Every AI agent in your company is about to start writing into your systems — your CRM, your help center, your Slack channels, your project tools.
The question nobody’s asking is simple: how good is the knowledge they’re writing?
An agent that updates your help center with stale information isn’t automation. It’s damage at scale. An agent that posts outdated context into a Slack channel doesn’t save time — it creates confusion that takes longer to clean up than doing it manually.
AI tools are becoming more capable every day. They can read, summarize, generate, and now take action. But every one of those actions is only as good as the knowledge behind it. In regulated industries, a wrong answer isn’t just inconvenient — it’s a compliance risk.
That’s why we built Guru as the AI Source of Truth. And today, we’re introducing the next step in that vision: Knowledge Sharing — verified knowledge, everywhere it’s needed.
Your source of truth is no longer passive. It doesn’t simply wait to be searched. It actively distributes verified, permission-aware knowledge into the systems and AI agents your teams rely on. Until now, a source of truth was something you went to. Now it comes to you.
Why this matters now
Every team in your organization is adopting AI in some form. Support teams deploy chatbots. Sales teams use copilots. Engineers build internal agents. Executives rely on AI chat tools for research and synthesis.
Increasingly, these systems are not just answering questions — they’re taking action. They update records, draft communications, publish content, and trigger workflows across your stack.
Most AI tools, however, are still pulling from the same scattered collection of documents, chat threads, and repositories that humans have struggled with for years. The difference is that humans learned to double-check. Agents don’t.
As AI moves from assisting work to performing it, knowledge quality becomes a governance issue — not just a productivity issue. And governance does not scale when it has to be rebuilt inside every AI tool.
That is the gap Guru was built to solve.
The journey that made this possible
This launch is not an isolated feature. It is the result of a deliberate, multi-phase strategy to create a governed intelligence layer for the enterprise.
1. Connect everything
Most companies operate across dozens of systems: document repositories, Slack and Teams, CRMs, help desks, project management tools, meeting recordings, and more. Critical knowledge is fragmented across all of them.
Guru connects to those sources, continuously gathers updates, and preserves permissions at the object level. Employees — and AI tools — see only what they are authorized to see. There is no separate access model to maintain.
Rather than replacing your stack, Guru becomes a secure, connected intelligence layer across it.
2. Give people and AI access to the same trusted context
Connected knowledge is only valuable if it is usable wherever work happens. Guru makes verified company knowledge accessible in Slack, Teams, the browser, and the Guru app itself.
Through APIs and the Model Context Protocol (MCP), Guru enables AI chat tools and custom agents to securely retrieve governed, permission-aware knowledge from a single source of truth — without rebuilding retrieval, permissions, or oversight inside every tool.
Rather than duplicating RAG pipelines and access controls across systems, IT teams govern knowledge once. Every connected agent inherits that same policy-enforced foundation.
Whether it’s ChatGPT, Claude, Copilot, Slack agents, or internally built assistants, they operate from the same continuously improving knowledge layer.
People and AI share one company brain — governed centrally.
3. Continuously improve the knowledge behind AI
Connected and accessible knowledge is dangerous if it is wrong. Governance must be built into the system itself.
Guru enables expert-led verification workflows, usage-based signals that surface stale or conflicting content, and full lineage and audit logs for every answer. When an expert corrects or verifies information, that correction becomes the new truth everywhere Guru’s knowledge appears.
Knowledge does not decay silently. It improves over time.
Each of these phases made the next possible. Connecting knowledge created shared context. Shared context enabled governance. Governance made safe distribution achievable. Knowledge Sharing is the natural outcome — where verified knowledge does not remain confined to one interface, but compounds across your entire technology stack.
Knowledge sharing: From source to system
With Knowledge Sharing, your AI Source of Truth no longer waits for a question. It proactively distributes verified knowledge into the systems and agents that need it.
What flows outward is not raw data. It is knowledge that has been corrected by your experts, deduplicated, checked for freshness, scoped to the right permissions, and logged for auditability.
That distinction matters. There is a meaningful difference between AI that can act and AI that can act correctly.
Slack: Where you’ll see it first
Today also marks an important milestone for Slack with the general availability of their Real-Time Search API and MCP Server. Slack is increasingly becoming the surface where humans and AI agents collaborate in real time.
We’re proud to be a launch partner for this moment.
Slack provides the collaboration environment. Guru ensures the agents operating within it are grounded in verified, governed knowledge.
Consider a product update that ships on Tuesday. By Wednesday morning, your customer success channel in Slack can already contain a verified summary of what changed, what customers need to know, and links to updated documentation — all scoped appropriately to the teams involved. No one has to search. No one has to manually draft the update. The right knowledge is already present because your source of truth distributed it.
Slack is where many teams will feel this first. But this capability extends far beyond a single collaboration tool. The same verified knowledge can flow into your CRM, your help center, your work management systems, and any AI agent operating across your stack. Instead of splintered context, you get consistency. Instead of competing answers, you get alignment.
What this means for leaders navigating the agent era
If you lead IT or AI initiatives, you are being asked to move quickly while maintaining security, compliance, and control.
You do not want governance rebuilt inside every AI tool. You do not want multiple retrieval systems to audit independently. And you cannot afford different agents operating from different versions of the truth.
What you need is a policy-enforced knowledge layer that every system and every agent can inherit. One correction. One audit trail. One governed foundation.
Guru is the AI Source of Truth — the governed knowledge layer between your company’s data and every AI tool that acts on it. Instead of managing AI risk tool by tool, you govern knowledge once and allow every connected system to operate from that verified base.
The companies that succeed in the agent era will not simply deploy more AI. They will ensure that every AI system operates from trusted, permission-aware, continuously improving knowledge.
Your source of truth should not sit idle, waiting to be queried. It should actively support the systems that power your business — safely, consistently, and at scale.
Make your AI Source of Truth active
Your source of truth shouldn’t sit idle, waiting to be queried. It should actively support the systems that power your business — safely, consistently, and at scale.
If you’re evaluating how to deploy AI agents responsibly across your organization, we’d welcome the conversation.
Every AI agent in your company is about to start writing into your systems — your CRM, your help center, your Slack channels, your project tools.
The question nobody’s asking is simple: how good is the knowledge they’re writing?
An agent that updates your help center with stale information isn’t automation. It’s damage at scale. An agent that posts outdated context into a Slack channel doesn’t save time — it creates confusion that takes longer to clean up than doing it manually.
AI tools are becoming more capable every day. They can read, summarize, generate, and now take action. But every one of those actions is only as good as the knowledge behind it. In regulated industries, a wrong answer isn’t just inconvenient — it’s a compliance risk.
That’s why we built Guru as the AI Source of Truth. And today, we’re introducing the next step in that vision: Knowledge Sharing — verified knowledge, everywhere it’s needed.
Your source of truth is no longer passive. It doesn’t simply wait to be searched. It actively distributes verified, permission-aware knowledge into the systems and AI agents your teams rely on. Until now, a source of truth was something you went to. Now it comes to you.
Why this matters now
Every team in your organization is adopting AI in some form. Support teams deploy chatbots. Sales teams use copilots. Engineers build internal agents. Executives rely on AI chat tools for research and synthesis.
Increasingly, these systems are not just answering questions — they’re taking action. They update records, draft communications, publish content, and trigger workflows across your stack.
Most AI tools, however, are still pulling from the same scattered collection of documents, chat threads, and repositories that humans have struggled with for years. The difference is that humans learned to double-check. Agents don’t.
As AI moves from assisting work to performing it, knowledge quality becomes a governance issue — not just a productivity issue. And governance does not scale when it has to be rebuilt inside every AI tool.
That is the gap Guru was built to solve.
The journey that made this possible
This launch is not an isolated feature. It is the result of a deliberate, multi-phase strategy to create a governed intelligence layer for the enterprise.
1. Connect everything
Most companies operate across dozens of systems: document repositories, Slack and Teams, CRMs, help desks, project management tools, meeting recordings, and more. Critical knowledge is fragmented across all of them.
Guru connects to those sources, continuously gathers updates, and preserves permissions at the object level. Employees — and AI tools — see only what they are authorized to see. There is no separate access model to maintain.
Rather than replacing your stack, Guru becomes a secure, connected intelligence layer across it.
2. Give people and AI access to the same trusted context
Connected knowledge is only valuable if it is usable wherever work happens. Guru makes verified company knowledge accessible in Slack, Teams, the browser, and the Guru app itself.
Through APIs and the Model Context Protocol (MCP), Guru enables AI chat tools and custom agents to securely retrieve governed, permission-aware knowledge from a single source of truth — without rebuilding retrieval, permissions, or oversight inside every tool.
Rather than duplicating RAG pipelines and access controls across systems, IT teams govern knowledge once. Every connected agent inherits that same policy-enforced foundation.
Whether it’s ChatGPT, Claude, Copilot, Slack agents, or internally built assistants, they operate from the same continuously improving knowledge layer.
People and AI share one company brain — governed centrally.
3. Continuously improve the knowledge behind AI
Connected and accessible knowledge is dangerous if it is wrong. Governance must be built into the system itself.
Guru enables expert-led verification workflows, usage-based signals that surface stale or conflicting content, and full lineage and audit logs for every answer. When an expert corrects or verifies information, that correction becomes the new truth everywhere Guru’s knowledge appears.
Knowledge does not decay silently. It improves over time.
Each of these phases made the next possible. Connecting knowledge created shared context. Shared context enabled governance. Governance made safe distribution achievable. Knowledge Sharing is the natural outcome — where verified knowledge does not remain confined to one interface, but compounds across your entire technology stack.
Knowledge sharing: From source to system
With Knowledge Sharing, your AI Source of Truth no longer waits for a question. It proactively distributes verified knowledge into the systems and agents that need it.
What flows outward is not raw data. It is knowledge that has been corrected by your experts, deduplicated, checked for freshness, scoped to the right permissions, and logged for auditability.
That distinction matters. There is a meaningful difference between AI that can act and AI that can act correctly.
Slack: Where you’ll see it first
Today also marks an important milestone for Slack with the general availability of their Real-Time Search API and MCP Server. Slack is increasingly becoming the surface where humans and AI agents collaborate in real time.
We’re proud to be a launch partner for this moment.
Slack provides the collaboration environment. Guru ensures the agents operating within it are grounded in verified, governed knowledge.
Consider a product update that ships on Tuesday. By Wednesday morning, your customer success channel in Slack can already contain a verified summary of what changed, what customers need to know, and links to updated documentation — all scoped appropriately to the teams involved. No one has to search. No one has to manually draft the update. The right knowledge is already present because your source of truth distributed it.
Slack is where many teams will feel this first. But this capability extends far beyond a single collaboration tool. The same verified knowledge can flow into your CRM, your help center, your work management systems, and any AI agent operating across your stack. Instead of splintered context, you get consistency. Instead of competing answers, you get alignment.
What this means for leaders navigating the agent era
If you lead IT or AI initiatives, you are being asked to move quickly while maintaining security, compliance, and control.
You do not want governance rebuilt inside every AI tool. You do not want multiple retrieval systems to audit independently. And you cannot afford different agents operating from different versions of the truth.
What you need is a policy-enforced knowledge layer that every system and every agent can inherit. One correction. One audit trail. One governed foundation.
Guru is the AI Source of Truth — the governed knowledge layer between your company’s data and every AI tool that acts on it. Instead of managing AI risk tool by tool, you govern knowledge once and allow every connected system to operate from that verified base.
The companies that succeed in the agent era will not simply deploy more AI. They will ensure that every AI system operates from trusted, permission-aware, continuously improving knowledge.
Your source of truth should not sit idle, waiting to be queried. It should actively support the systems that power your business — safely, consistently, and at scale.
Make your AI Source of Truth active
Your source of truth shouldn’t sit idle, waiting to be queried. It should actively support the systems that power your business — safely, consistently, and at scale.
If you’re evaluating how to deploy AI agents responsibly across your organization, we’d welcome the conversation.
