Lessons from the Field: AI-Powered Export Control Transformation at a Manufacturer and a Trading Company

Hello, this is Hamamoto from TIMEWELL. Today I want to share the experience of two companies that have implemented TRAFEED, and what AI-powered export control transformation has actually looked like for them.

"The features sound good, but does it actually deliver results?" "Will it work for a company in our industry?" "Is implementation difficult?"

These are questions many people have. This article takes a detailed look at implementation cases from two different industries — manufacturing and trading — to give you a concrete picture of what is possible.

Case Study 1: Precision Equipment Manufacturer A

Company Overview and Pre-Implementation Challenges

Company A is a precision equipment manufacturer with approximately 600 employees, producing and selling measurement instruments and inspection equipment. Building on strong domestic market recognition, the company had been accelerating its international expansion in recent years, extending its reach across Asia, Europe, and North America.

Pre-implementation situation:

Item	Figure
Total counterparties	Approximately 3,000
New counterparties per month	20–30
Monthly screening man-hours	80+ hours
Staff assigned	2 (concurrent with other duties)

Table 1: Company A's pre-implementation situation

"Around the time overseas sales exceeded 30% of total revenue, the inadequacy of our export control framework started to become increasingly apparent," reflects the company's export control manager.

Three Core Challenges

Challenge 1: Managing a Large Counterparty Base

The company was exporting to more than 40 countries, with counterparties ranging from major manufacturers to small regional distributors. Two staff members were working full-time cross-referencing all of these parties against sanctions lists — and even that was not sufficient.

Challenge 2: Uncertainty About Accuracy

"Honestly, I wasn't confident we were catching all the concerning cases." Working from Excel on a manual basis, the team faced inherent limits when it came to name variations and abbreviations, and always carried the risk of missed detections.

Challenge 3: Knowledge Concentration in Export Classification

Export classification for products near the boundary of list controls had always been handled by a single veteran practitioner with years of experience. That person was approaching retirement, and if they left before their knowledge was transferred, classification accuracy was at risk of a significant decline.

How Implementation Came About

Company A learned about TRAFEED at an industry seminar and reached out for more information. A three-month pilot covering approximately 500 counterparties in a specific region was conducted.

The pilot results uncovered three cases of similar names that manual checking had failed to detect. None were ultimately confirmed to be the same party as a sanctioned entity, but the fact that "cases that could have been missed were found" was the deciding factor in moving forward.

Implementation Results

Results in numbers:

Metric	Before	After	Improvement
Screening man-hours	80 hrs/month	24 hrs/month	70% reduction
Screening frequency	Quarterly	Weekly	Major improvement
Newly detected concern cases	—	15 cases/year	—
Export classification time per case	60 minutes	20 minutes	67% reduction
Audit findings	2	0	—

Table 2: Company A's implementation results

Staff feedback: "I've been freed from routine tasks and can now concentrate on work that's actually meaningful. Being able to refer to AI determinations means I can make decisions with confidence."

For junior staff, seeing AI reasoning in action has become a learning opportunity in itself. Being able to check "why was this name flagged?" has been well received as a way to build export control knowledge on the job.

Case Study 2: Trading Company B

Company Overview and Pre-Implementation Challenges

Company B is a major trading company conducting business with more than 100 countries. With approximately 20,000 counterparties, new ones were being added on a daily basis.

Challenges specific to trading companies:

Not manufacturing products internally, making it harder to grasp the technical specifications of the goods being traded
An enormous counterparty base, creating extreme screening volume
Transactions spanning diverse countries and regions, each with their own regulatory requirements

Pre-Implementation Situation

"New counterparties come in every single day — ten companies a day, sometimes more than twenty. Just cross-referencing each one against multiple sanctions lists was taking up enormous time," recalls Yamada (a pseudonym) from the export control division, reflecting on those days.

The company had been managing counterparties in Excel for years, but could only search by exact name match — there was no way to handle name variations.

Keeping pace with sanctions list updates was also not happening. "Ideally, we wanted to re-check all counterparties every week. But that was physically impossible. Doing it quarterly, covering only the major counterparties, was the best we could manage."

What Prompted Implementation

The trigger was a finding from an external audit. The audit identified "insufficient screening frequency" and "inadequate handling of similar names" as issues, and the company was pushed to overhaul its approach.

A two-week free trial was conducted, covering approximately 2,000 of the company's counterparties.

"The results were a shock," says Yamada. "We found more than ten cases with similar names that our old approach hadn't caught. None of them turned out to be sanctioned parties, but the fact that we might have been missing things all along was genuinely alarming."

Implementation Results

Results in numbers:

Metric	Before	After	Improvement
Screening time	160 hrs/month	30 hrs/month	~80% reduction
Screening frequency	Annual	Weekly	Major improvement
Newly detected concern cases	—	~120 cases over 2 years	—
Audit findings	2	0	—

Table 3: Company B's implementation results

What had previously taken several days to fully screen 20,000 companies now completes in a matter of hours. Continuous monitoring made it possible to stay aware of sanctions list updates in near real time, and the improved framework was evaluated positively in audits.

Yamada's perspective: "Implementing TRAFEED was a turning point for us. The role of the export control team shifted from 'processing tasks' to 'managing risk.' That change has been enormous."

Common Threads Behind Success

Success Factors Both Companies Shared

1. Clear problem awareness

At both companies, the recognition that "the current approach is inadequate" was shared across the organization, including senior management. The initiative was framed not merely as "operational efficiency improvement" but as "reducing compliance risk" — which gave it the weight to move forward.

2. Piloting before full rollout

Rather than deploying organization-wide from day one, both companies ran a limited-scope pilot first, confirmed the results, and then expanded. This allowed them to move forward while keeping risk contained.

3. Establishing operating rules

Beyond introducing the tool itself, both companies set up operating rules: how to handle AI determinations, how to process cases at each risk level. Tool and process working together maximized the impact.

4. Ongoing improvement

After going live, both companies continued to regularly evaluate results and refine operations. Analyzing categories with high false-positive rates and cases where misses had occurred informed ongoing improvements.

Frequently Asked Questions

Q: How long does implementation take?

A: Initial configuration takes a few days. Depending on the condition of your counterparty data, the typical timeline from trial start to live operation is one to three months.

Q: Can TRAFEED integrate with existing systems?

A: Integration with major ERP and CRM platforms is supported. Please contact us for details on your specific environment.

Q: What internal resources are needed for implementation?

A: IT involvement can be kept to a minimum. The export control team takes the lead, with TIMEWELL's support team providing backup throughout the process.

Conclusion: Technology Transforming Export Control

The experiences at Company A and Company B demonstrate that AI technology can genuinely transform export control operations. Achieving both man-hour reduction and accuracy improvement simultaneously had been difficult under traditional approaches — TRAFEED's implementation made it a reality.

Of course, introducing a tool alone does not resolve everything. Behind both companies' success was management commitment, active engagement from practitioners on the ground, and a sustained commitment to continuous improvement. It was the combination of tool and human effort that produced results.

If you are interested in strengthening your export control framework, please reach out to us at TIMEWELL. We would be glad to propose solutions tailored to your organization's specific challenges.

References [1] METI, "Reference Example for Export Control Internal Compliance Programs," 2025 [2] JETRO, "Practical Guide to Export Control," 2025

If you are reviewing export-control operations or classification workflows, download the TRAFEED product catalog (PDF) or contact us.

Lessons from the Field: AI-Powered Export Control Transformation at a Manufacturer and a Trading Company