Most AI tools your team uses today start every session from zero. They don't know your customers, your pricing, your decisions, or your processes. You paste context in, the session ends, and tomorrow you do it again.
Company brain software exists to fix that. These tools ingest your company's actual data, keep it current, and make it queryable by your AI agents and your people — so the AI that writes a proposal or answers a support ticket knows what your business actually does.
This guide covers nine tools, ranked by how well they solve that problem. Criteria come first, then each entry, then a routing guide to help you choose.
Key takeaways
- Company brain software connects your existing apps (Slack, Notion, Google Drive, CRM, email) into a shared knowledge base that AI agents can query with real context.
- The tools in this category differ on three axes that matter: whether they expose context via MCP (so any agent can use it), how they handle permissions, and whether they require engineering work to set up.
- Most teams don't need a custom RAG pipeline — they need a managed context layer that stays current without maintenance.
- Gyld is the strongest option for teams that want their existing AI agents (Claude, ChatGPT, Cursor) to query company context via MCP, with no pipeline to build.
- Several tools here serve adjacent needs (wiki, project management, meeting notes) and are worth considering if your problem is narrower than a full company brain.
How we judged these tools
Every entry was evaluated on the same five criteria. They're listed in order of weight.
1. Context quality and freshness. Does the tool pull from the apps your team actually uses, and does it stay current when those sources change? A knowledge base that goes stale in two weeks isn't a brain — it's a snapshot.
2. Agent accessibility via MCP. The Model Context Protocol is the emerging standard for how AI agents query external knowledge. A tool that exposes an MCP server lets Claude, ChatGPT, Cursor, or any MCP-compatible agent pull your company context mid-task, without copy-pasting. This is now a meaningful differentiator.
3. Permission model. Can you control what's visible to whom — and to which agents? A tool that gives every agent access to everything is a liability. Private, team-scoped, and company-wide tiers matter.
4. Setup and maintenance burden. How much engineering does it take to get running, and to keep running? A pipeline you have to maintain is a cost that compounds.
5. Source citation. When the AI answers a question, does it tell you where the answer came from? Without citations, you can't verify — and you can't trust the output for anything consequential.
Pricing is noted where publicly stated. Where it isn't, we say so.
The 9 best company brain software tools in 2026
1. Gyld
Best for: Founders and engineering teams who want their existing AI agents — Claude, ChatGPT, Codex, Cursor — to query real company context via MCP, without building or maintaining a pipeline.
What it is: Gyld is a business context layer for AI: a managed company brain that ingests data from the apps your team already uses (Slack, Gmail, Outlook, Notion, Google Drive, HubSpot, Salesforce, QuickBooks, and more) into a per-company knowledge base, then exposes that knowledge as MCP servers. Any MCP-compatible AI agent plugs in and gets company context on demand. There's no fine-tuning, no hand-built RAG pipeline, and no infrastructure to maintain.
Why it fits:
- Ingests from the apps your team already uses — you don't move data or rebuild workflows.
- Exposes company knowledge as MCP servers, so agents like Claude Code or Cursor query it natively, mid-task.
- You choose exactly what gets indexed; nothing is ingested without your selection.
- Permissions are granular: private, team-scoped, or company-wide, per source.
- Every answer carries source citations, so you can verify what the agent pulled and from where.
- No fine-tuning, no vector database to maintain — the knowledge layer stays current as your sources update.
Trade-offs:
- MCP is the access pattern; teams that don't use MCP-compatible agents yet won't get the full benefit until they do.
- Gyld is purpose-built for business context, not for project management or wiki editing — it complements those tools rather than replacing them.
- As a newer entrant, the integration list is growing; check what a managed company brain looks like on your own apps for the current set.
Pricing: Not publicly listed — contact for details.
2. Notion AI
Best for: Teams already living in Notion who want AI answers grounded in their existing pages and databases.
What it is: Notion AI is an AI layer built into the Notion workspace. It can answer questions, summarize pages, draft content, and search across your Notion content. As of 2026, Notion has also expanded its connectors to pull in content from Google Drive and Slack, according to Notion's own documentation.
Why it fits:
- Zero setup if your team is already in Notion — the AI reads what's already there.
- Answers questions in the context of your actual pages, databases, and docs.
- Good for teams whose knowledge lives primarily in Notion.
- Connectors for Google Drive and Slack extend the reach beyond Notion itself.
Trade-offs:
- Context is bounded by what's in Notion (and connected sources). If your real operational data lives in Salesforce, HubSpot, or email threads, Notion AI won't reach it.
- No MCP server — the AI context stays inside the Notion interface and doesn't feed other agents.
- Knowledge freshness depends on how well your team maintains their Notion pages, which varies widely in practice.
- Permissions follow Notion's page-level model, which can get complex at scale.
Pricing: Notion AI is included in the Plus plan at $12/member/month (billed annually), per Notion's pricing page.
3. Confluence + Atlassian Intelligence
Best for: Engineering and product teams already using Jira and Confluence who want AI search across their existing documentation.
What it is: Confluence is Atlassian's wiki and documentation platform. Atlassian Intelligence adds AI search, summarization, and Q&A across Confluence pages, Jira tickets, and connected Atlassian products. Vibe's 2026 AI collaboration roundup notes Confluence's strength in structured team documentation.
Why it fits:
- Deep integration with Jira means engineering context (tickets, sprints, decisions) is queryable alongside documentation.
- Strong for teams with mature documentation practices and a lot of existing Confluence content.
- Atlassian Intelligence can summarize pages, find related content, and answer questions across the connected workspace.
Trade-offs:
- Confluence is a wiki — knowledge quality depends entirely on how diligently your team writes and maintains pages. Stale documentation produces stale AI answers.
- No MCP server; AI context stays within the Atlassian ecosystem.
- Setup and administration can be significant for large instances.
- Atlassian Intelligence is not available on all plans; the feature set varies by tier.
Pricing: Confluence Standard starts at $4.89/user/month; Atlassian Intelligence features require Premium or Enterprise tiers, per Atlassian's pricing page.
4. Guru
Best for: Customer-facing teams (support, sales, success) who need a verified, single-source-of-truth knowledge base with AI search.
What it is: Guru is an AI-powered knowledge management platform focused on keeping answers accurate and verified. It uses a card-based system where knowledge owners are assigned to verify content on a schedule, reducing the stale-knowledge problem that plagues most wikis.
Why it fits:
- Verification workflows mean knowledge has an owner and an expiry — a meaningful structural advantage over unmanaged wikis.
- AI search surfaces the right card for a given query, with the verified answer front and center.
- Integrates with Slack and browser extensions so answers are accessible in context.
- Good fit for support teams that need consistent, accurate answers at speed.
Trade-offs:
- Knowledge has to be written into Guru cards — it doesn't ingest from your operational apps (CRM, email, project tools) automatically.
- No MCP server; context doesn't feed external AI agents.
- The card model works well for stable, reference knowledge but is less suited to fast-moving operational context (deal status, recent decisions, open tickets).
- Verification workflows add overhead; teams without a knowledge manager often let them lapse.
Pricing: Guru's pricing is not publicly listed on their website as of this writing.
5. Lore
Best for: SMB to mid-market teams that want a self-contained company brain with in-platform chat, scheduled agents, and MCP access.
What it is: Lore is an AI knowledge platform designed around fast document ingestion, in-platform chat grounded in company context, reusable skills, and scheduled agents. Brewster Consulting's 2026 roundup highlights Lore's native MCP server as a standout feature — it gives Claude, ChatGPT, and Cursor access to your indexed company knowledge.
Why it fits:
- Native MCP server means your existing AI agents can query Lore's knowledge base directly.
- Document ingestion is fast; training updates dynamically when source documents change.
- Reusable skills and scheduled agents extend the platform beyond passive search into active workflows.
- In-platform chat gives teams a shared interface for querying company context together.
Trade-offs:
- Primarily document-ingestion focused; less suited to teams whose context lives in operational apps (CRM records, support tickets, transactional data) rather than docs.
- Newer platform with a smaller integration ecosystem than established players.
- Scheduled agents and skills add capability but also complexity — teams without a clear use case may not get the value.
Pricing: Free tier available; paid plans from $350/month, per Brewster Consulting's review.
6. Glean
Best for: Larger organizations that need enterprise-grade AI search across every app in their stack, with strong security and compliance controls.
What it is: Glean is an enterprise AI search platform that connects to 100+ business apps and builds a unified search index across all of them. It uses AI to surface the most relevant results from across your connected sources and has added generative AI features (Glean Chat) that answer questions using your indexed content.
Why it fits:
- Breadth of connectors is a genuine strength — if your company uses many different tools, Glean's indexing reach is hard to match.
- Enterprise security and compliance controls (SOC 2, data residency options) matter at larger organizations.
- Glean Chat can answer questions with citations from your indexed content.
- Permissions are enforced from the source systems — if someone can't see a document in Google Drive, they can't see it in Glean either.
Trade-offs:
- Glean is enterprise-priced and enterprise-scoped; it's not the right tool for a 10-person startup.
- Search is the primary paradigm; it's a retrieval layer, not a context layer that feeds AI agents mid-task.
- No public MCP server — agents don't query Glean natively.
- Implementation typically requires IT involvement and a sales process.
Pricing: Not publicly listed; enterprise contract required.
7. Tettra
Best for: Small teams that need a simple, structured internal wiki with AI search and Slack integration, without the complexity of a full enterprise platform.
What it is: Tettra is a knowledge management tool built for small and growing teams. It focuses on structured internal documentation with AI-powered search and a Slack bot that lets team members ask questions and get answers from the knowledge base without leaving Slack.
Why it fits:
- Simple to set up and maintain — appropriate for teams that don't have a dedicated knowledge manager.
- Slack bot integration means the knowledge base is accessible where the team already works.
- AI search surfaces relevant pages for a given query.
- Verification reminders keep content from going stale.
Trade-offs:
- Like Guru, Tettra requires knowledge to be written in — it doesn't ingest from operational apps.
- Limited to smaller teams; lacks the permission complexity and compliance controls needed at scale.
- No MCP server; context stays within Tettra and Slack.
- The AI layer is search-focused, not generative in the way that newer platforms are.
Pricing: Tettra's Basic plan starts at $4/user/month, per their public pricing.
8. Sentra
Best for: Teams that need bi-temporal awareness and contradiction detection in their company knowledge — specifically, tracking not just what's true but when it became true and when it stopped being true.
What it is: Sentra describes itself as a governed organizational memory layer. Sentra's own 2026 guide defines its approach: it holds decisions, commitments, and context with bi-temporal tracking (when a fact was recorded vs. when it was true), and exposes this over REST or MCP. The guide notes Sentra scores 40% on MEME Cascade and 43% on Absence — benchmarks for organizational memory retrieval — though these are self-reported figures.
Why it fits:
- Bi-temporal awareness is a real differentiator: most knowledge bases can't tell you what was true at a given point in the past, only what's true now.
- Contradiction detection surfaces when two sources disagree, rather than silently returning one answer.
- MCP and REST access means agents can query Sentra natively.
- Commitment tracking — recording what people promised, not just what was documented — is a capability most tools lack.
Trade-offs:
- Sentra is a newer, more specialized platform; the integration ecosystem is narrower than established players.
- The bi-temporal model adds conceptual complexity — teams need to understand the data model to use it well.
- Self-reported benchmark scores (per Sentra's own published figures) are the only available performance data; independent evaluation isn't yet available.
- Best suited to teams with a specific need for temporal and commitment tracking, not general-purpose knowledge management.
Pricing: Not publicly listed.
9. Company Brain (company-brain.ai)
Best for: Small teams or solo operators who want a free, lightweight way to load persistent company context into Claude, ChatGPT, and Copilot sessions.
What it is: Company Brain is a lightweight context-loading tool. Per their own site, the core product is free and stays free. It connects to MCP-compatible AI clients and loads business rules, policies, and strategy into sessions so they don't start from zero. It's positioned as a personal or small-team solution rather than an enterprise platform.
Why it fits:
- Free core product with no expiry — genuinely low barrier to entry.
- MCP and API connectors mean it works with Claude, ChatGPT, and Copilot.
- Solves the specific pain of re-pasting the same context into every new AI session.
- Simple enough for a solo founder to set up without IT involvement.
Trade-offs:
- Lightweight by design; it loads context into sessions rather than building a queryable, permissioned knowledge base across your team's apps.
- No ingestion from operational apps (Slack, CRM, email) — you configure the context manually.
- No team permission model; not suited to multi-team or multi-role deployments.
- The product is in early access; feature depth and reliability at scale are unproven.
Pricing: Free core product, per company-brain.ai.
How to choose the right company brain software
The right tool depends on three questions. Answer them in order.
1. Where does your real company knowledge actually live?
If it's in docs and wikis you already write and maintain, a wiki-layer tool (Notion AI, Confluence, Tettra, Guru) may be sufficient. If it's scattered across Slack threads, email, CRM records, and project tools — places your team uses but rarely documents formally — you need a tool that ingests from those sources directly. Gyld and Lore are built for the latter.
2. Do your AI agents need to query this knowledge mid-task?
If you use Claude, Cursor, ChatGPT, or any MCP-compatible agent, and you want them to pull company context while they work (not just in a separate chat interface), you need MCP server access. Of the tools here, Gyld, Lore, Sentra, and Company Brain offer MCP. The others don't.
For more on why this matters, see how to ground AI in your company data and the four types of enterprise AI context agents actually need.
3. What's your setup and maintenance budget?
If you have engineering capacity and specific requirements, a more configurable platform (Glean, Sentra) may be worth the investment. If you need something running in an afternoon without a pipeline to maintain, Gyld's managed approach or Lore's ingestion workflow are more appropriate. Company Brain is the right starting point if you want free and immediate, with the understanding that it's lightweight.
| Tool | MCP server | Ingests from apps | Team permissions | Best company size |
|---|---|---|---|---|
| Gyld | Yes | Yes (Slack, Gmail, Notion, Drive, HubSpot, Salesforce, QuickBooks, more) | Yes (private/team/company) | Startup to mid-market |
| Lore | Yes | Primarily documents | Not detailed publicly | SMB to mid-market |
| Notion AI | No | Notion + Google Drive, Slack | Notion page-level | Any |
| Confluence + AI | No | Atlassian ecosystem | Atlassian space-level | Mid-market to enterprise |
| Guru | No | Manual (card-based) | Yes | SMB to mid-market |
| Glean | No | 100+ apps | Source-enforced | Enterprise |
| Tettra | No | Manual (wiki-based) | Basic | Small teams |
| Sentra | Yes | Not detailed publicly | Not detailed publicly | Teams needing temporal tracking |
| Company Brain | Yes (MCP connectors) | Manual configuration | No | Solo / small teams |
Why MCP changes the selection criteria
A year ago, the question was: "where do we store our knowledge?" In 2026, the more important question is: "can our AI agents access that knowledge when they need it?"
MCP (Model Context Protocol) is the protocol that makes the latter possible. When a tool exposes an MCP server, it means an agent running in Claude Code, Cursor, or ChatGPT can query your company knowledge mid-task — not as a separate step, but as part of the agent's normal operation. The agent asks a question, the MCP server returns the relevant context with source citations, and the agent continues.
This matters because agents that don't have company context don't just give generic answers — they sometimes give confidently wrong ones. The posts on why AI agents go rogue and AI agent alignment in enterprise cover the failure modes in detail.
For teams already using MCP-compatible agents, MCP server availability should be a hard requirement, not a nice-to-have. For teams not yet using those agents, it's worth evaluating now — the tooling is moving fast, and the July 2026 MCP specification changes make it more relevant to business teams, not less.
Frequently asked questions
What is company brain software?
Company brain software is a category of tools that ingest a company's knowledge from its existing apps and data sources, keep it current, and make it queryable by both people and AI agents. The goal is to give AI tools real business context — your customers, decisions, processes, and commitments — rather than having every session start from zero.
How is a company brain different from a wiki or knowledge base?
A traditional wiki or knowledge base requires your team to write and maintain documentation manually. A company brain ingests from the apps your team already uses — Slack, email, CRM, project tools — and stays current as those sources change. The other key difference is agent accessibility: a company brain is designed to feed AI agents context mid-task, not just answer human search queries.
Do I need an MCP server to build a company brain?
You don't need one to store and search company knowledge, but you do need one if you want AI agents (Claude, ChatGPT, Cursor, Codex) to query that knowledge mid-task. Without MCP, agents can't pull company context during a task — you'd have to copy-paste answers manually. MCP is what closes that gap.
What's the difference between RAG and a company brain?
RAG (retrieval-augmented generation) is a technique for grounding an LLM's answers in retrieved documents. A company brain is a broader concept: it includes retrieval, but also permissions, source citations, freshness, and the ability to track decisions and commitments over time. Most RAG implementations require engineering work to build and maintain; a managed company brain handles that layer. For a detailed comparison, see Gyld vs RAG.
How long does it take to set up company brain software?
It depends on the tool and your data sources. A lightweight tool like Company Brain can be configured in under an hour for a solo user. A managed platform like Gyld is designed to connect to your existing apps without pipeline work — the setup is connecting sources and choosing what to index, not building infrastructure. Enterprise platforms like Glean typically require IT involvement and a longer implementation.
What permissions model should I look for?
At minimum, you want source-level control over what gets indexed, and user/role-level control over who can query what. For teams with sensitive data (customer records, financial data, HR information), you also want the ability to scope knowledge to specific teams rather than making everything company-wide. Granular permissions are what separate a usable company brain from a liability.
Is company brain software safe for sensitive business data?
It depends on the tool's architecture and your configuration. The key questions to ask any vendor: where is data stored, who can access it, is it used to train shared models, and how are permissions enforced? A tool that respects source permissions (so a user can't query data they couldn't access in the original app) and doesn't use your data for model training is the baseline for sensitive business use.
Related reading
- How to Ground AI in Your Company Data (So Agents Stop Guessing) — the practical mechanics of connecting company knowledge to AI agents.
- Why AI Agents Go Rogue — And How Company Context Fixes It — what happens when agents operate without real business context, and why the fix is structural.
- The Four Types of Enterprise AI Context Agents Actually Need — a framework for thinking about what context your agents are missing and what each type costs you.
- MCP Stateless Protocol: What the July 2026 RC Means for Business AI — why the latest MCP spec changes matter for teams building on company context.
If your AI tools are still starting from zero every session, the tools in this list are the practical options for fixing that. Start building your company brain with Gyld and connect the apps your team already uses — no pipeline required.