Alphaholic grew out of a familiar research problem: the evidence behind a view lives in too many places. A company disclosure, an interview note, a spreadsheet, and a saved article may all matter to the same question — but bringing them together takes time.
The problem
The difficult part of research is rarely just finding another piece of information. It is retaining its context, connecting it to the company or question it belongs to, and finding it again when the thesis changes.
I wanted a workspace that could support this whole process: collecting material, reading it, comparing it with other evidence, recording a view, and returning to it later.
What I’m building
Alphaholic connects research sources, company information, working notes, files, and AI assistance. The aim is to reduce the repeated work between reading, organising, and analysing.
- Research: a place to browse and revisit material from different sources.
- Companies: financial information, disclosures, and research context organised around a business.
- Memos & files: working knowledge that remains useful beyond a single conversation.
- AI assistance: summarisation, retrieval, and research tools within a broader workflow.
The workflow
The central loop is simple: collect → connect → investigate → revisit. The useful unit is a research question with its evidence, rather than an isolated answer.
An AI summary can speed up a first pass. Its value depends on whether I can go back to the underlying source, check the statement, and decide what it means. This is why source context and access to original material matter throughout the experience.
Product decisions that matter
- Preserve the path back to the source. A fluent answer is only one step in the research process.
- Build around recurring research tasks. Company work, notes, and reading need their own context.
- Treat incomplete data and processing failures as visible states. A file being uploaded does not automatically make it searchable.
- Keep access to research material aligned with the permissions attached to it.
My role & the work ahead
I work across the product and its implementation, informed by using it in my own research. That includes the interface, data workflows, integrations, and the way AI tools fit into everyday tasks.
Alphaholic is an ongoing product. The next useful improvement is often less about adding a new model and more about making a familiar workflow clearer, more reliable, or easier to return to.