Writing from the firm on enterprise AI engineering.
A working archive of architectural notes, governance frameworks, and engineering reflections drawn from the firm's practice. Written for the senior technologist, architect, governance officer, or executive who cares how AI actually behaves in production — not how it reads in a vendor deck.
From the archive.
None of this is promotional. It's the kind of material we circulate internally, published outside the firm when it's useful to the senior practitioners who make up most of our readership.
From Prototype to Production: The Hidden Cost of AI Demos
A working demo and a working system are not the same thing. The gap between them sits in the parts no one demonstrates: the evaluation harness, the observability stack, the governance scaffolding, the drift monitoring, the incident playbooks. That gap is where most enterprise AI lives, and where most of it stays.
Retrieval-Augmented Generation in Practice
RAG is now the default architecture for grounded LLM systems, and rightly so. It is the only pattern that lets a model speak to facts that change after the model was trained, with citation back to source, and with access control enforced at the document layer. The published patterns, however, assume a level of data hygiene that most enterprises do not have.
The Case for Small Language Models
Frontier-model thinking dominates the AI conversation. The headlines are about parameter counts, benchmark records and capability frontiers. The production reality for many enterprise workloads is different. Smaller, domain-adapted models often perform better than frontier models on the specific task that matters, cost an order of magnitude less to operate, and sit more comfortably inside the governance and infrastructure constraints that regulated enterprises face.
EU AI Act Readiness: A Practical Framework
The EU AI Act is in force. The obligations are time-bound. The enforcement posture is taking shape. For enterprises operating within the Union or touching EU data subjects through their AI systems, the question has moved from "when" to "how".
Designing Agent Architectures That Don't Fail
Agentic systems are an architectural commitment. They are not a feature flag attached to an existing application. The failure modes are different from those of traditional software, the testing discipline is different, and the operational posture has to be designed in from the start. Treating an agent system as "an LLM with tools" is the most reliable way to ship something that fails in production.
UAE Personal Data Protection Law: Implications for AI Systems
The UAE Federal Decree-Law No. 45 of 2021 on Personal Data Protection introduced a framework that, while modeled on GDPR-style principles, has its own structure, scope, and obligations. For enterprises building AI systems that process the personal data of UAE residents, the Law intersects directly with system design.
New writing lands on an irregular cadence. Questions on a specific topic go to [email protected].