Operations at Scale
Deploy AI agents as operational workers inside a cybersecurity stack — supported by a RAG layer for grounded intelligence and ML services for prediction, scoring, and automated response.
Autonomous workers for SOC, threat hunting, compliance, and incident response.
Grounded knowledge from runbooks, incidents, and policy docs — zero hallucination.
Production-grade open source replacing commercial vendors at a fraction of the cost.
Each agent handles a specific high-value task. Deterministic workflows handle structured tasks; dynamic agents handle open-ended investigation.
Deterministic, structured tasks stay as workflows. Open-ended investigation becomes agentic. This separation makes each component easier to secure, test, scale, and audit independently.
Clean separation between interfaces, orchestration, knowledge, and ML — each layer secured, tested, and scaled independently.
Allows AI agents to answer from current, internal knowledge without retraining the foundation model. Retrieval quality and corpus discipline are the keys.
Filter by team, environment, and sensitivity level before the model sees a chunk.
Production-grade OSS tools competing with — and often beating — commercial vendors in capability, flexibility, and innovation speed.
Recommended: hybrid approach — OSS for control, vendor where SLAs are non-negotiable.
Start narrow, ship fast, measure everything. Each phase delivers standalone operational value.
Narrow enough to ship quickly, measurable by design, and exercises the full architecture.