Moultrie reduced seasonal hiring headaches and delivered step-by-step troubleshooting with Quiq AI agents

Moultrie’s support complexity stems from its products. Trail cameras and feeder systems involve firmware, connectivity, cellular transmission, power management, and device-specific settings that vary across dozens of models. A customer asking why their camera isn’t sending images might be experiencing a power issue, a network registration problem, a SD card error, or a firmware bug. The diagnostic path depends on which.

The previous chatbot failure made the requirements for a new system explicit:

• Seasonal volume that couldn’t be staffed away: Moultrie’s contact volume follows a sharp seasonal pattern. Hiring agents to cover the peak meant recruiting, onboarding, and training new staff every year, then scaling back. The resulting support quality was inconsistent, and the operational overhead was significant.
• A customer base that distrusts generic AI responses: Moultrie customers are technically knowledgeable about their products and expect specific, accurate answers. They notice when an AI response is generic or doesn’t match their actual situation. The previous chatbot’s failure was immediate and visible.
• Device-specific troubleshooting that required real context: Answering questions like “why won’t my camera connect to the Moultrie Mobile app” correctly requires knowing the camera model, the cellular plan, whether the SIM is active, and what the customer has already tried. Static FAQ matching doesn’t have access to that context.
• Need for step-by-step visibility in the AI agent: Moultrie needed to be able to see why the AI agent was generating specific responses, test changes before deploying them, and make updates without requiring engineering support. The previous black-box system made it impossible to diagnose failures or iterate quickly.

Moultrie and Quiq built Avery, an agentic AI agent, configured to handle the brand’s specific support needs with the transparency and configurability the team required.

The deployment was built around:

• Data Transformation: Moultrie’s support content spans product manuals, knowledge base articles, troubleshooting guides, and device specifications across a wide product catalog. Quiq processes this content automatically, chunking it into focused sub-articles, generating related questions, extracting URLs, and keeping everything in sync with source documents. The team doesn’t manually reformat content for AI use.
• Platform Integrations: Avery connects to Zendesk, Moultrie’s order management system, and device and account information APIs. When a customer asks about a specific camera model, Avery can pull the technical specifications and the customer’s account information, including registered devices, in real time. Troubleshooting uses actual device data, not generic guidance.
• Orchestration Layer: For each customer message, Avery performs multi-dimensional classification: what the customer intends, which device or product line is involved, what troubleshooting topic applies, whether the customer has already attempted fixes, and whether a photo upload would help diagnose the issue. This analysis selects the appropriate Process Guide, the specific set of instructions and tools for that situation, and validates responses before sending them.
• AI Studio for clear-box management: The Moultrie team manages Avery entirely in Quiq’s AI Studio, which provides full visibility into why specific responses were generated, testing tools for evaluating changes against real conversation scenarios, version control with rollback capability, and monitoring of AI decision-making in real time. The team makes updates without engineering involvement. Parker DiPaolo and the team can see exactly what Avery is doing and why.
• Human-like language by design: Moultrie’s customer base expects warmth and empathy, not robotic phrasing. Avery uses discourse markers, follow-up questions, and empathetic phrasing calibrated to the brand’s voice. When a customer contacts support with a frustrating product issue, Avery acknowledges the frustration before addressing the problem.

Device connectivity diagnosis: A customer contacts Moultrie because their trail camera stopped sending images to the Moultrie Mobile app. Avery asks for the camera model and last known cell signal, checks the customer’s registered device information via the device API, determines the cellular plan status, and walks through a targeted diagnostic sequence specific to that model and carrier. If the issue requires a hardware reset, Avery provides the exact sequence for that model, not a generic reset instruction.

Pre-season setup for a new camera: A customer who just purchased a new trail camera contacts support to understand how to set up the cellular plan and register the device. Avery walks through account creation, device registration, and cellular plan activation step by step, asking clarifying questions when the customer’s setup differs from the default configuration. The entire onboarding interaction completes without human agent involvement.

Escalation with full context: When Avery determines it can’t fully resolve an issue, it generates a structured summary of the problem and extracts the relevant data points before escalating. The human agent who receives the escalation knows the device model, what’s been tried, what the diagnostic pointed to, and what the customer has said. They don’t start from scratch.

The seasonal staffing cycle that drove the initial decision to adopt AI has changed fundamentally. The volume that required hiring additional agents each year now routes to Avery first. Seasonal peaks that previously required weeks of recruiting and onboarding are absorbed by the AI agent. The team still scales for peak season, but the scale required is smaller, and the human agents who are hired focus on genuinely complex issues from day one.

Human agent satisfaction has also improved. The handoffs from Avery arrive with context attached, so agents aren’t asking customers to repeat themselves. The queue that reaches the human team consists of the issues that genuinely require human judgment, which makes the work more engaging and the handoffs more efficient.

Parker DiPaolo uses AI Studio to monitor Avery’s performance, identify which question categories are producing the highest volume, and iterate on process guides in response. The team’s ability to make changes without engineering support has compressed the iteration cycle from weeks to days.

Avery launched with a 35% AI resolution rate that is still improving. The impact on seasonal hiring and operational efficiency was immediate, and the team’s ability to maintain quality through peak season has changed the conversation around how Moultrie scales its support operation.

• 35% AI resolution rate from launch, with the rate continuing to improve through ongoing iteration
• Seasonal hiring requirements significantly reduced: peak volume now routes to Avery before reaching the human team
• Complex, multi-turn troubleshooting handled end-to-end, including device-specific diagnosis using real account and telemetry data
• Full observability: the team can see every AI decision, test changes before deployment, and update Avery without engineering support
• Human agent satisfaction improved through structured escalations with full context attached
• Fast time-to-value: Avery launched quickly and delivers ongoing improvement through AI Studio’s lifecycle management tools