AI Mediation Support
The Problem
“Disputes are stuck in inboxes—teams lose days reconstructing facts and negotiating outcomes”
Organizations face these key challenges:
Tenant complaints and disputes arrive through multiple channels and become long, unsearchable email/ticket threads
Staff waste hours reconstructing timelines, finding photos/invoices, and interpreting lease/policy details per case
Resolution quality is inconsistent across properties/managers, driving perceived unfairness and escalations
Backlogs spike during peak periods (move-in/out, maintenance surges), increasing churn risk and legal exposure
Impact When Solved
The Shift
Human Does
- •Manually intake complaints from email/phone/walk-ins and create/merge tickets
- •Collect evidence (photos, invoices, vendor notes) and chase stakeholders for updates
- •Interpret lease clauses, policies, and local rules; negotiate outcomes ad-hoc
- •Write tenant communications and settlement summaries; escalate late to managers/legal
Automation
- •Basic ticketing automation (forms, templates, routing rules) in PMS/helpdesk tools
- •Keyword search and manual report exports from disparate systems
Human Does
- •Approve recommended resolutions/credits/next steps for high-impact or sensitive cases
- •Handle exceptions (legal threats, discrimination claims, safety incidents) and final negotiation
- •Provide policy guidance and update playbooks/thresholds based on outcomes
AI Handles
- •24/7 intake across channels, deduplication, and automatic case creation with structured fields
- •Entity extraction (unit, lease, resident, vendor), timeline reconstruction, and evidence bundling
- •Policy/lease-aware resolution suggestions (e.g., credits, scheduling, vendor dispatch, rule enforcement) with rationale
- •Severity triage, escalation prediction, and automatic routing to the right team/vendor with SLA tracking
Operating Intelligence
How AI Mediation Support runs once it is live
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not approve credits, concessions, charge reversals, or spend commitments without a property manager or authorized operations lead reviewing the recommendation. [S2]
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Real-World Use Cases
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