RAIDS - Continuous AI Monitoring Platform

RAIDS provides continuous behavioral monitoring for AI systems operating in production, including large language models, structured or tabular models, and time-series models, each with its own detection approach. Unlike traditional AI observability tools, which need model access and focus on technical performance, RAIDS analyzes only AI inputs and outputs to detect deviations from normal behavior as they happen.

RAIDS learns a baseline of what's normal for each system specifically, then continuously monitors for anomalies that may indicate security risks, operational failures, data misuse, or unintended behavior: from sudden breaks, like a jailbroken chatbot, to slow drift, like a lending model skewing gradually over months. Detection runs in real time, with sub-100ms latency for tabular and time-series workloads and near-real-time detection for LLM-based systems.

RAIDS operates as a black-box, non-intrusive solution: it monitors in parallel with your production systems, introducing no additional overhead and requiring no changes to your models, pipelines, or workflows. It's independent and vendor-agnostic, so you can monitor AI systems from any provider using one consistent approach. Every detection comes with the reasoning behind it, including which behaviors deviated, by how much, and how far outside the normal range, building an evidence trail your team, auditors, and enterprise customers can rely on. Human-in-the-loop feedback continuously refines the baseline and improves detection accuracy over time.

• Independent and vendor-agnostic monitoring. Operates as an independent, third-party layer compatible with AI systems from any provider: unbiased behavioral monitoring across your whole environment, not tied to one vendor.
• Black-box, non-intrusive monitoring. Analyzes AI inputs and outputs only, never model internals, training data, or proprietary code. Runs in parallel with production, with no added overhead or disruption.
• Real-time anomaly detection. Sub-100ms detection latency for tabular and time-series workloads, with near-real-time detection for LLM-based systems, so deviations are caught in production, not in a report afterward.
• Behavioral baselining. Learns typical system behavior over time and detects both sudden anomalies and gradual drift against it.
• Explanations, not just alerts. Every flagged interaction includes why it was flagged: the evidence trail that turns "trust us" into something your risk team, auditors, and buyers can verify.
• Human-in-the-loop learning. Your team validates detections directly in the platform; that feedback refines the baseline and reduces false positives over time.
• Continuous audit trail. ISO 42001 evidence normally takes 6 to 12 months to gather by hand. RAIDS collects it continuously as your systems run, so your audit has what it needs in about 6 weeks instead. RAIDS doesn't make you compliant; it gives you the ongoing evidence that supports your own compliance work.

RAIDS is built for companies that develop and sell AI solutions, in healthcare, legal, financial services, HR, sales, customer service, security, or any other industry putting AI into production.

RAIDS is a strong fit when:

• Enterprise customers are asking how you monitor and govern AI systems in production
• Security and procurement reviews are slowing down deals over AI governance or risk-management questions
• You need to demonstrate alignment with frameworks such as the EU AI Act, ISO 42001, or emerging AI assurance requirements
• You want to proactively show trust, transparency, and responsible AI practices before buyers ask for evidence
• You want a buyer-facing trust seal: a live attestation that monitoring is running right now, not a static badge from months ago, to attach to security questionnaires and RFP responses

RAIDS turns governance and assurance into a competitive advantage: it helps AI vendors accelerate enterprise sales and strengthen customer trust.