Embedded AI engineering team plus a platform for running and securing production LLM systems

SlashLLM occupies the space between an AI security product and a delivery firm. Its platform, SlashStack, is deployed into the customer's own environment rather than sold as multi-tenant SaaS, and the company's own engineers run it, an arrangement it describes as an integrated service provider model for AI security. The argument behind it is a practical one: most enterprises adopting generative AI do not have a platform team capable of standing up monitoring, evaluation and guardrail infrastructure, and the gap between a working prototype and a production system that can be audited is where projects stall.

The security side centres on an AI security operations function that observes the prompts, responses and tool calls flowing through an organisation's LLM applications, aimed at the failure modes enterprises cite most often: data leakage, toxic output, hallucination and regulatory exposure. Around that sit the engineering concerns that determine whether a system is viable at all, including evaluation harnesses and regression testing wired into CI/CD, telemetry for token consumption, latency and model quality drift, dynamic routing across models to control inference spend, per-customer cost attribution, and private VPC or air-gapped deployment for regulated data. The company also builds agent frameworks, retrieval and vector pipelines, and domain applications in finance, healthcare and legal.