S&B Filters: From Prototype to Production AI in 4 Weeks

About the Client

S&B Filters is a U.S.-based manufacturer of high-performance air filters for the automotive aftermarket. With 700+ employees and a complex, multi-channel business, the company runs its core operations — order management, internal workflows, customer data — on NetSuite.

As the business scaled, so did the volume of customer inquiries — especially around order status, delivery timelines, and product details — putting increasing pressure on the support team.

The company attempted to solve this with AI — but the results fell short.

The Challenge

S&B's CEO, Berry Carter, is the kind of leader who builds before he buys. Rather than wait for a vendor pitch, he wired up Claude's MCP (Model Context Protocol) connector to NetSuite himself, wrote his own prompts, and stood up an internal AI assistant for order status.

For a CEO-built prototype, it worked. It proved the use case. It showed the team what was possible.

Then the limitations of an experiment-grade architecture surfaced:

The Solution: A Production AI Layer Purpose-Built for NetSuite

We didn't extend the prototype. We rebuilt the architecture from the ground up — a custom AI agent stack engineered for S&B's data, channels, and operational realities.

The result is not a chatbot. It's an AI layer on top of NetSuite that serves internal teams and external customers from a single foundation.

📦 AI for a High-Impact Use Case — Order Status (Powered by NetSuite)

We started with what delivers the most immediate value: instant access to NetSuite data and order status. The AI assistant now:

• integrates directly with ERP (NetSuite)
• retrieves order data instantly
• works across Shopify and phone orders
• handles both structured and highly unstructured inputs.

🖥️ Two Interfaces — One NetSuite AI Layer

To maximize impact, we deployed the solution across two key environments:

1. Internal AI Assistant (for support teams)

The most critical use case for operations:

• agents can instantly retrieve order data by simply entering an order or PO number
• eliminates manual navigation across NetSuite
• significantly reduces handling time and cognitive load

👉 What used to take minutes inside NetSuite now takes seconds.

2. Customer-Facing AI Assistant (website)

A self-service layer for end users:

• instant order status updates
• reduced need to contact support
• 24/7 availability.

🧠 AI as a Product Expert (Knowledge Base Layer)

We extended AI beyond transactions into product knowledge. The assistant now:

• answers installation and compatibility questions
• supports technical inquiries
• enriches responses with images, videos, and step-by-step guides.

This complements NetSuite by bridging the gap between data and customer understanding.

Previously, agents had to manually navigate the NetSuite interface to locate order details — a time-consuming and fragmented process. With the AI assistant, they can now simply enter an order or PO number and instantly retrieve all relevant information.

What used to take minutes of manual search is now reduced to seconds — significantly improving both response speed and agent efficiency.

What Made It Work

1. Making NetSuite Data Usable for AI

NetSuite is structured. Real-world inputs are not. We built a layer above the ERP that:

• Validates inputs across formats and data sources
• Falls back across identifiers (Sales Order → PO number → customer reference)
• Uses conversation context to infer intent

👉 This allows the system to consistently deliver accurate results — even when inputs vary across channels.

2. Extending NetSuite with a Dynamic Knowledge Layer

Product knowledge is continuously evolving — with new products, updated installation guides, and varying levels of access requirements. While NetSuite provides a robust foundation, enabling this level of flexibility and content control benefits from an additional, purpose-built layer.

We built a dynamic knowledge layer connected to — but not limited — NetSuite:

• OneDrive integration for easy content updates by the client
• structured document formats optimized for AI processing
• controlled visibility rules to separate internal vs customer-facing data
• instant updates without system redeployment.

👉 The AI always delivers accurate, up-to-date answers — without exposing sensitive internal information.

3. Speed as a Core Metric

The previous solution technically worked — but with 4–6 minute response times, it was unusable in real workflows. Support agents still had to manually search NetSuite, and customers experienced delays.

We optimized for instant access to NetSuite data, not just answer quality:

• fast, direct data retrieval from ERP
• minimal latency in AI responses
• real-time interaction design

👉 What used to take minutes now takes seconds — enabling both faster support and a seamless customer experience.

Technologies Used

Projected Business Impact

For S&B Filters, this is no longer a support project. It's the foundation for an AI-first operation:

• AI is embedded in core workflows, not bolted on the side
• A single architecture serves both internal teams and external customers
• The same platform is already extending into orders, invoices, dealer identification, and personalized discounts

For other companies running on NetSuite, S&B is a reference point. The same approach — production AI layered on top of the ERP — applies across operations, finance, support, and sales.

Methodology note: Estimates are based on pre-implementation ticket volume, average handling time reduction driven by AI-assisted responses, and typical support agent cost benchmarks. Automation rate reflects the share of inquiries fully resolved by AI without human intervention, while ROI accounts for both operational savings and implementation costs.

What's Next

Already on the roadmap:

• Full order control — customers placing, modifying, and canceling orders via the assistant
• Dealer identification via NetSuite data
• Personalized discounts based on customer history
• Invoice access through the chatbot
• Deeper Zendesk automation