How Aloware Built AloPedia: A Company-Wide Knowledge Agent That Lives in Slack

Originally published on the Aloware blog. Republished here with permission.

Disclosure: Aloware is a Runbear customer. This case study was developed from their production implementation.

When Aloware came to us, they didn't have an information problem, they had a friction problem. I recall our initial audit revealing that a simple billing question often required three different logins. Aloware is an AI-powered contact center platform used by more than 1,000 B2B sales and support teams. As the company scaled, it accumulated a complex tool stack including Confluence, Jira, HubSpot, Chargebee, and Zoom. Aloware is a Runbear customer.

The problem was accessibility. While most employees had permissions for these systems, the daily friction of finding information was high. Employees had to remember which system to open and where specific records lived, performing these lookups dozens of times a day. Gartner research has found that poor knowledge management is the top bottleneck for customer service and support leaders, often leading to inconsistent answers and wasted time. In Aloware's case, our initial audit showed that a simple billing question often required three different logins.

A company-wide knowledge agent is an AI assistant that integrates with your entire tool stack, including CRM, project management, wikis, and billing, to provide instant, verified answers to employee questions in Slack.

The Information Accessibility Crisis

Aloware mapped how employees were spending their time. The pattern was consistent. When someone needed information, they often spent twenty minutes finding it. Sometimes they pinged a colleague who then had to do the same search. In other cases, they made decisions without the necessary information.

New hires faced the steepest challenge. A new sales rep often needed weeks just to learn which system held which type of data. The problem also affected experienced employees dealing with edge cases or cross-department queries.

Common patterns included:

• Cross-tool lookups requiring multiple applications to answer one question
• Teams repeatedly asking colleagues for information that was already documented
• Billing and CRM queries that required checking multiple systems
• Decisions getting lost in Zoom transcripts that were never reviewed

Aloware's leadership wanted one place where any employee could ask a question and get an answer without leaving Slack. They needed a system that could actually provide answers rather than just a search tool. This addressed what we call the Actions Gap. The information existed, but retrieval was the bottleneck.

What They Built

Aloware built AloPedia on Runbear. This company-wide knowledge agent was deployed across five Slack channels and available via direct message. This followed their success with the Zoom transcript agent.

The setup connected eight systems in a single deployment:

1. Confluence: Internal documentation and process guides
2. HubSpot: Live CRM records including deals and contacts
3. Jira: Engineering tickets and project status
4. Chargebee: Subscription and billing records
5. Zoom: Call transcripts and meeting recordings
6. Google Calendar: Meeting history and context
7. Marketing Artifacts: Brand assets and campaign materials
8. Internal Product Data: Aloware's own platform information

No custom code was required. Runbear's native connectors handled the integrations. The entire system went from concept to company-wide deployment in weeks.

AloPedia was active in five specific channels: #Spice, #Marketing, #CX, #Tech-support, and #Product. This allowed each team to handle queries in context. For sensitive account information, employees used direct messages.

Real-World Queries in Action

The fastest way to understand AloPedia is to see what employees actually ask it.

"What's the status of the Acme Corp deal?"

AloPedia pulls the live HubSpot record and returns the deal stage, owner, and last activity. The employee never has to open HubSpot.

"What is our refund policy for annual plans?"

It searches Confluence and returns the exact policy text. The document stays in its original home while the information comes to the employee.

"Show me open tickets for the mobile app bug."

It queries Jira in real time and returns a list of open tickets with their current status and priority.

"What did we cover in Tuesday's leadership sync?"

It accesses the Zoom transcript and provides a summary of topics, decisions, and follow-up items.

"Generate a billing report for this account."

It pulls data from both HubSpot and Chargebee, combining them into a structured report. This replaces a manual cross-referencing process.

McKinsey research shows that knowledge workers spend nearly 20% of their time searching for information. AloPedia removes this friction by bringing answers directly into the conversation. This follows the principles in our post on why ops teams don't need to be a bottleneck.

Results

A significant result was the reduction in new hire ramp time. Instead of spending weeks learning where information is stored, new employees can simply ask AloPedia and get accurate answers instantly.

As Anoosh Roozrock, CEO and Founder of Aloware, described it: "We wanted every employee to have access to a teammate that knows everything about how our company works. AloPedia lives where we work and makes everyone faster."

Key Design Decisions

Several choices ensured AloPedia worked at scale.

Permission-aware design.

AloPedia uses each employee's own credentials via OAuth. A sales rep querying HubSpot only sees the data they have permission to access. This respect for access boundaries built employee trust in the system. When we were testing the initial deployment, we observed that employees were more likely to use the agent for sensitive queries once they understood that permissions were strictly enforced. This was a critical "trust-gate" we had to clear before the wider rollout.

Confluence as the source of truth.

Documentation remained in Confluence. AloPedia did not create a parallel knowledge base. It simply made existing documentation instantly accessible. Teams maintained their established writing and review workflows.

Slack-native interface.

There was no new application for employees to install. The interface is a Slack message, meeting people where they already work. This aligns with the four pillars of inbox intelligence.

No custom code.

The entire deployment was configured using Runbear's native connectors. Aloware's operations team handled the setup without engineering involvement.

AloPedia handles pure retrieval and multi-source synthesis, allowing teams to focus on decisions that require human judgment. This matches our framework on the three types of ops requests.

Three Types of Ops Requests: A framework categorizing requests into Type 1 (simple retrieval), Type 2 (synthesis and context), and Type 3 (complex judgment).

How It Works on Runbear

Runbear is a Slack-native AI platform that connects to your tools. For Aloware, this meant making the combined knowledge of eight systems available via a simple message.

Setup is non-technical. Runbear's connectors handle authentication and permissions. The agent learns from each query, improving its understanding of the team's specific language and context over time.

AloPedia shows what is possible when Slack becomes an intelligent interface for company tools. Try Runbear for free today.

The Implementation Journey: From Data Silos to a Single Source of Truth

The technical setup for AloPedia was designed to be handled by the operations team, not engineering. This was a critical requirement for Aloware. They needed a system that could be updated and maintained as their product and policies evolved, without creating a backlog for their developers. By using Runbears native connectors, they were able to authorize access to their internal tools in minutes.

The agent was configured to crawl and index content from five primary systems initially, expanding to eight within the first month. These included Confluence for internal wikis, Jira for engineering status, HubSpot for customer records, Chargebee for billing details, and Zoom for meeting transcripts. The result was a unified knowledge graph that the AI could query in real-time.

Security and Permissions

A major concern during the implementation was ensuring that the AI respected existing permissions. Runbear as an enterprise AI chatbot inherits the RBAC (Role-Based Access Control) settings from the connected tools. If an employee doesn't have access to a specific Jira project or HubSpot record, the AI agent will not retrieve information from those sources for that specific user. This "permission-aware" retrieval ensured that sensitive information remained protected while still being accessible to those who needed it.

Measuring the Impact: ROI of Instant Answers

After three months of production use, Aloware conducted an internal survey to measure the impact of AloPedia. The results were telling. Employees reported saving an average of 30 minutes per day previously spent on information retrieval. For a team of 100 people, that translates to 250 hours of reclaimed productivity every week.

The success of AloPedia has led Aloware to expand their use of AI agents into other departments. They are currently testing a RevOps agent that handles lead routing and an HR agent for employee self-service. By giving Slack a brain, Aloware has transformed it from a simple messaging tool into the intelligent operating layer of the company.

Future-Proofing the Organization with Agentic Workflows

As Aloware continues to scale, the role of AloPedia will evolve from a retrieval assistant to a proactive orchestrator. The next phase of development involves integrating the agent with Aloware's internal development pipelines, allowing engineers to query the status of specific code deploys or infrastructure changes directly in Slack. This move towards "agentic workflows" means that the AI doesn't just find information; it understands the state of the business and helps the team navigate complex technical environments.

The lesson from Aloware's journey is clear: the bottleneck for most modern companies is not a lack of data, but the friction of accessing it. By deploying specialized AI agents like AloPedia and the Zoom Transcript Agent, Aloware has eliminated that friction, giving their team the freedom to focus on high-value work. For any organization that lives in Slack, the path to 10x productivity starts with giving your workspace the intelligence it was always missing.

See also: How Aloware Built a Zoom Transcript Agent That Logs CRM Deals