TL;DR: There's no single best shared context tool, because the seven options do different jobs. Enterprise search retrieves documents, AI projects hold one person's working context, agent memory APIs are developer infrastructure, and a project brain holds reconciled account state with sources. The incumbent everywhere is spreadsheets plus Slack, which costs knowledge workers 5.3 hours a week recreating or waiting for knowledge. Teams that moved account state into a project brain show the ceiling: 3x accounts per implementation manager at Sellsy, same team, same quality.

You run an FDE or implementation team, your engineers each carry four to eight accounts, and the account state lives in a mix of heads, docs, and chat threads. You've decided that's a problem worth spending money on. Now you're staring at a market where "AI knowledge" describes enterprise search, chat workspaces, personal vaults, memory SDKs, and at least one product category that didn't exist two years ago.

The confusion is legitimate, because these tools genuinely overlap in marketing and genuinely don't in function. The question that cuts through it isn't "which is best." It's "which job am I hiring for?" A tool that's excellent at finding documents can be useless at telling a new engineer what was promised to the customer, and the reverse is also true.

This guide compares the incumbent and seven replacements by the job each actually does, using each vendor's own claims. It ends with an honest section on when each one is the right answer, including the cases where Tandem isn't.

What job are you hiring a shared context tool for?

Start with what your team actually asks during a week: what did we promise this customer, what did we decide about the integration, what's at risk on this rollout, and what's next. Those are questions about account state, not about documents. The shared context problem is that the answers exist, but they exist in one person.

The research on that gap is old and consistent. Panopto's 2018 study of knowledge sharing found that 42% of institutional knowledge is unique to the individual, and that waiting for or recreating it eats hours weekly per employee. AI made the stakes higher rather than lower. MIT's NANDA research, via Fortune, found about 95% of enterprise AI pilots produce no measurable P&L impact, with generic tools stalling because they don't learn from or adapt to workflows. The same report's build-vs-buy split (purchased specialized tools succeed about 67% of the time, internal builds about a third) hangs over the DIY option below.

So evaluate each tool against four requirements: does it hold account state or just documents, can the whole team and its AI tools query it, are answers source-linked, and does anything keep it current. Here's the whole field against those four, before the option-by-option detail.

  • Spreadsheets + Slack (the incumbent). What it holds: Status fields, scrollback. Who can query it: Anyone, by asking a person. Source links on answers: No. Staleness handling: None, re-ask and hope.
  • Enterprise search (Glean, Notion AI). What it holds: Indexed documents and messages. Who can query it: Whole company, permission-aware. Source links on answers: Yes, to documents. Staleness handling: Index refresh, content itself can be stale.
  • Claude Projects. What it holds: Chats plus a knowledge base. Who can query it: You, teammates on Team/Enterprise plans. Source links on answers: No structured sourcing. Staleness handling: Manual re-upload.
  • ChatGPT Projects. What it holds: Files, instructions, project memory. Who can query it: You plus invited collaborators, tiered caps. Source links on answers: No structured sourcing. Staleness handling: Manual, memory accrues per project.
  • Obsidian vault. What it holds: Markdown notes, local-first. Who can query it: The owner, up to 20 sync collaborators. Source links on answers: Links you maintain yourself. Staleness handling: Manual, varies by team.
  • Agent memory APIs (Mem0, Supermemory). What it holds: An agent's memory of its user. Who can query it: The apps a developer builds on them. Source links on answers: Varies by implementation. Staleness handling: Vendor-dependent fact updating.
  • DIY stack (Supabase + Claude Code, vault + Git). What it holds: Whatever you build. Who can query it: Whoever you build auth for. Source links on answers: If you build it. Staleness handling: Whoever you assign, usually no one.
  • Project brain (Tandem's account brain). What it holds: Reconciled account state: promises, decisions, risks. Who can query it: Team, managers, sales, and agents via Claude, Claude Code, ChatGPT. Source links on answers: Yes, facts carry sources. Staleness handling: Reconciliation plus drift surfaced as ranked risks.

Cells are hedged on purpose. "Varies by team" is the true answer more often than any vendor page admits, ours included.

The incumbent: spreadsheets and Slack

Every comparison in this market is really against the tracker spreadsheet and the Slack scrollback, so start there honestly. The combination is free, universal, and requires zero onboarding. It's also where account state goes to fragment. The tracker holds ten fields per account, updated when someone remembers. The real state lives in threads, and finding it means asking the person who was there, which is why interruptions become the retrieval mechanism. Research on task switching puts the cost of each one at 23 minutes and 15 seconds to fully resume the original task.

When it's the right answer: fewer than roughly three accounts per person, one FDE, no handoffs on the horizon. Plenty of early teams are genuinely there, and adding tooling before the pain exists is its own mistake.

Enterprise search: Glean and Notion AI

Enterprise search is what most buyers meet first, because it's what IT already evaluates.

Glean

Glean positions itself as "enterprise AI that understands your company", with 275+ connectors and permission-aware search across enterprise content. For the job it does, finding what was written, wherever it was written, it's the mature option in this list.

Notion AI

Notion AI brings the same retrieval motion into the workspace, searching across Slack, Google Drive, GitHub and more, with meeting notes and agents operating inside Notion. If your team already lives in Notion, it upgrades what you have.

The limit is shared: search retrieves what was written, and account state is mostly never written down. No document says the go-live slipped because the customer's IT lead changed, if that decision happened on a call and a Slack thread. Search over stale or absent documents returns stale or absent answers, confidently. We've made the full argument that enterprise search is not a project brain separately, so this guide won't re-litigate it.

When it's the right answer: the question your team actually asks is "where's that document," at company scale, with permissions. That's a real job, and these tools do it well.

AI projects: Claude Projects and ChatGPT Projects

The second thing teams try is the projects feature in the AI tool they already pay for. It's a rational move, and it works better for one person than for a team.

Claude Projects

Anthropic describes Projects as self-contained workspaces with their own chat histories and knowledge bases, with sharing restricted to Team and Enterprise customers. One practitioner's honest assessment calls Projects a useful shared working memory while noting it isn't a replacement for a system of record and offers no package export. Knowledge goes in as files. There's no event stream, no per-fact sourcing, and no account state that crosses projects.

ChatGPT Projects

OpenAI's version stores files, instructions, and a project-scoped memory, with sharing on all plans under tiered collaborator and file limits, and shared projects locked to project-only memory. The memory is real but personal-by-design: it accrues from chats, invisible to inspection, and a GTM consultancy we spoke with watched a client feed a single ChatGPT project for two years and still lose account context.

Both also stop at the chat window. Nothing reaches the terminal where FDEs increasingly work, a gap Anthropic's own users have registered as an open request for a shared layer across Claude Code, Cowork, and Claude.ai Projects. Our deeper teardown of Claude Projects and ChatGPT Projects for client work covers the per-client sprawl pattern.

When it's the right answer: one person's working context on a bounded engagement, or a small team on Enterprise plans that needs shared prompts and files rather than shared account state.

Obsidian and the local-first vault

Obsidian deserves its following. A markdown vault, local-first, infinitely linkable, and it pairs naturally with Claude Code for the solo FDE. As a personal second brain for client work, it may be the best tool on this list, and we've written up the solo Obsidian setup that works, and where it breaks.

The break is the team. Obsidian's own collaboration docs state that fine-grained permissions are not supported, all collaborators get owner permissions, real-time editing doesn't exist, and shared vaults cap at 20 people with conflicts merged at sync. The reader who isn't in Obsidian (your manager, sales) simply can't ask it anything. And the vault drifts, because reconciliation is a chore with no owner. One FDE we spoke with, a founding FDE at a vertical fintech, was blunt about his own setup: "the number one thing is I cannot share the Obsidian brain with other people."

When it's the right answer: a solo FDE, or a personal thinking tool that deliberately isn't the team's system of record. Nothing wrong with keeping one alongside a shared layer.

Developer routes: agent memory APIs and the DIY stack

Two options on the list are things you build with rather than things a team adopts, and they belong together in the evaluation. The newest aisle sells memory as infrastructure, and fairness matters here: it's developer tooling, and good at its job.

Mem0

Mem0 describes itself as drop-in memory infrastructure for AI agents and apps, extracting user preferences and histories from interactions so an agent remembers its user across sessions.

Supermemory

Supermemory positions as the memory layer for AI agents and a context engineering platform, building user profiles and knowledge graphs, with both a personal app and a developer API.

Notice the noun both share: the user. Agent memory is an agent's memory of a person. What an implementation team needs is the account's memory across people and agents, which is a different data model with different questions: promised, decided, at risk, next. If you're building a customer-facing agent product, this category is your shortlist. If you're trying to give a team shared account state, you'd be buying an SDK and signing up for the DIY build below. The full argument, vendor facts from vendor pages only, is in agent memory vs team memory.

When agent memory is the right answer: you employ developers building agent features, and memory-of-the-user is the feature.

The DIY stack

Between the vault and the vendor sits the internal build: a Supabase store fed by a Claude Code save command, transcripts in a vector database, or an Obsidian-Git-wiki sandwich. Teams with engineers reach for it instinctively, and one FDE we interviewed at a voice-AI startup described the ambition well: "you have kind of a company brain sort of thing."

The prototype takes a weekend. The product takes the year, spent on per-source pipelines, multi-user auth, and reconciliation nobody owns, and the MIT-reported success rate for internal builds sits around a third, against 67% for purchased specialized tools. We've written the honest ledger of that trade in build vs buy a shared context layer, including when building really is right: the layer is your product, a platform team owns it, or policy bars vendors. There's also a lighter-weight middle path using tools you have, covered in the Notion and Airtable DIY project brain.

When DIY is the right answer: a named owner with roadmap time, few sources, or requirements no vendor can meet.

The project brain: reconciled account state

The last option is the category built for this job: a project brain, a maintained store of account state where facts are reconciled and carry their sources. Tandem's version is the account brain, part of the AI workspace for implementation and deployment teams.

Concretely, in Tandem: the brain is edited through an AI chat interface, and several people push to one brain with configurable alert recipients. Engineers, managers, and sales query it from Claude chat, and engineers and their coding agents reach it from Claude Code or ChatGPT through Tandem MCP, built on the open Model Context Protocol standard that Claude, ChatGPT, VS Code, and Cursor all support. You choose what syncs from a Claude Code session into the brain. The risk check returns a list of risks, each with an explanation, the reason, and sources, and a human reviews every one before anything is acted on. Briefs come on demand or scheduled, delivered anywhere including Slack.

The results are the point. Sellsy runs 3x accounts per implementation manager, same team, same quality, with average project duration down 40%. At Aircall, engineers query rollout context, commitments, porting dates, and integration decisions through Claude via MCP, and their CPO's line doubles as the category's job description: "The memory belongs to the team, not the person."

When Tandem isn't the right answer

Honesty cuts both ways, so here's ours. If your team doesn't run client-facing accounts, there's no account state to hold and you don't have this problem. If you operate under HIPAA or similar constraints that rule out our current posture, we're not your vendor today. And if you're a solo FDE with three accounts, an Obsidian vault and discipline will beat paying for a team product, at least until the second FDE arrives. For teams comparing us against delivery platforms rather than context tools, our AI delivery platforms comparison is the better read.

How to run the evaluation

  • Write down the last ten context questions your team actually asked. If they're "where's the doc," buy search. If they're "what did we promise," you need account state.
  • Check who needs answers. Just you: vault or projects. Team plus manager plus sales plus coding agents: only a queryable shared layer serves all four.
  • Demand sources on every answer. An answer you can't trace, you can't put in front of a customer. This single requirement eliminates most of the list.
  • Ask each vendor how facts go stale and who fixes them. Staleness handling is the difference between a memory and a liability. Press us on it too.
  • Pilot on one live account for two weeks. Measure questions answered without interrupting the account owner, before and after.

What stays manual

No tool on this list, Tandem included, removes the human parts. Judgment doesn't transfer through any store. Relationship trust is earned on calls, not queried. In Tandem specifically, you trigger the sync from a coding session and choose what's worth keeping, agents act only with human approval, and the risk list is an input to a human decision, not a replacement for one. And whichever tool you pick, someone still has to care whether the answers are true. Evals for account memory are an open question across this whole market, and you should ask every vendor about them, us included.

Choosing, in one paragraph

Buy enterprise search for finding documents. Use AI projects for one person's working context. Keep Obsidian as a personal brain. Use Mem0-class APIs when you're building agents. Build only with a named owner. And when the job is account state a whole team and its agents can query with sources, that's a project brain. If that's the job on your desk, see Tandem flag the risk on one of your live implementations in a demo and judge the answer quality on your own accounts.

FAQs

What's the best AI knowledge base for an implementation team?

Wrong question, genuinely. Enterprise search, AI projects, and project brains hold different things. If your team's questions are about account state (promised, decided, at risk, next), a project brain fits. If they're about locating documents, Glean or Notion AI fits. Match the tool to the questions you actually ask.

Can we just use the ChatGPT or Claude Projects we already pay for?

For one person on a bounded engagement, yes, and it's the cheapest start. For a team, sharing is plan-gated on Claude and capped on ChatGPT, answers aren't source-linked, and nothing reaches Claude Code where your engineers work. Teams tend to hit those walls around the second person.

Do we need this if we already have Glean?

Maybe not, if your gap is document retrieval. Glean and a project brain answer different questions, and some teams run both. The test: ask Glean what was promised to your hardest account and whether the answer carries sources you'd show the customer.

How do agent memory tools like Mem0 differ from a project brain?

Mem0 and Supermemory are developer infrastructure for an agent's memory of its user. A project brain is a product holding the account's memory for a whole team. One is a component you build with, the other is a system your team queries on day one.

How fast do teams see results from a project brain?

Varies by team, so distrust anyone quoting a universal number. The published cases are the honest reference: Sellsy reports 3x accounts per implementation manager, and Spendesk reports call prep cut from 45 minutes to 5. Your mileage depends on how much of your account state is currently trapped in heads.

When should a team stay on spreadsheets and Slack?

One FDE, a handful of accounts, no handoffs coming. The incumbent is free and everyone knows it. The switch trigger is usually the second engineer, the first handoff, or a manager who can't answer "how's the rollout" without three Slack interruptions.

Key terms

Shared context: the account state a whole team and its AI tools can query, defined by four questions: what was promised, what was decided, what's at risk, what's next.

Project brain: the category artifact, a maintained and queryable store of reconciled, source-linked project facts. Distinct from a knowledge base of curated documents.

Account brain: Tandem's product part, a project brain with risk detection, Tandem agents operating under human approval, and a customer workspace.

Forward deployed engineer: A forward deployed engineer is an engineer who spends roughly half their time with customers understanding the problem and half building the solution for that specific customer.

Enterprise search: permission-aware retrieval across a company's documents and messages, exemplified by Glean's 275+ connector approach. It finds what was written, which is a different job from holding account state.

Agent memory: infrastructure (Mem0, Zep, Supermemory) that lets an AI agent remember its user across sessions. Memory of a person, not of an account.

MCP (Model Context Protocol): the open standard, described by its maintainers as a USB-C port for AI applications, that lets tools like Claude Code query external systems. Tandem MCP is Tandem's server for it.