AI advisor for companies that need more than experiments.

Choose the right AI work before committing the company to the wrong platform.

Companies do not need another abstract AI strategy deck. They need a sharp view of where AI creates leverage, which workflows are safe to automate, which data can be used, which vendors or models make sense, and what the internal team can actually build and support.

Dan's advisory work connects executive priorities to product design, system architecture, tool contracts, backend integration, security, governance, and evaluation. The result is a roadmap that an engineering team can act on and a leadership team can defend.

Common advisory questions

• Where should AI enter our product without creating support, compliance, or trust problems?
• What should be a copilot, an agent, a background workflow, a desktop tool, or a conventional feature?
• Should we use hosted models, private inference, local models, or a hybrid gateway?
• How should tools, permissions, audit logs, and human approvals be designed?
• How do we evaluate quality before customers or employees rely on the system?
• Which AI vendors are credible for our use case, and where will we still need our own engineering?

Deliverables

• AI opportunity map and prioritized use-case portfolio.
• Product architecture for copilots, agents, workflow automation, RAG, or AI-enabled internal tools.
• Model, provider, inference, and data architecture recommendations.
• Governance boundaries for tools, permissions, privacy, audit, and human review.
• Prototype plan and build sequence with engineering risks called out early.