Capstone · MS Strategic Design & Management, Parsons · Spring 2026
Follow.
One shared memory for your team’s AI chats, and the artifacts they produce.
Follow sits between the AI tools your team already uses and turns every chat and document into shared knowledge: one index of what the team knows, and a directory of who knows it.
Project timeline
Spring 2025 → May 2026: field research, expert interviews, and the two pivots that turned Follow from an AI-native document tool into a team-memory layer.
What this is not: a longitudinal deployment study. The peer tests informed a pivot, not a validation; the longitudinal pilot ahead is the move from modeled to measured.
The research
Mixed-methods, and honest about scope: a theoretical spine, plus six primary engagements, from lived experience to expert interviews.
The problem
As work moves into AI workflows, the reasoning behind it disappears into private chats.
Every decision now gets worked out with an AI first: the constraints, the rejected options, the why. All of it stays in one person’s thread, invisible to the rest of the team.
Teams already run as a transactive memory system with each other: everyone keeps a rough map of who knows what. Nothing like it exists for their AI tools: the context that shaped the work scatters across separate chats. Across five concept tests, most people volunteered the same feeling unprompted: their AI-assisted work didn’t quite feel like theirs.
The structural change · today
Same team. Same tools. The reasoning scatters.
Adapted from the capstone deck: three teammates, three AIs, each working in a private thread the others can’t see.
Insights & areas of opportunity
Three findings shaped where Follow plays, and where the opportunity is largest.
AI is the invisible teammate.
Teams already run as transactive memory systems with each other, but not with their AI, because its contributions were never captured in a form the team could route to.
Cross-tool memory is structurally vacant.
Native memory inside one AI tool is solved by vendors. Cross-tool, cross-contributor memory is empty; no vendor with the surface to build it has a reason to make it cross-vendor.
Provenance matters where stakes are high.
The strongest signal for value comes where the cost of being wrong is asymmetric: legal review, regulated work, compliance. That’s also where pricing tolerance is highest.
“The deliverable shipped — we didn’t fail. But I could have done better work if I’d known how my teammates got where they got.”
From the Housing Works experience; the five concept tests kept surfacing the same feeling, unprompted.
The opportunity
How might we give a team one shared, trustable memory, across every AI tool they already use?
The response
Follow.
A shared memory layer that lives between your AI tools, not inside any one of them. This is the whole system:
the whole system
Three teammates, three different AI tools, and the team’s documents, with one shared memory between them. Hover or tap any part to see what it does, and click the index to look inside it.
What it does
Not another search box over your chats. A memory with receipts, and six things fall out of that.
typical RAG
Finds text that matches.
You get back a similar-sounding paragraph: no owner, no date, no idea whether the team still believes it.
Follow
Finds the source.
Who worked it out, in which chat, when, what it connects to, and whether anyone disagrees.
Glean indexes your documents. Follow indexes the reasoning that produced them.
Maya’s Claude, Alex’s ChatGPT, Sam’s Gemini: every teammate’s AI reads and writes the same memory, over MCP.
Every fact carries who said it, in which chat, and when, so answers come back attributed, and you can check them.
When two teammates’ AIs conclude different things, Follow flags the conflict and keeps both sides on the record.
The directory of what your team knows and who knows it, maintained by the work itself, not by anyone filling in profiles.
New decisions retire the old ones they replace, and the old version stays in the trail, so nothing silently vanishes.
Uploads and live docs alike. Follow follows the artifact: its facts land in the same shared memory and version up as it changes.
The sandbox
The shipped dashboard, replicated live.
the story
The team
Aurora is a fictional checkout-redesign team. Maya designs in Claude, Alex runs product in ChatGPT, Sam builds in Gemini: three tools, normally three silos.
The week
One working week, captured as they worked: 16 conversations, 7 files, a 32-fact memory. Three questions ended the week still contested; Follow keeps both sides on the record.
Your seat
You’re the fourth teammate. Ask Follow anything about the week: it thinks, picks its tools, and you watch every call cross the wire. Save the conversation and you join the memory too.
new here? the tour: five stops, two minutes
live model · Follow's actual MCP tool contracts (Follow ↗) · fictional sample workspace
All items
Every captured conversation, uploaded file, and extracted fact: one feed, newest first.
loading the workspace…
MCP console
Follow is headless by design: the shipped server exposes these tools over JSON-RPC at /mcp, to people and machines alike. Same names, schemas, and response shapes here.
A real model, running Follow's real tools. It decides which to call; every call and result crosses the wire below; the answer is grounded in what came back.
You've got the fourth seat on Aurora; Maya, Alex, and Sam's week is already in the memory. Try one of the prompts below: the model thinks, picks its tools, and you'll see the JSON-RPC-shaped traffic in the open. Ask it to save the conversation and this session appears in Conversations and Facts, under “You”.