunting capabilities
- High-risk escalations
- Marketplace fraud protection
- Protecting brands from impersonation
- Pipeline scaling inbound to abm
- Enterprise abm program expansion
- Phishing scam detection
- Brand protection from online fraud
- Cyberattack prevention
▲Active Projects
- Real-time fraud detection platform
- Emerging fraud trend analysis
- Advanced phishing investigation
- Automated phishing detection engine training
- Marketplace threat detection engine
- Takedown actions during active proof-of-concept evaluations
- Targeted threat hunting for malicious domains
- Operationalize abm programs for enterprise
- Scale demand generation for smb and mid-market
- Build predictable pipeline engine
Hiring Activity
9 roles · 6 in 30d
Department
Seniority
Company intelligence
