Overview
Platform engineering
Patterns for bringing repeatable governance and deployment controls into AI delivery workflows.
Blog
InsightsonAIsafety,evaluation,andplatformdelivery
Read how enterprise teams are approaching red-teaming, evaluation observability, and multi-model operations in fast-moving AI environments.
Overview
Red-team programs
Approaches for structuring adversarial scenarios and measuring model resilience over time.
Overview
Observability practices
Lessons from teams instrumenting AI evaluations with actionable metrics and trace data.
Featured article
Red-Team Programs10 min read
Red-Teaming Large Language Models: Beyond Manual Testing
Manual prompt testing catches perhaps 10% of what a structured red-team programme finds. Enterprise deployments need systematic adversarial coverage — not heroic individual effort.
AK
Alok Kulkarni
Observability8 min read
Observability Beyond Accuracy: Tracing the Full Evaluation Lifecycle
Most teams measure accuracy. The best teams measure the entire evaluation lifecycle — from input telemetry to judge reasoning traces. Here is what you are missing without trace-level observability.
AK
Alok Kulkarni
Red-Team Programs9 min read
Escalation Failures in Contact Centre AI: What the Data Shows
Enterprise contact centre AI fails in predictable patterns when conversations leave the intended path. Five failure modes account for the majority of incidents — and all of them are testable before deployment.
AK
Alok Kulkarni
Research11 min read
The EU AI Act's Evaluation Requirements: A Practitioner's Guide
The EU AI Act entered into force in August 2024. Compliance obligations for high-risk AI systems are now active. Here is what the Act actually requires from your evaluation programme — and the gaps most teams have.
AK
Alok Kulkarni
Platform Engineering9 min read
From Prompt to Production: Building a Repeatable AI Evaluation Pipeline
Most teams test their model before initial deployment. Very few have automated evaluation pipelines that run on subsequent model updates. The gap between a deployment test and a sustainable programme is where most AI quality stories end.
AK
Alok Kulkarni
External resources
Industrystandards&references
Authoritative frameworks, standards, and research cited across our articles.
Security
OWASP LLM Top 10 (v2.0, 2025)
The definitive guide to the top security risks in LLM applications. Updated for 2025 with new findings on agentic systems.
Framework
NIST AI Risk Management Framework
NIST AI RMF 1.0 — the US federal framework for managing AI risk across the full AI lifecycle. Widely adopted internationally.
Security
MITRE ATLAS
Adversarial Threat Landscape for AI Systems — the most comprehensive taxonomy of real-world AI and ML attack techniques.
Regulation
EU AI Act (Official Text)
Regulation (EU) 2024/1689 — the full text of the EU AI Act as published in the Official Journal of the European Union.
Standards
OpenTelemetry GenAI Semantic Conventions
Standardised span attributes and event names for instrumenting LLM and generative AI applications with OpenTelemetry.
Research
Stanford HAI AI Index Report 2024
Comprehensive annual report tracking AI development, deployment, public perception, policy, and safety research worldwide.