Blog
Field notes on shipping AI-native systems with human engineering judgement: architecture, modernisation, automation, and the tradeoffs in between.
Most document AI projects do not fail at the model. They fail at the layer underneath it, in the workflow around it, or at the audit conversation six months later.
A public API on a system that was never designed for one leaks internal models, has no rate limiting, and breaks third-party clients on every change. Here is what actually breaks, and the facade plus anti-corruption layer pattern that lets you add one without touching the fragile core.
The problems teams hit after adopting event-driven architecture are predictable: duplicate delivery, out-of-order events, dual-write drift, poison messages, and flows nobody can trace. Here’s what breaks in production and how to avoid each one.
A practitioner framework for deciding where a human signs off in an automated workflow: confidence-threshold routing, exception queues, sample-based QA, and hard approval gates for irreversible actions.
MCP standardises how apps expose tools and data to LLMs. It fixes a real integration problem, but it’s plumbing, not intelligence. Here’s what to weigh before adopting it in production.
React Server Components move rendering off the client and shrink bundles, but they punish teams that treat them as SSR with a new name. Here is where RSC genuinely helps in production, where it hurts, and how to adopt it on a large app you cannot rewrite.
A practitioner’s guide to choosing between RBAC and ABAC for multi-tenant SaaS: where role-based access holds, where attributes and policy engines earn their keep, and why authorization is one of the hardest things to retrofit later.
Most SaaS teams pick a shared schema for speed, then hit a compliance or noisy-neighbor wall. Here is how pool, silo, and bridge models actually trade off, and the concrete signals that tell you it is time to move a tenant to stronger isolation.
Most secondary sales rollouts don’t fail at launch. They fail six weeks later, when reps quietly stop entering data and the dashboards go blank. Here is why field force automation adoption stalls, and the design decisions that fix it.
The ROI on secondary sales automation is real, but most teams measure the dashboard instead of the six metrics that actually move: data latency, stockouts, forecast accuracy, field-rep time, scheme leakage, and cash cycle.
Most wrong answers from a RAG system trace back to retrieval, not the language model. Here is a diagnosis-to-fix framework for the five failure modes we see most in enterprise document work: bad chunking, query-embedding mismatch, missing reranking, ungrounded generation, and a stale index.
A backend-focused decision guide to monolith vs modular monolith: when the simple version is enough, how to draw module boundaries that survive growth, and how to extract services later without a risky big-bang rewrite.