CASE STUDY
Air Emissions Compliance
SOLUTION
AirLog + Genny AI
~5 min
80
12
Executive Outcome Summary
Supporting Complex Manufacturing Operations with Environmental Accountability
For 65 years, Atmus Filtration has manufactured high-performance filtration solutions supporting heavy-duty diesel automotive applications, including semi-trucks, mining equipment, tractors, and industrial machinery. Its Neillsville facility in central Wisconsin reflects the operational complexity common across advanced manufacturing environments. The approximately 155,000-square-foot site operates three shifts five to six days per week and produces seven million different parts, including air filters and housings.
The facility maintains ISO certifications for health, safety, and environmental management and is also IATF certified—reinforcing expectations around operational discipline, documentation, traceability, and environmental stewardship. Air emissions management represents a significant compliance responsibility at the site, driven by the diversity of manufacturing activities occurring within the facility:
Each activity contributes to an emissions profile that must be monitored, documented, and reported in accordance with permit requirements across multiple recurring reporting cycles.
Compliance Context
Managing Air Emissions Compliance Across 80 Materials and 17 Processes
The environmental compliance challenge at Neillsville was not understanding regulatory obligations. The challenge was managing operational complexity at scale. The facility needed to track air emissions from approximately 80 production materials used across 17 manufacturing processes while supporting recurring monthly reporting requirements.
Generating those records required information from multiple operational sources. Production teams supplied production records. Floor-level inspections determined material consumption from tanks and totes. Maintenance records provided visibility into inventory withdrawals. The environmental team also relied on safety data sheets, technical data sheets, process testing documentation, and records used to support annual and biannual emissions reporting.
Bringing those inputs together every month required significant coordination and validation. The challenge was not simply collecting information—it was maintaining confidence in calculations, supporting audit readiness, and ensuring reporting obligations could be met consistently across a highly flexible manufacturing environment.
Prior to using the AI-assisted workflow, emissions calculations and supporting records were managed through a combination of 12 spreadsheets and Microsoft Access.
Lance Wilson, HSE Engineer — Atmus Filtration
Exploring AI as a Practical Compliance Workflow Accelerator
Atmus worked with the Benchmark Gensuite team to evaluate how the Permit Agent within Genny AI Agent Hub could support a specific environmental compliance task: transforming permit requirements into structured data suitable for emissions management system setup.
Rather than pursuing AI for broad automation objectives, the effort focused on a practical operational challenge—how to reduce the manual effort required to translate permit language into usable reporting structures. The collaboration centered on connecting three activities that are traditionally performed as separate, sequential steps:
The objective was not to eliminate human review, but to reduce administrative effort while preserving environmental oversight and accountability throughout the setup process.
Connecting Permit Analysis with AirLog Configuration Through Genny AI Agent Hub
1
Permit Upload
The environmental user uploads a permit document into the Permit Agent within Genny AI Agent Hub. No custom formatting or coding is required before submission, allowing the workflow to fit within existing environmental compliance processes.
2
AI-Assisted Source Identification
The agent reviews permit content and identifies air emission sources, processes, and associated reporting parameters. This reduces the amount of manual permit interpretation required before setup activities can begin.
3
Structured Output Generation
The system returns a structured list of sources and parameters that can support AirLog configuration activities, creating a more consistent starting point for emissions program setup.
“One shot took about five minutes, and here’s the list of your processes. Here’s the parameters that you’re going to need to be reporting every month.”
Lance Wilson — Atmus Filtration
4
Human Review and Validation
Users review the generated output and make edits as needed before finalization. This allows environmental professionals to maintain oversight while improving the usability of source names and reporting structures for future program owners. Permit terminology is not always the most practical language for day-to-day operations—a permit may identify a source as “P01,” while the team may prefer “P01-paintline” to make the record more meaningful for future users.
“The other nice thing—that table can be edited. So P01, maybe that’s not the best name for that process. It’s actually a paint line. So we can do P01-paintline because the next person after me, they might not know what P01 is.”
Lance Wilson — Atmus Filtration
5
AirLog Integration
Validated information can then be ingested into AirLog for emissions tracking and reporting workflows. The workflow also includes checks that notify users when a source already exists, helping prevent duplicate setup and supporting data consistency across the emissions management program.
Faster Setup Readiness, Less Manual Translation, and Improved Program Continuity
Atmus’ evaluation demonstrated how AI-assisted permit analysis can support environmental compliance workflows without removing human review from the process. Using a 41-page Title V permit, the Permit Agent generated a structured list of air emission processes and associated reporting parameters in approximately five minutes.
41-page Title V permit processed through Genny AI Agent Hub
Source and parameter list generated in approximately five minutes
No coding or custom development required from the user
Outputs remained editable prior to AirLog ingestion
Source naming adaptable to improve operational usability and continuity
Manual permit interpretation effort reduced during initial setup activities
This evaluation did not produce enterprise-wide deployment metrics, ROI calculations, or audit outcome data. Outcomes reflect the scope of the initial workflow evaluation using a single Title V permit document.
Evolving from Manual Permit Translation to Structured Setup Preparation
| BEFORE | AFTER |
|---|---|
| Air emissions calculations supported by 12 spreadsheets and Microsoft Access | Structured source and parameter outputs generated through Genny AI Agent Hub |
| Manual interpretation of permit requirements before setup could begin | AI-assisted identification of sources and reporting parameters from permit content |
| Permit setup activities dependent on individual expertise and institutional knowledge | Structured outputs available for review, refinement, and ingestion by any team member |
| Permit terminology may not align with operational naming conventions | Editable source naming prior to AirLog setup supports operational clarity and continuity |
| Fragmented documentation assembled from multiple sources during setup preparation | Direct pathway from permit analysis toward AirLog configuration and centralized management |
| Manual formatting and preparation effort required before system ingestion | No-code extraction workflow reduces pre-setup administrative burden |
Operational Impact
Lance Wilson, Atmus Filtration
Practical AI Adoption Starts with High-Friction Compliance Tasks
Atmus’ experience highlights several lessons relevant to environmental professionals evaluating AI-enabled workflows for emissions compliance programs.
Turning Permit Requirements into Operationally Usable Data
Environmental professionals across manufacturing industries face growing expectations around reporting accuracy, documentation quality, traceability, and audit readiness. At the same time, many organizations continue to rely on manual processes to translate permit requirements into operational tracking systems—a dependency that concentrates institutional knowledge, creates fragmentation across tools, and adds administrative burden to already complex compliance programs.
The Atmus example illustrates a practical application of AI within environmental compliance. Rather than treating permits as static documents that must be manually translated into operational systems, AI-assisted workflows can help transform permit content into structured starting points that support emissions management, reporting preparation, and long-term program continuity.
As environmental programs become increasingly data-intensive, the ability to move efficiently from regulatory requirements to operational execution is becoming a more important component of environmental governance maturity. The transition from fragmented manual interpretation to structured, system-ready compliance data represents a meaningful operational improvement for facilities managing complex emissions profiles across multiple processes and reporting obligations.
ORGANIZATION
Founded
65+ years ago
Facility Size
~155,000 sq ft
Operations
3 shifts, 5–6 days/week
Certifications
ISO EHS, IATF
Industry
Filtration Manufacturing
Solutions Used
AirLog
Genny AI Agent Hub
Permit Agent
TOPICS
Air Emissions Compliance
Title V Permit
Permit Management
AI-Assisted Workflows
EHS Compliance
Audit Readiness
Knowledge Transfer
Environmental Governance
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