Insights
Field notes from the control layer
What we’re learning about agentic QE, AI evaluation, and compliance — from inside production engagements.
EU AI Act Enforcement Starts August 2: Your GPAI and High-Risk AI Readiness Checklist
EU AI Act enforcement begins August 2, 2026, when the AI Office gains fining power up to 3% of global turnover. Here is the readiness checklist every regulated enterprise needs.
“A gap between what your AI governance policy says and what your evaluation records can prove is the single most dangerous position to be in when an AI Office inquiry lands.”
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What AI Quality Engineering Actually Looks Like in Practice
AI quality engineering goes far beyond traditional software testing. Here is what regulated enterprises must understand before deploying AI at scale.
June 2026
Testing for the EU AI Act: From Obligation to Evidence
EU AI Act compliance testing is now a board-level concern for regulated enterprises. Here's what it means in practice and where to start.
June 2026
AI Assurance in Banking: Where Model Risk Meets Regulation
AI assurance for BFSI is no longer optional. Here's what financial and insurance leaders need to understand before deploying AI in regulated environments.
June 2026
AI Assurance in Healthcare: Earning Trust Before the Clinic
AI assurance for healthcare is no longer optional — it's the operational and regulatory foundation that determines whether clinical AI can be trusted at scale.
June 2026
Synthetic Test Data: Turning AI's Privacy Problem Into an Engineering One
Synthetic test data for AI lets regulated enterprises validate models without exposing real customer records — but only if the approach is rigorous and governed correctly.
June 2026
AI Model Validation Is a Discipline, Not a Launch Gate
AI model validation is now a regulatory expectation in BFSI, healthcare, and insurance. Here's what senior leaders need to understand before deploying AI at scale.
June 2026
Data Residency Isn't Sovereignty: Deploying AI Inside the Boundary
Sovereign AI deployment is reshaping how regulated enterprises control data, risk, and accountability. Here is what decision-makers need to understand before they commit.
June 2026
LLM Red Teaming: How to Test AI Agents Before They Go Live
LLM red teaming exposes failure modes in AI systems before they reach production — here's how to run it rigorously in regulated enterprise environments.
June 2026
Why Agentic QE Is the Next Frontier
Autonomous AI agents are transforming quality engineering from reactive to proactive — handling test generation, execution, and adaptation without per-step human direction.
April 2025
The EU AI Act Compliance Clock Is Running: What Every AI Team Must Do Now
A practical framework for technology teams addressing high-risk AI obligations — fairness, explainability, technical documentation, and post-market monitoring — before enforcement reaches them.
March 2025
SLMs vs LLMs for Enterprise QE: Why Smaller Models Win When You're Serious About Quality
Small language models beat their larger cousins on cost, latency, and compliance for well-defined enterprise tasks — and they are easier to govern.
March 2025
Sovereign AI: Why Regulated Enterprises Can't Afford to Send Their Models Abroad
Data residency and governance requirements are pushing regulated banks and insurers toward AI deployed entirely within their own infrastructure — here is the architecture that makes it work.
February 2025
RLHF in Production: The Gap Between Research Paper and Real Eval Pipeline
Annotator disagreement, reward model overfitting, and distribution shift — the practical challenges of reinforcement learning from human feedback that papers gloss over.
February 2025
Synthetic Data Management Is Not a Data Problem — It's a Quality Problem
Generating synthetic data is easy. Generating synthetic data that is statistically valid, business-rule compliant, and provably effective for testing is a quality discipline.
January 2025
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