LogPal AI

I treat LogPal as a learning project. The goal was to work through the architecture of a context-aware AI product, the AI workflow behind it, and how a person stays in control of what the AI produces.

Important context is fragmented across online calls, physical meetings, voice notes, recordings, documents, side conversations, and follow-up feedback.

Most meeting tools solve only one part of that problem: capturing and summarizing the meeting itself. But the information required to actually execute the work often continues changing after the meeting ends.

I designed LogPal around a broader product model: conversation, then context, then intelligence, then action.

Instead of treating every meeting as an isolated transcript, LogPal treats conversations and supporting information as evolving work context. A stakeholder voice note, follow-up recording, document, or later conversation can enrich that context and influence resulting decisions, actions, and reports.

Capture: physical meetings, online meetings, recordings, voice notes, and supported inputs.

Add context: files, notes, recordings, documents, and post-meeting information.

Understand: identify what happened, what matters, what was decided, what changed, and what remains unresolved.

Act: convert intelligence into actions, tasks, reminders, owners, and follow-ups where supported by the source context.

Report: generate structured summaries, updates, decision histories, and recurring reports.

Multi-context capture, AI meeting intelligence, context enrichment, action intelligence, side notes, automated reporting, and multilingual workflows.

Context before automation. AI can organize and surface information, but important decisions and commitments remain under human control.

LogPal shows how I approach AI product development beyond adding an LLM to an existing workflow: start with the underlying information problem, design the context architecture, determine where AI adds intelligence, and preserve human judgment where it matters.