We operate at the forefront of custom AI integration. Read our developer reports, model guides, and workflow case-studies written by practicing product engineers.
The unglamorous engineering that separates an AI demo from an AI product: cold starts, idempotent webhooks, background jobs that actually finish, and cost you can predict.
Cross-cutting engineering lessons from shipping astrology, options analytics, accounting, and proposal-writing products on one shared stack — including the multi-tenant payment trap that nearly emailed the wrong customers.
The reference architecture we use across products: thin route handlers, RAG grounding on computed data, cost-smart model routing, retries with backoff, evals, caching, and queueing on a platform with no servers.
Concrete, measured tactics that cut our own LLM bill by roughly 70%: model tiering, prompt and output caching, context trimming, structured outputs, and choosing RAG over giant prompts.
App Router patterns we use for AI products: route handlers and streaming, server actions, after() for background work, the edge-vs-node tradeoffs that bite (fonts, OG, Chromium for PDF), and file-convention OG images.
A practical decision framework for RAG versus fine-tuning, when each wins, when to combine them, and why we ground our AI Jyotish chat on computed chart data with RAG instead of fine-tuning a model on astrology.
Discover how modern autonomous AI agents are moving beyond simple chat interfaces to execute complex, multi-step business operations completely hands-free.
An in-depth technical comparison of OpenAI GPT-4o, Anthropic Claude 3.5, and Google Gemini 1.5 APIs for document processing, agentic routing, and speed.
How invoice parsing, OCR table extraction, and automatic ledger entry systems are transforming manual bookkeeping operations in accounting and trucking logistics.
