AI Operations Infrastructure

AI BPO & Call Center Sales Systems

For BPO teams, call centers, appointment setters, and support-led sales teams that need AI-assisted queues, approval workflows, QA context, escalation visibility, and cleaner handoffs.

Best for teams handling inbound chats, calls, tickets, DMs, follow-ups, appointment setting, or AI-assisted support and sales work.

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AI ops proof

JD SYS

Sagad OS command center showing queue health, SLA risk, approval load, pod coverage, and channel readiness.

Attention queue

Sagad OS conversation review showing draft reply, AI decision trail, knowledge context, and QA compliance gate.

Review surface

01 / Sagad OS

02 / AI draft review

03 / Human-in-the-loop approvals

04 / Queue and SLA visibility

Sagad OS preview proof for AI-assisted queues, approvals, QA context, and handoff work.

Proof In This Category

Portfolio systems that match this operating pattern.

These examples show the business problem, the system path, and the proof available to inspect.

Proprietary Build / v1 Preview

Sagad OS

Work-in-progress AI sales operating layer for queue visibility, AI draft review, HITL approvals, knowledge context, and human handoff workflows.

Built

Next.js v1 console with typed home-services mock data, command center, attention queue, conversation review, KB/SOP/QA context, and Agent Studio backend direction.

Proof

Preview screenshots show the command center, attention queue, conversation review, mobile views, AI decision trail, and approval gate.

AI sales OSLangGraphLangChainLangSmithChatwoot

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What Breaks

The problem is not only demand. It is what happens after the work starts moving.

AI drafts, human replies, and customer context are hard to inspect in one place.

Escalations happen, but the reason and owner are not always visible.

QA rules, SOPs, and policy context sit outside the actual conversation flow.

Managers see queue risk too late because work is spread across inboxes, sheets, and CRM notes.

What I Build

A business system around the way this industry actually sells, books, fulfills, and reports.

AI-assisted queue workspace

Draft review and approval layer

QA/SOP knowledge context panel

Escalation and human takeover path

Agent orchestration backend boundary

Trace and audit visibility

Outcomes

What gets clearer after the system is built.

Clearer view of what AI drafted and why

Safer human approval before high-trust actions

Faster inspection of SLA risk and stuck conversations

Cleaner handoff between AI, agents, team leads, and CRM work

Portfolio Proof

Inspect the systems before booking a review.

Start with proof across CRM workflows, automation handoffs, dashboards, commerce operations, and business-facing apps before adding more tools.

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