Consumer hardware & personal AI
Personal knowledge system
A full-stack LLM product that transforms continuous audio from a wearable device into a personal knowledge base—with retrieval-augmented answers users can explore through conversation.
- RAG
- LLM
- Full stack
- Agents
Question
Retrieval
embed & search
Context
Model
Answer
Context
A digital agency building a wearable audio product needed software that could ingest continuous, high-volume personal recordings and make that archive genuinely useful—not just stored, but searchable and discussable. The challenge spanned ingestion at scale, privacy-sensitive personal data, and an interface non-technical users would actually adopt.
What we built
We architected a proprietary LLM application with retrieval-augmented generation at its core: pipelines to process incoming audio, build and maintain a personal knowledge index, and serve grounded answers through a conversational interface. Delivery included backend APIs, AI orchestration, and coordination with a dedicated frontend contributor—while aligning scope and tooling with executive leadership throughout.
Delivery highlights
- Designed ingestion and indexing for continuous, high-volume wearable audio data
- Implemented RAG retrieval tuned for personal archives and conversational follow-ups
- Shipped backend services and APIs supporting the product experience end to end
- Led AI development team coordination, intake, and workload distribution
- Consulted with C-level stakeholders on roadmap, emerging tools, and team mentorship
Impact
- Delivered a flagship knowledge product ready for user-facing iteration
- Created a technical foundation the team could extend without re-architecting
- Balanced ambitious AI scope with realistic timelines and resource allocation