Lapis
Jul 2025 - Present
Founding Engineer / Full-Stack + AI Systems
Built an AI-native research platform with equal focus on product clarity, user workflows, and deep technical systems.
Lapis is an AI-native project management platform for research teams, not just a search product. The work spanned most of the platform: multi-surface product development, NeuralCore agent orchestration, semantic retrieval, document and knowledge-base systems, import and upload pipelines, tabular dataset handling, enterprise integrations, authentication and permissions, and the day-to-day UX researchers use to run work. Built across Next.js, Node.js, TypeScript, Supabase/Postgres, Azure/OpenAI, pgvector-style retrieval flows, and a growing data pipeline that supports both unstructured documents and structured datasets.
Overview
Lapis
Worked across product, UX, and engineering to shape the researcher experience end to end, from board and document workflows to AI agents, retrieval, tabular data, integrations, and platform infrastructure.
Platform
AI-native PM for research teams
Core systems
Agents, search, docs, datasets
Product lens
UX, workflows, systems thinking
Team
Built with a 5-person founding team
Detail
Platform
Built Lapis as a full product platform for research teams: project workspaces, board experiences, result capture, document systems, onboarding flows, permissions, and the infrastructure tying it all together. A large part of the work was not just engineering features, but shaping how the product should feel, flow, and support real research work.
AI + Retrieval
Developed the NeuralCore architecture around classification, planning, retrieval, execution, and response streaming. That included semantic search, project-scoped retrieval, document/entity handlers, context management, evaluation harnesses, and AI-native product behavior instead of a simple chat wrapper.
Documents + Data
Built the document and ingestion layer researchers actually operate in: uploads, imports, review states, embeddings, file viewers, knowledge-base organization, and structured dataset support for CSV/XLSX-style analytical workflows and tabular querying. That work also required thoughtful UX for dense information, status feedback, and multi-step workflows.
Product + UX
Contributed heavily to the product layer itself: translating complex AI and data capabilities into interfaces researchers could actually navigate, trust, and use. That included workflow design, interaction decisions, UI implementation, and making technically heavy features feel more coherent inside the platform.
Integrations + Access
Implemented and supported production integrations across GitHub, Google Drive, OneDrive, and SharePoint-style enterprise workflows, alongside authentication, scoped permissions, org/project isolation, and the operational plumbing required to make the platform usable in real teams.
Outcomes
Stack