AI-driven exposure management platform automating security remediation workflows
Seemplicity builds an exposure action platform that converts security findings into prioritized remediation tasks via AI agents. The tech stack—Python, Go, Kubernetes, Databricks, ClickHouse, and Datadog—reflects a data-heavy, distributed-systems approach to aggregating and analyzing security data at scale. Hiring is concentrated in senior engineering and leadership roles in Israel, suggesting they are scaling execution rather than headcount, while internal pain points around large-scale data pipelines and ETL processes indicate active infrastructure work to handle the data volume required by their automation engines.
Seemplicity is a Tel Aviv-based security automation platform founded in 2020, now operating with a small, senior-heavy team. The company targets security teams struggling with the gap between vulnerability detection and actual remediation—a workflow problem rather than a detection problem. The product uses AI agents to ingest security findings from multiple sources, contextualize business risk, and output clear, actionable remediation plans. The platform is built on a microservices architecture running on Kubernetes and AWS/Azure, with data pipelines (Spark, Databricks, ClickHouse) handling aggregation and prioritization at scale.
Python, Go, Kubernetes, Databricks, ClickHouse, Apache Spark, PostgreSQL, React, TypeScript, AWS, and Azure, with observability via Datadog, Prometheus, and New Relic.
Tel Aviv-Yafo, Israel. The company was founded in 2020 and remains privately held.
Other companies in the same industry, closest in size