Company Profile
Company Type
Precision parts manufacturer
Team Size
100-500 employees
Industry
Manufacturing
Key Challenge
Struggling with inefficient manual follow-up processes that were slowing growth and increasing operational costs. Their primary concern was supply chain visibility.
Tools Connected
The Challenge
Manual follow-up was the biggest bottleneck in this precision parts manufacturer's operations. Their team of 100-500 employees processed hundreds of follow-up requests weekly, each requiring multiple steps, cross-referencing against manufacturing-specific requirements, and coordination between departments. The average follow-up request took 45 minutes to complete manually, and the backlog was growing by 15% each quarter.
Beyond the time drain, the quality of their follow-up output was inconsistent. Different team members followed different procedures, and there was no standardized way to handle edge cases that are common in manufacturing. A recent audit revealed that 12% of completed follow-up records contained errors that required rework — costing the organization an additional $50K annually in correction and remediation efforts. The leadership team recognized that continuing to throw people at the problem wasn't viable and began searching for an AI-powered solution.
The Solution
Arahi AI provided the automation backbone this manufacturing team needed. They deployed a multi-agent workflow that breaks the follow-up process into discrete, automated steps — each handled by a specialized AI agent. The first agent monitors triggers from SAP and Google Sheets. The second agent analyzes and processes incoming requests using manufacturing-specific business logic. The third agent executes actions across connected tools and notifies team members via Monday.com.
The beauty of the no-code approach was speed of implementation. The team had their first agent live within 90 minutes, and the full follow-up workflow was operational within a single afternoon. They used Arahi AI's template for manufacturing follow-up as a starting point, customized the business rules to match their specific process, and connected their existing tool stack without writing a single line of code. Within the first week, the agents had processed over 200 follow-up instances with 99%+ accuracy — more than the team typically handled in a month.
The Results
Measurable improvements across key manufacturing follow-up metrics.
Task Completion Time
97% faster
Before
2-3 hours average
After
< 5 minutes
Team Productivity
250% increase
Before
Baseline
After
3.5x output
Quality Score
26% improvement
Before
78% accuracy
After
98.5% accuracy
Monthly Cost
85% savings
Before
$8,200/month
After
$1,200/month
Customer Satisfaction
35% increase
Before
3.4/5
After
4.6/5
“The difference is night and day. Our manufacturing clients used to wait days for follow-up to be completed. Now it happens in minutes, and the quality is consistently higher than what we achieved manually. Customer satisfaction scores went through the roof.”
VP of Customer Success
Precision parts manufacturer
Key Takeaways
The most important lessons from this manufacturing follow-up automation project.
This manufacturing team proved that follow-up automation doesn't require technical expertise — the no-code platform made it accessible to business users.
Scaling follow-up capacity by 10x without adding headcount fundamentally changed the economics of their manufacturing operations.
Consistent AI-powered processing eliminated the quality variance that came with different team members handling follow-up differently.
Real-time visibility into follow-up metrics gave leadership the data they needed to make better strategic decisions.
Implementation Timeline
From zero to production in Half a day — here's how they did it.
Step 1: Mapped the existing follow-up workflow
Documented every step of the current manual follow-up process, including decision points, exceptions, and handoffs between team members. Identified which steps could be fully automated versus those needing human oversight.
Step 2: Built the automation in Arahi AI
Used Arahi AI's no-code builder to create the follow-up workflow: connected SAP and Slack as data sources, configured AI decision logic for manufacturing-specific requirements, and set up automated actions and notifications.
Step 3: Parallel run with manual process
Ran the AI agents alongside the manual process for one week to compare outputs. The AI matched or exceeded human accuracy on 98% of follow-up instances, with the 2% of edge cases automatically flagged for human review.
Setup Time
Half a day
AI Agents
4 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating follow-up in manufacturing.
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