Fixed-scope AI + automation packages
Clear deliverables, defined boundaries, and outcomes you can measure. No “we’ll figure it out as we go” fog.
Ops, Support Ops, Sales Ops, Finance Ops
Package A — Human-in-the-loop AI Agent Setup
Handles structured work, follows rules, and pauses for approval when the step is sensitive or ambiguous.
Typical use cases:
- • Intake triage + routing
- • Summarizing threads and tickets (without losing the important bits)
- • Collecting missing info from requesters
- • SOP checks before handoff
Deliverables
- + Rules + boundaries (what it can’t do matters as much as what it can)
- + Knowledge inputs (SOPs, docs, allowed sources)
- + Approval flow (who signs off, when, and on what)
- + Traceability (logs + decision notes)
- + Basic monitoring + handoff docs
What “done” means
- ✓ Baseline metrics captured
- ✓ Agent running in production with approvals
- ✓ Manual steps reduced in a way you can measure
What you provide
- → Access to relevant tools + sample data
- → SOPs (messy is fine; messy is normal)
- → Someone available to approve edge cases during rollout
Teams drowning in cross-tool busywork
Package B — Automation + Integration (AI-assisted)
Connects systems so work moves without copy/paste and without a human acting like the glue.
Typical use cases:
- • Forms → ticket creation → assignment → notifications
- • CRM updates triggered by support or ops events
- • Approvals + status sync across tools
- • Exception handling for the weird cases (because there are always weird cases)
Deliverables
- + Workflow map (current vs improved)
- + Integrations (API/webhooks or native connectors)
- + Approvals on critical actions
- + Error handling + alerts
- + Monitoring checklist + documentation
What “done” means
- ✓ Workflow runs end-to-end reliably
- ✓ Failures are visible and recoverable
- ✓ Cycle time reduced and tracked
What you provide
- → Access to tools (or a sandbox)
- → A workflow owner who can validate the steps
- → Acceptance criteria for success
Recurring reporting that keeps stealing hours every week
Package C — Reporting / Insights Automation (AI augmentation)
Automates pulls, locks metric definitions, and produces consistent outputs your stakeholders stop arguing about.
Typical use cases:
- • Weekly ops review pack
- • Support performance summary
- • Finance rollups + variance notes
- • “What changed this week” narrative updates
Deliverables
- + Automated data pulls + schedules
- + Metric definitions (so numbers don’t drift month to month)
- + Output formats (dashboards, docs, email)
- + Sanity checks (data QA rules)
- + AI-generated narrative summaries with controls
What “done” means
- ✓ Reporting time drops, materially
- ✓ Output is consistent each week
- ✓ Stakeholders trust the numbers
What you provide
- → Data sources + access
- → The “must-have” metrics list
- → A reviewer during the first few runs
Standard Across All Packages
Baseline + post-launch measurement
Approvals for critical actions
Traceability/audit approach
Documentation + handoff
Not Sure Which Package Fits?
Take the readiness assessment and we’ll point you to the cleanest starting place.