Akarsh C

A four-level plan for adding AI to a product that already has customers: shadow mode, suggestions, approvals, then bounded autonomy.

Follow a support-ticket feature as it grows from a model call into a production request lifecycle involving application data, backend actions, asynchronous work, and client delivery.

A refund request looks like one AI task, but real automation needs your backend several times — to verify, to act, and to deliver. Here is why, told through a refund automation example.

Set up, test, and validate AI features visually. Preview your pipeline, run LLM testing without burning tokens, and catch structured output issues before deployment.

Learn how startups can avoid AI provider lock-in, switch between GPT-5.5, Claude, Gemini, DeepSeek, and Kimi without code changes, compare costs, test safely, and build more reliable AI products.

Direct SDK calls work until they don't. This post breaks down the architecture shift from calling providers directly to routing through an AI gateway — when it becomes necessary, what signals indicate it's time, and what the gateway layer actually does.

A deep technical breakdown of why Elixir and the Phoenix framework — with OTP supervision trees, Oban job queues, Phoenix Channels, ETS caching, and Cloak encryption — are uniquely suited for building production AI gateways and orchestration layers.

The technical decisions that separate AI agents that work in demos from AI agents that survive production — failover chains, prompt caching, request isolation, metered billing, and the infrastructure nobody talks about.

A practical comparison of the best LLM frameworks in 2026 — LangChain, LlamaIndex, Haystack, Semantic Kernel, and more. Learn which one fits your use case, and why most LLM frameworks struggle in production.

Learn how to build production-ready AI systems using backend pipeline architecture. Decouple AI generation from delivery, process webhooks, use callbacks, and stream results in real time.

Discover why we built ModelRiver: real-time AI infrastructure, auto-failover, structured outputs, client SDKs, CLI, and observability for developers shipping AI products.