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Resources on SaaS development — building, shipping, and scaling software-as-a-service products from MVP through growth stage.
8 posts
The Vercel AI SDK handles provider abstraction and streaming primitives well. What it doesn't handle — production error recovery, cost control, provider failover, and structured output edge cases — is where most teams lose weeks. This is what we learned shipping AI features with the SDK across enterprise engagements.
Model Context Protocol (MCP) is an open standard that defines how AI applications connect to external data sources and tools. This guide explains what MCP is, how it differs from regular API integrations, and when it matters for your organization — written for CTOs and technical leaders, not developers.
An MCP server is middleware that connects AI systems to business data through a standard protocol. Instead of writing custom integration code for every data source, MCP gives LLMs a consistent way to read databases, call APIs, and execute actions across enterprise systems. This is the architecture layer that separates AI prototypes from production AI.
An AI-powered CRM is not Salesforce with a chatbot bolted on. It is a system where AI handles the work salespeople hate — data entry, activity logging, lead research, follow-up scheduling — so the CRM stays accurate without anyone manually updating it. The difference between an AI CRM that gets adopted and one that gets ignored is whether the AI reduces work for the rep or creates more of it.
RAG (retrieval-augmented generation) and fine-tuning solve different problems. RAG gives a language model access to external data at query time — company documents, product catalogues, knowledge bases. Fine-tuning changes the model's behaviour itself — how it writes, what tone it uses, how it formats output. Most production AI systems need RAG. Very few need fine-tuning. The decision comes down to whether the problem is knowledge (use RAG) or behaviour (consider fine-tuning).
SaaS companies use offshore engineering teams for three things: building the initial product with a non-technical founding team, scaling capacity after product-market fit, and running dedicated feature teams. This guide covers when each model works, when it doesn't, and how to structure it.
Outsourcing SaaS development provides senior engineering capacity at 40-60% of equivalent US hiring cost. The trade-offs are real: timezone discipline, async communication overhead, and the need for clear ownership structures. This guide covers what works and what does not.
The best B2B software products come from people who spent years in an industry and know exactly which processes are broken. Here's how domain experts go from validated concept to production product with the right engineering partner.
