We built a working system for Rhema AI where a team of agents helps manage sales, analyse calls, prepare content and research the market. The founders direct the work through one interface and make decisions using the material the agents prepare.
Explore the system
View full screenWe needed one system for records, finances, income and expenses, leads and the quality of customer messages. The same workspace needed to support content scripts and plans, call summaries, conversation analysis and draft proposals. The goal was to delegate recurring work to agents while seeing what runs automatically, what fails and what needs a human decision.
We developed our internal JARVIS project into Rhema OS, a business operating system with agents assigned to specific functions. It brings together a command centre, CRM, call analysis, content preparation and an activity log. Agent roles range from coordinating work and AI architecture to business analysis and content. People review the results and direct what happens next.
The founder sets the goal. One helper assigns tasks, another connects the working tools, and others prepare sales notes, documents or publications.
The team can see what is being done and review prepared material before deciding on the next step.
Two details from the same workspace, shown at a readable scale.
The founder sets the direction. The diagram shows how the helpers are connected and which role coordinates the work.
The conversation sits beside the team view, so people can discuss the next step within the same workspace.
From the first enquiry to a call review, content draft or agent activity log.
An overview of leads, tasks and business figures. The agent map shows roles and their connections: founder, coordinating agent, AI architect and agents for individual functions. The same workspace provides a place to assign a task and check its status.
The customer records system (CRM) shows each enquiry and its current stage. Call analysis accepts text or audio to help identify the client context and prepare material for the next step. A separate view brings together call reviews and recommendations for improving the sales conversation.
Planned contacts, company context and draft messages appear in one list. A team member reviews the text, uses it in the conversation and records the outcome: message sent, connection accepted or reply received.
A workspace for topics, observations about content in the market and publication drafts. An idea can become a draft carousel for review, approval or rejection. Content preparation and editorial decisions are part of the same visible process.
Agents help review company websites and gather observations about processes that could benefit from automation. Findings are grouped by industry, business task and possible solution. The team checks this material before deciding where to focus its work.
The activity log displays completed tasks, errors, the amount of text processed and running costs. History retains call reviews and business analyses. The team can revisit a result, understand what happened and identify where human review is needed.
The system receives working material: a lead, a call recording, a company to research or a content topic. The team defines the goal and chooses the relevant workflow.
Agents work within their assigned roles to analyse material, gather information and prepare findings and drafts. The result appears in the relevant section of the system.
A team member checks the prepared material, makes the decision and records the action or status. The activity log and history make it possible to revisit the work and understand the result.
Rhema OS is our own internal product. This case shows how we connect AI agents with a team’s working processes. For another business, the scope starts with its own tasks: choose one process, test it in practice and develop the system from there.
Project you are exploring: Rhema OS / JARVIS
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