Use Cases

Real agents for real work

Detailed blueprints for deploying AI agents across your organization. Each use case includes setup effort, step-by-step agent flow, and ROI data.

Customer Operations

Order Management & Support

Resolve customer issues in seconds, not hours. The agent connects directly to your OMS and CRM to take real action — not just surface FAQs.

Deployment contextEmbedded in your customer support portal or help center
Setup effort~2 hours with existing OMS and CRM APIs
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What the agent does

1Customer initiates

Customer opens the widget and describes their issue: "My order hasn't arrived and I need it by Friday."

2Agent queries OMS

Agent calls your Order Management System API, fetches the shipment record, carrier status, and estimated delivery date.

3Agent evaluates options

If delivery is delayed beyond SLA, agent checks your policy rules and determines eligibility for replacement or refund.

4Agent takes action

With customer confirmation (or automatically based on policy), agent initiates replacement order, logs the interaction in CRM, and sends email confirmation.

ROI Impact

Support teams using AI agents for order management report 40–60% reduction in first-response time, 30% drop in escalations to human agents, and measurable improvement in CSAT scores. The agent handles the full resolution loop — humans focus only on edge cases.

Internal Productivity

Sales Call Prep & Intelligence

Walk into every discovery call fully briefed. The agent aggregates CRM history, recent emails, company news, and deal context into a concise one-pager.

Deployment contextTeam workspace with CRM and email integrations
Setup effort~3 hours with CRM + email integration via MCP
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What the agent does

1Rep triggers prep

Sales rep opens the team workspace and asks the agent to prep for their upcoming discovery call — or triggers it automatically via Slack or the Task API.

2Agent aggregates context

Agent reads CRM deal history, open tasks, email threads, previous call notes, and company-level account data.

3Agent generates brief

Produces a structured one-page brief: key stakeholders, deal timeline, open questions, competitive risks, and recommended talk tracks.

4Delivery to rep

Brief delivered to Slack DM or email before the call starts. Rep enters the call with context that previously took 45+ minutes to compile.

ROI Impact

Sales reps save 45–90 minutes of manual prep per week. More importantly, they enter calls better prepared — leading to higher close rates on enterprise deals and faster qualification of poor-fit leads. ROI compounds with deal size.

Automated Monitoring

Scheduled Metric Alerts

Replace dashboard fatigue with intelligent monitoring. The agent watches your metrics on a schedule and only alerts when human action is actually needed.

Deployment contextAutomated cron pipeline, alerts routed to Slack or PagerDuty
Setup effort~1 hour with your existing metrics API or data warehouse
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What the agent does

1Cron triggers agent

Task API is called on a schedule (e.g., every hour) with monitoring instructions and metric definitions.

2Agent queries metrics

Agent calls your metrics APIs, database queries, or analytics dashboards to fetch current values.

3Anomaly detection

Agent compares values against configured thresholds and recent baselines. Generates natural-language summary with severity assessment.

4Routed alert (or silence)

If threshold is exceeded: routes alert to Slack/PagerDuty with full context. If normal: logs silently. Ops team only gets paged when it matters.

ROI Impact

Teams report 70%+ reduction in false-positive alert noise and a significant drop in mean-time-to-detection for real incidents. Engineers reclaim hours per week previously spent watching dashboards. The agent's natural-language summaries also dramatically reduce time-to-understand when incidents do occur.

Customer Success

Churn Risk Detection

Stop losing accounts you could have saved. The agent monitors usage patterns weekly and surfaces at-risk customers before they churn.

Deployment contextTeam workspace with automated weekly reports
Setup effort~4 hours with product database + CRM access via MCP
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What the agent does

1Weekly agent run

Agent triggered every Monday via Task API cron with instructions to scan all active accounts.

2Multi-signal data pull

Reads product usage data (login frequency, feature adoption), support ticket history, NPS scores, and contract renewal dates.

3Risk scoring

Agent scores each account by churn risk using pattern matching against historical churn signals. Flags high-risk accounts with specific reasons.

4CS team report

Outputs a ranked list of at-risk accounts with recommended outreach actions: check-in call, feature training, executive sponsor outreach, or escalation.

ROI Impact

Early detection enables proactive outreach that saves 15–25% of at-risk ARR each quarter. CS teams shift from reactive firefighting to systematic retention programs. The agent's recommended actions also standardize the CS playbook across the team. CS managers can also open the workspace anytime for ad-hoc questions about specific accounts.

Build your own use case

Any workflow that calls APIs, processes information, and needs to take action is a candidate for a Sigmic AI agent.

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