Inside Precision Opinion: Improving the Infrastructure Behind America’s Largest Surveys

• 600+ CATI stations — One of the largest survey research data centers in the US
• 750–1,200 employees — At peak capacity during election cycles
• SOC2 Type II — Certified · AAPOR member
• Engagement — Ongoing production support, maintenance, and integration management
• Stack — 10 AWS services: Lambda, API Gateway, Cognito, DynamoDB, S3, CloudFront, WAF, EventBridge, CloudWatch, Pinpoint
• Integrations — Voxco, BambooHR, Paycor

Key Facts

• Location: Las Vegas, Nevada, USA
• Scale: 600+ high-technology CATI interviewing stations
• Team: 750–1,200 employees at peak capacity
• Certification: SOC2 Type II · AAPOR member

Precision Opinion is one of the largest independently owned market research firms in the United States, with nearly 20 years of operation. Headquartered in Las Vegas, Nevada, the company is home to the James T. Medick Center for Survey Research — a 42,000 square foot facility with 600 Voxco CATI interviewing stations, completed in 2022.

The company works as a contractor for federal research programs, major media organizations, social science agencies, universities, and political consulting firms. Precision Opinion was the only subcontractor for nearly 20 years on what it describes as the largest federal data collection project in the market research industry. The company fielded national polling data for the Obama Campaign through the 2008 and 2012 presidential elections and conducted over 1,000 surveys with major political consulting firms across that period.

Precision Opinion holds SOC2 Type II certification, achieved in 2020, and is a member of AAPOR. The company maintains a strict no-offshoring, no-outsourcing, no-remote policy — all work is completed in-person within its Las Vegas facility. At peak capacity during election cycles, the company employs between 750 and 1,200 people. The internal systems that coordinate staff across active research projects carry direct operational weight.

Parts of the Precision on Time platform had evolved in ways that made future changes difficult and increased operational risk. Integrations between the platform and external systems had stopped passing data consistently, creating friction in client engagement with staff and impeding the effectiveness of research operations.

That was the starting point. The constraint that made it difficult was that stabilization had to happen without adversely impacting the operations already running on the platform. Any change to a production system at this scale, with undocumented dependencies, carried real risk to active research projects and the staff coordination that supported them.

Before making any changes, HebronSoft conducted a comprehensive analysis of the existing system to understand how it actually functioned: reconstructing the logic of every workflow, identifying dependencies between services, and understanding which parts of the system were fragile enough that touching them without full context could cause failures in live operations.

The analysis established the sequence in which work could proceed safely. The technical documentation produced during that process was the first reliable reference the team had for how the system actually functioned.

“When a system has no documentation, every change starts with investigation. Before we could build anything new, we had to understand which parts of the platform were holding the business together and which could be safely improved.”

— Bohdan Bilas, CTO, HebronSoft

HebronSoft provided ongoing technical support and maintenance for the existing AWS-based platform, covering stabilization, defect resolution, integration support, and implementation of changes according to business requirements.

Restoring Operational Stability

• Production support and troubleshooting across the serverless AWS infrastructure
• Analysis, localization, and resolution of issues in the production environment
• Bug fixing and maintenance without disrupting active business processes
• System stabilization sequenced to protect live research operations throughout

Reconnecting Critical Business Systems

• Analysis and resolution of integration issues between platforms
• Support for Voxco, BambooHR, and Paycor integrations
• Support for data import and synchronization with external systems
• API integration support via Amazon API Gateway and AWS Lambda

Making the Platform Observable

• System monitoring, log analysis, and incident diagnostics via Amazon CloudWatch
• Authentication and authorization support via Amazon Cognito
• Event-driven process support via Amazon EventBridge
• Technical documentation created from scratch — the first complete reference for how the system functioned

AWS Services

Amazon API Gateway, AWS Lambda, Amazon Cognito, Amazon DynamoDB, Amazon S3, Amazon CloudFront, AWS WAF, Amazon EventBridge, Amazon CloudWatch, Amazon Pinpoint.

Before the engagement, integration gaps between platforms were impeding client engagement with staff and the effectiveness of research operations. The system had no documentation, which meant diagnosing issues in production required reconstructing context from scratch each time.

• Active research operations continued without interruption throughout the stabilization work
• Integration gaps between Voxco, BambooHR, and Paycor resolved, restoring reliable coordination between staff and client engagements
• Technical documentation produced for the first time — giving the team a reference for maintenance, troubleshooting, and future development that had never existed before
• Platform prepared for further integration and capability development
• Continuous monitoring and incident response established through CloudWatch and EventBridge

We learned that understanding the system was part of the implementation itself, not something that happened before it. Mapping how the platform actually worked — not how it was supposed to work — shaped every decision that followed about what to change, in what order, and with what level of caution. The documentation produced during that process was the first time the team had a stable reference for the system, and it reduced the risk of every subsequent change.