Different work. The same need for memory.
Founders, developers, researchers, consultants, and teams do different work. But the shape of the problem is the same. Useful context fragments across ChatGPT, Claude, Perplexity, Cursor, and the next tool. Shelvia gives each role one place to capture it, find it, and continue from where the work actually stopped.
- 01Capture
- 02Review
- 03Memory
- 04Retrieve
- Investor call notes
- Competitor research threads
- User feedback interviews
- Pitch-ready context pack
- Decisions log with sources
- Best-performing prompt library
Founders don't only need notes. They need the memory of why a product changed.
Investor research, pitch drafts, market notes, user feedback, and old strategy threads end up scattered across chat sidebars, email, and Notion. Three months in, you can't trace why the positioning shifted. Only that it did.
What they save
- Investor questions and how you answered them
- Competitor research with the source attached
- Pitch drafts and the prompts that produced them
- User feedback themes pulled from interviews
- Decisions: what changed in the product and why
What they retrieve
- The reasoning behind a feature cut
- The prompt that produced your best landing copy
- The competitor citations from three pitch revisions ago
- What you told the last investor and what changed since
Example workflow
- 1
Drop in the investor call notes
Paste, screenshot, or upload. Shelvia detects the source and pulls out the questions worth tracking.
- 2
Pin the decisions
Save “we're narrowing to mid-market” as a decision with the conversation that drove it.
- 3
Generate a Context Pack
Before the next pitch, copy the project's Context Pack. Summary, decisions, sources, prompts that worked. Paste it into your AI tool of choice.
- 01Capture
- 02Review
- 03Memory
- 04Retrieve
- Cursor + Claude Code sessions
- GitHub PRs and reviews
- Bug repro chats
- Reusable prompt library
- Architecture decision log
- Context pack for next session
Coding sessions don't need to start from scratch tomorrow.
You finish a session, close the tab, come back tomorrow. And the agent has forgotten which trade-offs you already rejected. The build notes are in chat scrollback you can't cleanly grep.
What they save
- Build notes per project, with file paths and commands
- Prompts that produced clean diffs
- Decisions about architecture, dependencies, deployment
- Bug repro steps and the chat that diagnosed them
- Source links for libraries you settled on
What they retrieve
- The exact prompt that fixed the auth bug last sprint
- Why you chose Postgres over the alternative
- The migration sequence you ran in staging
- Which agent rules you set for this repo
Example workflow
- 1
Save the prompt that worked
When a Cursor or Claude Code session produces a clean fix, Shelvia flags the prompt as worth keeping. One click and it's in your library, attached to the project.
- 2
Pin the architectural decision
The Claude chat that resolved “monorepo or polyrepo” becomes a decision with the reasoning preserved.
- 3
Generate a paste-ready Context Pack
Next session, paste the project's Context Pack into Cursor, Claude Code, or whichever coding tool fits the task. The agent starts where the last one stopped.
- 01Capture
- 02Review
- 03Memory
- 04Retrieve
- Perplexity + Deep Research threads
- PDFs and screenshots
- Cross-model synthesis runs
- Topic-grouped source library
- Citation-backed claims
- Draft-ready synthesis pack
Source-first archives, not scattered citations.
You ran fifteen research threads, took screenshots of seven papers, and asked several assistants for synthesis. Now the question is: what was the actual citation behind the strongest claim? And which thread had it?
What they save
- Citations with the source URL and access date
- Research threads grouped by topic
- Notes pulled from PDFs with the original passage attached
- Comparisons across models for the same research question
- Open questions that still need a source
What they retrieve
- Every paper you saved on a single topic
- The thread that surfaced the contradicting finding
- The prompt template that generated good synthesis
- The citation behind a claim you made in a draft
Example workflow
- 1
Save the research thread
Drop in a Perplexity thread, a ChatGPT Deep Research run, or a Claude synthesis. Citations come out as source links you can attach to projects.
- 2
Group by topic
Tag sources with the question they answer. The Sources page groups them by topic, by domain, and by freshness, so the Perplexity citations sit next to the Claude analysis they backed.
- 3
Pull the synthesis when you write
When it's time to write, the project's Context Pack gives you the synthesis with citations preserved. Paste-ready for Claude, ChatGPT, or whichever tool you draft in.
- 01Capture
- 02Review
- 03Memory
- 04Retrieve
- Per-client AI sessions
- Industry benchmark sources
- Deliverable-shaped prompts
- Per-engagement decision log
- Reusable prompt vault
- Markdown handoff export
Per-client projects with prompt vaults and source libraries.
Six clients, six worlds of context. You can't mix the prompts that work for one industry into the next, and you can't remember which deliverable cited which source.
What they save
- Per-client projects with their own memory
- Prompt vaults grouped by deliverable type
- Client-specific source libraries
- Decisions logged per engagement
- Recurring frameworks promoted to templates
What they retrieve
- The prompt template that worked for last quarter's SaaS engagement
- The benchmark sources for healthcare versus fintech
- The decisions log for the last engagement with this client
- The export-ready summary for end-of-engagement handoff
Example workflow
- 1
One project per client
Each engagement gets its own workspace project. Imports, prompts, sources, decisions stay scoped.
- 2
Promote recurring prompts to templates
When the same prompt shows up across engagements, save it as a reusable Template you can rerun on new client inputs.
- 3
Export the handoff
End of engagement: export the project as Markdown. The client gets the structured memory; you keep the originals.
- 01Capture
- 02Review
- 03Memory
- 04Retrieve
- Standup + brainstorm transcripts
- User interview themes
- Competitor screenshots
- Pinned decision log
- Reversal history with reasoning
- Milestone briefing pack
The choices that shaped the product, not just the code.
PMs, designers, and engineers all run their own AI sessions. Decisions get made in scattered conversations and never reach the spec. Three sprints later, nobody remembers which option you didn't ship and why.
What they save
- Decisions linked to the conversation that produced them
- Open questions surfaced from research and never answered
- User feedback themes from interviews
- Competitor moves with the source
- The drafts that didn't ship and why
What they retrieve
- Why this feature scope was halved
- The user quote behind the priority change
- What changed between the previous milestone and this one
- The competitor screenshot from the strategy call
Example workflow
- 1
Pin decisions where they happen
PMs save the decisions out of standups and AI brainstorms. Engineers pull them up before implementation.
- 2
Track what was reversed
When a decision is superseded, the new one points at the old one. Reversal history stays readable.
- 3
Hand off to the next milestone
End-of-milestone: the project's Context Pack becomes the briefing for the next phase.
- 01Capture
- 02Review
- 03Memory
- 04Retrieve
- Per-member AI sessions
- Team-wide brainstorms
- Onboarding documents
- Shared prompt library
- Team decision log
- New-hire onboarding pack
Shared workspaces with a real audit trail.
Five people, five private AI subscriptions, no shared memory. New hires onboard by reading Slack scrollback, and the team ends up rediscovering the same prompts every quarter.
What they save
- Shared prompt libraries by use case
- Team-wide decisions with author and date
- A single source library scoped per project
- Activity log of what changed
- Roles that scope who can edit, view, or share
What they retrieve
- The team's best-performing prompt for sales emails
- The decision your colleague pinned six weeks ago
- The new hire's onboarding context as a single paste
- Audit history for compliance reviews
Example workflow
- 1
Set up the shared workspace
Owner invites teammates with roles: admin, member, viewer. Audit log captures every important action.
- 2
Curate the team prompt library
Members nominate prompts; admins approve. The library becomes the team's collective leverage.
- 3
Onboard new members fast
A new hire opens a project, reads the Context Pack, and starts contributing instead of catching up for two weeks.
The work doesn't need to start over every Monday.
Pick the role closest to yours, or start with a free workspace and shape it around the actual work you're doing.