Problem this solves
Teams are buried in repeatable monitoring, drafting, summarizing, routing, document review, and follow-up work that steals capacity from higher-value execution.
AI Agents
Build AI agents that increase capacity without replacing people.
LUMATARRA designs practical AI agents around real operating roles: executive briefing, revenue ops, finance, customer operations, documents, and Teams follow-up. They reduce repetitive work, protect focus, and keep humans in control.
What we build
Role-based AI agents connected to Microsoft 365, Teams, documents, data, workflows, approvals, logs, and human handoff rules.
What the client gets
More capacity, faster first drafts, better follow-up, less coordination drag, and AI support that stays inside clear guardrails.
Timeline
One useful agent workflow can be scoped quickly and built as a focused V1 when access, data, approvals, and owners are clear.
Microsoft stack
Microsoft 365, Teams, Power Automate, Azure, Copilot workflows, Entra ID, SharePoint/OneDrive, Fabric/Power BI where data is needed.
Agent use cases
Agents are role-based support systems, not novelty bots.
The useful question is not whether a chatbot can answer a question. It is which repeatable work is burning out the team, who approves the output, and how follow-through happens.
Executive Briefing Agent
Turns operating data, scorecards, meeting notes, and Teams context into concise leadership briefs with risks, decisions, and follow-ups.
Revenue Ops Agent
Keeps pipeline hygiene clean, flags stalled deals, drafts follow-up, and prepares the weekly revenue rhythm.
Finance / Controller Agent
Supports close cadence, AR/AP visibility, cash movement, variance notes, and financial scorecard preparation.
Customer Operations Agent
Watches onboarding, support patterns, open issues, renewal signals, and customer follow-up so operators see risk early.
Document Processing Agent
Extracts, summarizes, routes, and validates contracts, SOWs, invoices, forms, and intake packets.
Teams Workflow Agent
Moves AI from chat into Teams channels, approvals, reminders, meeting artifacts, and task follow-through.
How we build agents
Start with the strain on the team. Then give AI the right job, context, and controls.
01
Map the work
Define the role, trigger, inputs, outputs, approvals, and failure modes before anything is automated.
02
Connect the context
Give the agent governed access to the Microsoft, data, document, and workflow surfaces it needs.
03
Build with guardrails
Design tools, prompts, handoff rules, human approvals, logs, and escalation paths.
04
Operate and improve
Monitor quality, tune workflows, retire weak automations, and expand what proves ROI.
Pick one workflow where AI can carry the first draft, first pass, or follow-up.
We will map the role, data, approvals, risk, and ROI path before we build anything.