AIfortheKhozyayushkadry-cleaningchain
16 people in operations route leads and check quality by hand — some requests burn out unanswered, defects and complaints surface after the fact, and every new location needs new people.
In brief
AI for a franchise dry-cleaning chain (54 locations) — auto-leads, quality control, a custom CRM, support. The case explains the original process, implementation stages, available public outcomes, solution limits and the questions another company should verify with its own data before a pilot.
Key takeaways
- 16 → 3 — people in operations
- 100% — of leads handled
- seconds — to answer a lead
The Khozyayushka franchise dry-cleaning chain — 54 locations. Operations routed leads and checked quality by hand: some requests burned out, defects surfaced too late. We shipped auto-leads, automated quality control, a custom CRM built for the network and support agents — operations shrank from 16 to 3 people.
Architecture and process
The solution is presented as connected operating loops: intake, routing, quality control, CRM and support. Architecture separates an automated signal, an employee task and verified completion. State belongs in the operating system; the model is not the sole source of truth.
Public evidence boundary
The public page contains only the first-party facts approved for disclosure. Private architecture, personal data, contracts and internal logs are intentionally excluded.
What to validate before reuse
Another network should revalidate lead channels, assignment rules, quality sources, access rights and the cost of human exceptions. Public outcomes from this case are not a forecast; a new baseline, shadow mode and agreed rollback are required.
How we built it
Diagnostic
We mapped how the 54-location network routed leads and checked quality by hand. We locked the outcome metrics: lead response time and the share of handled leads.
Prototype on real data
We built the first loop of auto-leads and a custom CRM for the network — requests handled in seconds on the real flow of locations.
Baseline and scope
The team documents the current process, decision owner, permitted data and acceptance criteria. Public reporting separates observable facts from hypotheses; unsupported details are not used to justify an outcome.
Verification and operating handoff
The team compares accepted outcomes with evidence and documents limitations, access, monitoring and fallback. The process owner accepts the system only after a real-scenario check. Version changes trigger reassessment; an unverified effect never becomes a public promise.
What came out
A similar process? Let us assess the impact first
We will review the task, data and metric. If a pilot is unnecessary or AI is the wrong fit, we will say so before any work starts.
Limitations and risks
- The outcomes belong to this specific project and do not guarantee the same effect in another company.
- The public version does not disclose confidential data, personal information or private infrastructure details.
Questions and answers
What problem did the Khozyayushka project address?
The Khozyayushka franchise dry-cleaning chain — 54 locations. Operations routed leads and checked quality by hand: some requests burned out, defects surfaced too late. We shipped auto-leads, automated quality control, a custom CRM built for the network and support agents — operations shrank from 16 to 3 people. Public metrics from this project are not a guarantee for another organization.
How did Aiconic structure the work?
Diagnostic: We mapped how the 54-location network routed leads and checked quality by hand. We locked the outcome metrics: lead response time and the share of handled leads. Prototype on real data: We built the first loop of auto-leads and a custom CRM for the network — requests handled in seconds on the real flow of locations. Production & support: We scaled to 100% lead handling and automated quality control, added support and communication agents; operations shrank from 16 to 3 people. Public metrics from this project are not a guarantee for another organization.
Can another company expect the same result?
The outcomes belong to this specific project and do not guarantee the same effect in another company. The public version does not disclose confidential data, personal information or private infrastructure details. Public metrics from this project are not a guarantee for another organization.
Sources and evidence
- AI Risk Management Framework
Lifecycle AI risk management.
- Agentic AI Threats and Mitigations
Tool, permission and autonomous-action risks.
Aiconic Editorial Team · This material was prepared with AI tools in an Aiconic editorial session. Facts and wording are limited to published project data.
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