Project Readiness
A great AI project is a collaboration. I handle the technical build — but the best outcomes happen when clients invest some upfront effort too. Here's how we split the work.
Context is everything
AI models and agents can be extremely powerful — but they don't know anything about your business, your preferences, or your processes out of the box. The quality of an AI system's output is directly proportional to the quality of context it receives.
Context can be provided through memory, system prompts, or knowledge bases. But one of the most powerful ways to accelerate an AI project is pre-loading context before the build even starts. The more I understand about how your business actually works, the better the agents I build will perform from day one.
Once gathered, I can help structure and organize your context data — and for ongoing use, set up RAG (Retrieval-Augmented Generation) pipelines so your agents can pull from your knowledge base in real time.
Tip: use voice recordings
One of the fastest ways to capture context is to use a voice recording app on your phone. Walk through your internal processes out loud — how you handle a client request, how you triage incoming work, what your team's preferences are. Record as many details as you can about the workflows the project will touch. Don't worry about structure — just get the information out. I can help organize it from there.
How we split the work
A successful onboarding requires effort from both sides. Here's what each of us is responsible for.
Your task list
ClientGather context data
Brand guidelines, tone of voice documents, process documentation, example outputs, SOPs — anything that describes how your business works. The more, the better.
Document your workflows
Even informal descriptions help. Walk through your current processes — what triggers them, who's involved, what the output looks like. Voice recordings work great for this.
Provide access & credentials
API keys, system access, and tool credentials for the integrations we'll be building. These are handled securely and scoped to the project.
Define success criteria
What does "working" look like? This doesn't need to be formal — rough examples of expected outputs, or even "I'll know it when I see it" with a few pointers, is a fine starting point.
Be available for check-ins
Brief weekly syncs during the build phase make sure we're on track. These are short — typically 15-20 minutes.
My task list
DanielStructure your context
I take the raw context you provide — documents, recordings, notes — and organize it into structured formats that AI agents can actually use.
Set up RAG pipelines
Where appropriate, I build retrieval-augmented generation pipelines so your agents can pull from your knowledge base dynamically — not just at build time, but continuously.
Design the architecture
Map your workflows to agent capabilities, choose the right models and tools, and design a system that fits your actual needs.
Build, test & deploy
Iterative development with working prototypes, tested against real data, deployed to production with proper monitoring.
Ongoing support & optimization
Post-deployment monitoring, prompt refinement, and system evolution as your needs change. See supporting for details.
Ready to get started? The more context you can gather upfront, the faster we move.