AI Project Direction

Who this is for

You have an idea for an AI product, a startup, a feature, or an internal tool - but you do not know how to build it. Maybe you are a non-technical founder who needs to understand what is actually possible. Maybe you are a developer who knows how to code but has never built an AI system before. Maybe you are a product manager trying to scope an AI initiative. You need someone to look at your idea seriously and tell you what it takes to build it.

What we cover

• Evaluating the technical feasibility of your idea honestly
• Recommending the right architecture given your use case: when a RAG pipeline over an existing model is sufficient, when fine-tuning (LoRA, QLoRA, or full instruction tuning) is actually warranted, and when a multi-agent orchestration layer like LangGraph adds value versus adding complexity
• Identifying what to build first versus what to defer
• Choosing between API-first, open-weight deployment, or hybrid: covering the trade-offs of OpenAI and Anthropic APIs versus hosting Llama or Mistral variants, including cost per token at scale and data-residency considerations relevant to GCC clients
• Understanding data requirements, compute requirements, and realistic timelines
• Common failure points specific to LLM products: prompt injection exposure in multi-user systems, context window mismanagement, retrieval quality as the silent killer of RAG products, and latency surprises when chaining model calls
• How to brief a development team or freelancer if you are not building it yourself
• MVP scoping - what is the smallest version of your product that proves the concept

What you walk away with