AI Deployment, Observability & Evaluation

Backslash offers the Unified Vibe Coding Security Platform , the definitive solution for managing the security risks introduced by the rapid adoption of AI-augmented development, also known as "vibe coding". We provide B2B organizations with the preemptive security controls necessary to secure their entire Generative AI development ecosystem, ensuring both speed of innovation and continuous security. The Problem: The rise of AI coding IDEs and agents has created a new governance and security blind spot for engineering teams. Backslash research has shown that popular LLMs, when used with simple or "naive" prompts, frequently generate code that is insecure or vulnerable. Relying on developers to craft effective security prompts is unrealistic, leading to vulnerable code 40%–90% of the time. Furthermore, the introduction of unvetted MCP (Model Context Protocol) servers presents unacceptable infrastructure and data security risk. The Backslash Value Proposition: Vibe Securing. Backslash addresses this challenge by shifting the mindset from merely detecting vulnerabilities after code is written to preventing their creation before code is generated. We call this "vibe securing". The platform provides the built-in guardrails and context-aware system needed to achieve true "security by design" for AI-generated code. Key Platform Capabilities: Visibility and Governance: Gain full visibility into where developers are using AI coding agents, which LLMs are active, and which MCP servers and prompt rules are in use across the developer infrastructure. The Vibe Coding Dashboard provides an immediate assessment of their security posture. Secure AI Prompt Rules: Preemptively create secure code using prompt rules that automatically enhance developer input to adhere to security best practices. These rules are transparent to developers, resulting in secure code that is free of vulnerabilities and exposures from the start. Ecosystem Hardening: AI Agent and IDE Hardening enforces uniform configuration across Agentic IDEs (like Cursor and Windsurf) to fence off agentic AI, reduce the attack surface, and prevent unexpected behaviors. MCP Server Security allows you to analyze and vet MCP servers to prevent excessive permissions and insecure configurations that could be exploited by malicious actors. Contextual Risk Mitigation: The proprietary Backslash App Graph Model provides a core code security engine that models the application. It ensures findings are contextual from the outset, eliminating noise and false positives by only flagging vulnerabilities that have a real, demonstrable risk attached. This empowers developers with actionable, real-time security guidance directly in their workspace. Backslash boosts AI adoption across software engineering teams by providing governance and preemptive security controls for security and AI governance teams

Fiddler helps enterprises monitor, conduct root cause analysis, improve, and secure ML, LLM, and GenAI models and applications. * Unified platform for ML, LLM, GenAI, and agentic systems * Custom metrics and dashboards that provide visibility and a holistic view into the performance of how AI systems contribute to organizational KPIs * Fiddler Trust Service which comprises purpose-built, fine tuned models that score LLM prompts and responses at a 6x less expensive rate (compared to LLM as a judge) and are scalable * Fiddler Guardrails that offer <100ms latency and can run in cloud or VPC environments * Available natively in AWS SageMaker * Integrations with NVIDIA NeMO Guardrails and NVIDIA NIM

Edra captures institutional knowledge from support tickets, emails, and communications to automatically build living playbooks which enable AI automation. Founded by ex-Palantir AI leads, we turn tribal expertise into auditable automation - solving the knowledge foundation problem which is needed to make enterprise AI actually work.

Mitratech simplifies AI governance by providing a centralized inventory and request system for all AI applications. Enabling firms to manage risk from third-party's use of AI as well as any in-house AI development and extending this to automatically discovering "shadow" AI development outside of IT's control.

Traceloop is a platform designed for monitoring and evaluating large language models (LLMs). It creates a continuous feedback loop that enhances model reliability by automatically running quality checks on real data, such as assessing faithfulness and relevance. With just one line of code, users can gain immediate visibility into various model metrics, including response times and accuracy. Traceloop allows teams to define what quality means for their specific applications, enabling custom evaluations. The platform is compatible with multiple programming languages and integrates with over 20 LLM providers and tools, making it suitable for both startups and enterprise environments. It is designed with compliance standards in mind and offers transparency through its open-source framework.