Function calling (tool use) is what gives AI agents the ability to interact with the real world — searching databases, calling APIs, and taking actions. This guide covers how function calling works across GPT-4o, Claude, and Gemini, with code examples and production patterns.
Writing an effective AI RFP requires more than a standard software procurement template. This guide covers the essential sections, evaluation criteria, technical requirements, and common mistakes that derail AI procurement.
Should you build an in-house AI team or hire an AI development agency? This comparison covers total cost, time to production, talent access, IP ownership, and the hybrid model that many successful companies use.
Model Context Protocol (MCP) is an open standard that gives AI agents a universal way to connect to external tools and data sources. This guide explains how MCP works, why it matters, how it compares to A2A and ACP, and how to implement it.
Multi-agent AI systems use multiple specialized agents working together to handle complex tasks no single agent can. This guide covers when you actually need multi-agent, architecture patterns, communication strategies, and the trade-offs vs single-agent systems.
A practical comparison of OpenAI, Anthropic, and Google as LLM providers for business applications. Compare GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro across capabilities, pricing, safety, enterprise features, and when to choose each.
Outsourcing AI development is how most companies build their first AI product. This guide covers when to outsource vs build in-house, how to evaluate partners, engagement models, cost comparison, and how to manage the relationship for success.
Prompt engineering is the most cost-effective way to improve AI agent performance. This guide covers the techniques that work in production — system prompts, few-shot examples, chain-of-thought, structured output, and advanced patterns for agents.
Asking the right questions separates good AI development partners from expensive mistakes. Here are 25 questions that reveal whether a company can actually deliver production AI — covering experience, technical depth, pricing, process, and post-launch support.
How much does it cost to build a RAG system? Full breakdown covering development, vector databases, embedding models, LLM APIs, infrastructure, and ongoing maintenance. Includes cost ranges by complexity and tips to reduce costs.
Choosing the wrong pricing model is as costly as choosing the wrong development partner. This guide compares fixed price, time and materials, dedicated team, and retainer models — with cost ranges, risk analysis, and recommendations by project type.
Agentic AI refers to autonomous AI systems that can plan, reason, use tools, and take actions without step-by-step human instructions. This guide explains how agentic AI works, how it differs from generative AI, real use cases, and how to evaluate whether your business is ready for it.