The choices that decide an AI project.
Four decisions come up in almost every engagement. Each page gives the honest comparison, a verdict table, the cost of choosing wrong and what we would do in your position.
AI agent vs chatbot: which does your business need?
Choose a chatbot when the job is pure question-answering from public content. Choose an AI agent when the job has steps, needs the customer's own data, or must change something in your systems. Agents cost more because they integrate, act and are evaluated; they are measured on problems solved rather than contacts avoided.
RAG vs fine-tuning: which one does your product need?
Retrieval fixes missing knowledge; fine-tuning fixes missing behaviour. Most products need retrieval first, fine-tuning rarely, and both only when an evaluation proves a specific failure survives prompt and retrieval improvements.
Build vs buy AI: a decision framework
Buy when a product covers about 80% of the need and the remaining 20% is not your differentiator. Build when the process or the data is what makes you different, when integration depth decides the outcome, or when residency and audit rules rule the product out. Integrate when both are true: buy the platform, build the layer that is yours.
Eazyware vs building an in-house AI team
Hire in-house when AI is core to your product for years and you can attract and retain the engineers. Use a partner when you need the first system shipped this quarter, when the skills are needed once rather than always, or when you want a fixed price and date. Most mid-market companies do both: a partner builds the first system, an internal owner grows into it.