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19 articles tagged #AI Integration.
P.01Clients ask agencies to 'do something with these PDFs' more often than you'd think. Here's how to actually build document extraction pipelines that work in production: OCR, vision models, and structured output.
P.02AI video tools have moved from toy to production-grade in 18 months. Here's what's real, what still fails, and how to have an honest conversation with a client about it.
P.03A growing class of AI agent frameworks can control a browser the way a human does — clicking, typing, navigating. Here's what works in production, what breaks, and when to actually reach for these tools.
P.04Both approaches customize LLM behavior for your use case, but they solve different problems. Here is how to decide which one you need, how to know when to use both, and what teams consistently get wrong.
P.05Three leading agent orchestration frameworks, three different mental models. Here's when each one earns its place, what each costs you in complexity, and what the choice looks like when you're debugging at 2am.
P.06AI projects introduce IP, liability, and data training questions that standard software contracts don't address. Here's the language clients send and what it actually means for an agency taking the work.
P.07An agent that forgets everything when the session ends is a limited tool. Here are the practical patterns for building different kinds of memory into your agents.
P.08When your AI agent needs to run the code it writes, you can't let it touch your production servers. Here's how the main isolation options work and when to use each.
P.09Figma shipped real AI features at Config 2025 and teams have been using them for nearly a year. Here is what actually works, what still doesn't, and how design workflows at agencies have changed.
P.10Past the chatbot hype, AI is genuinely improving specific e-commerce outcomes. Here are the implementations producing measurable results, and the ones that still mostly disappoint.
P.11Bad documentation is usually not a writing problem — it's a maintenance problem. AI tools are changing the equation by making initial doc generation cheap and doc refresh practical at scale.
P.12The Vercel AI SDK has become the default for building AI features in JavaScript apps. Here is what it actually does, how its core primitives work, and where the sharp edges still live.
P.13Hallucination is not a bug that gets patched in the next model release. It is a property of how language models work. Here are the patterns that actually reduce it in production systems, and what they cost.
P.14Single-provider AI dependencies are a reliability risk. Routing layers like LiteLLM and OpenRouter let you fall back across providers, cap costs, and try smaller models first. Here is the architecture and when it actually matters.
P.15CodeRabbit, GitHub Copilot code review, and similar tools have changed the first-pass review. Here's what they actually catch, where they fail, and the workflow that combines both effectively.
P.16Add similarity search to your existing Postgres database using pgvector. Real setup, indexing strategies, and when you actually need a dedicated vector database.
P.17Building an AI feature is only half the work. Once it's in production, you need to know when it's drifting, what it's costing, and where it's failing. Here's how to instrument LLM applications properly.
P.18Explore Google's latest Workspace update with Gemini AI enhancing Docs, Sheets, Slides, and Drive, empowering users to leverage AI capabilities for more efficient productivity.
P.19Choosing the wrong AI partner wastes 6 months and $50K+. Here is the evaluation framework our CTO clients wish they had before their first engagement — 23 questions across technical depth, process, and culture.