Disseqt AI provides a Responsible AI Operations platform that integrates as service‑as‑code components into CI/CD pipelines to continuously monitor, simulate, and enforce policy controls across the AI stack. The system captures agent decisions, detects drift and anomalies in real time, and automatically generates audit‑ready evidence for standards such as NIST, helping enterprise AI teams reduce compliance risk and accelerate production releases.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises deploying agentic AI systems face limited testing, opaque runtime behavior, and insufficient governance, which leads to high failure rates, regulatory risk, and costly post‑deployment remediation.
Solution
Disseqt AI delivers a Responsible AI Operations (RAIOps) platform that continuously monitors, documents, and enforces policy controls across the entire AI stack. The service integrates as programmable “service‑as‑code” components within existing CI/CD pipelines, enabling automated pre‑launch simulations, real‑time drift detection, and security alerts. Structured audit evidence is generated automatically to satisfy standards such as NIST and GRC, while a cloud‑hosted dashboard provides end‑to‑end visibility into agent reasoning and actions. By offloading compliance and risk monitoring to a low‑carbon ML/CPU runtime, teams can accelerate production releases and reduce the cost of compliance.
Target Audience
The primary customers are enterprise AI/ML engineering teams, DevOps and MLOps groups, and compliance officers in regulated industries that require production‑grade agentic systems.
Features
- System‑level visibility that captures agent decisions, tool interactions, and workflow context across development, staging, and production environments.
- Policy‑aligned guardrails defined as code, automatically enforced at runtime to prevent unsafe or non‑compliant actions.
- Continuous risk monitoring with drift detection, anomaly tracing (5× faster root‑cause analysis) and security alerts delivered within minutes.
- Pre‑launch simulation suite that stress‑tests agents under realistic workloads, security threat models, and performance loads.
- Automated generation of audit‑ready evidence (decision logs, compliance reports) compatible with NIST, GRC, and other regulatory frameworks.
- Plug‑and‑play integration via APIs and SDKs that embed validation and monitoring steps into existing model release pipelines without workflow disruption.
- Low‑carbon ML/CPU inference engine that delivers faster inference than GPU‑based alternatives while reducing energy consumption.