Engineering insights from the Silkate team - practical articles on AI engineering, enterprise software, security and developer productivity.
Four different techniques, four different problems they solve, one client conversation that conflates all of them every week. Here's how we decide which knob to turn.
Frontier models are the default reach for every new feature. They shouldn't be. We moved three production workloads off frontier APIs to smaller domain-tuned models and the numbers were better than we expected.
We ran AI code review across our engineering team for six months. Some of it became indispensable. Some of it made code review actively worse. Here's the honest breakdown.
Prompt engineering was the right framing when the prompt was the whole input. It isn't anymore. Here's how we think about context as a budget and what changed in our workflow.
AI tooling changes the productivity equation. Here is why focused, senior-led teams now have a structural advantage over large consulting organizations.
We've shipped enough production agents now to know which traces matter, which evals catch real regressions and which dashboards no one ever opens. Here's what we instrument and why.
The remote desktop market is shifting. We break down what is driving enterprise RDP adoption and what it means for IT teams evaluating solutions.
Six months ago our engineers worked in IDEs. Today most of them work in terminals with agentic coding tools running long-horizon tasks. Here's what changed, what stayed and what we got wrong on the way.
Encryption at rest and in transit are well-understood. Encryption in use is where AI inference still leaks. Here's how we used confidential computing to close that gap for a regulated workload.
What we learned wiring up the Model Context Protocol across three enterprise multi-agent pilots - the orchestration patterns that held up in production and the ones that quietly fell apart.
When an autonomous agent acts on production data, the question "who did this?" needs a real answer. Here's how we modelled agent identity, scoped permissions and audit trails for an enterprise rollout.
An autonomous agent with tool access can do real damage in production. Here's the sandboxing architecture we built for an enterprise rollout - and the four findings the security audit raised anyway.
AI dashboards stress the frontend in ways traditional apps don't - high-frequency streaming updates, dozens of live cells, charts redrawing constantly. We benchmarked SolidJS and React on a real workload to see what actually mattered.
Static UIs assume every user wants the same thing. Generative UI builds the interface around the task at hand. Here's the architecture we used to make it work without sacrificing reliability or accessibility.
Industry surveys keep finding the same pattern - most enterprise AI pilots never reach production. After running a fair share of them, we think the reasons are less mysterious than the headlines suggest.
Building RAG over PHI is a different engagement from building RAG over public documents. Here's the architecture we used for a healthcare client - what we changed from the default stack and why.
Generic RAG works fine on Wikipedia. It falls apart on regulatory filings, footnoted financial statements and tables that don't fit on one page. Here's the pipeline we built when the off-the-shelf approach hit the wall.
Power Platform can replace custom development for certain workflows. Understanding where it excels and where it creates technical debt is the key to using it well.
A detailed breakdown of the per-monitor DPI challenges we solved building an enterprise RDP client - and the architectural decisions that made it work.
We migrated 150+ users from Google Workspace to Microsoft 365. Here is what the documentation does not prepare you for.
WebRTC tutorials show you how to connect two browsers on localhost. Production deployments are different. Here is what you need to know before you ship.
Parsing resumes at scale is harder than it looks. Here is what we learned from building an ML-based resume parser - what worked, what failed and where the real complexity hides.