A dental marketing company modernizes for speed & scale

Over eight years, we partnered with a leading dental marketing company to transform its legacy SaaS platform into one of the top solutions in its industry. Through long-term modernization, we turned a slow, monolithic architecture into an intelligent, cloud-based platform with automated workflows and AI-driven capabilities. Today, hundreds of solo dentists, groups, DSOs, and consultants rely on the platform to run and grow their practices.

Our work involved

• Cloud migration and CI/CD implementation on Amazon Web Services (AWS)
• Application modernization with Node.js and Angular
• Database performance optimization with PostgreSQL
• Implementation of a job management system
• AI-driven feature enablement

Impact

Many organizations approach application modernization as a project with hard start and stop dates. It starts when technical debt, UX issues, or slowing release velocity become impossible to ignore, and ends once the most visible symptoms disappear.

That approach may relieve short-term pressure, but it rarely produces lasting advantage. True innovation requires a modernization mindset that persists across the entire life of the product.

We partnered with a dental marketing company on a long-term, strategic modernization of its SaaS platform. This case study chronicles our eight-year modernization journey, which ultimately helped the dental company thrive in a highly competitive industry.

Challenge

When a monolithic architecture hits its limits

The company has helped practices reach the right patients through data-driven marketing for over a decade. At the center of its ecosystem is its flagship SaaS platform designed for clinics to manage their entire marketing funnel: from patient acquisition and retention to reputation management.

As adoption grew, the SaaS platform became a recognized brand in the industry. But scale exposed the limits of its monolithic, legacy architecture:

• Erratic performance: Report generation and data-heavy workflows struggled to keep up as usage and data volumes increased.
• Slow release velocity: Even small enhancements required outsized effort, delaying improvements that customers were asking for.
• Limited integration capabilities: Connecting the platform to new tools and data sources added complexity instead of flexibility.

The company knew it needed to modernize the application architecture. But it also knew it couldn’t afford to disrupt the service for customers.

Solution

Embedding modernization into company DNA

The project started with addressing immediate concerns caused by legacy architecture. Once progress was visible, the focus shifted to building a platform designed to remain competitive over time.

This eight-year partnership had four distinct modernization milestones along the way.

Phase 1: Rebuilding the platform foundation

The legacy platform operated in a rigid hosting environment with tightly coupled services, making it difficult to scale and expensive to change. We led an initiative to migrate the platform from a monolith to a cloud-based, modular architecture.

The migration followed an incremental modernization model, with production workloads supported throughout major architectural changes, including:

• Migrating core systems to AWS and introducing CI/CD pipelines
• Rebuilding core features in Node.js and Angular
• Introducing RESTful APIs for front-end and data services
• Enhancing PostgreSQL queries using modern database features to improve performance and reliability
• Implementing an advanced background job management system to handle high-volume data tasks asynchronously

This foundational work was not visible to users, but it restored the platform’s ability to evolve without compounding risk.

Phase 2: Creating a user & developer-friendly frontend

With a stronger backend foundation in place, the attention turned to the SaaS platform’s user experience. We rebuilt the frontend as a lightweight Angular application, replacing one-off implementations with reusable components.

The new interface delivered faster load times, responsive behavior across devices, and live dashboards with interactive components. As a result, usage increased, and teams could ship improvements without solving the same problems twice. Just as importantly, it created a frontend architecture that could scale alongside the business without rework.

Phase 3: Optimizing for automation & speed

As the platform matured, the next constraints were speed and operational efficiency. Growing data volumes had slowed report generation, and manual workflows limited how efficiently the platform could scale.

This phase targeted performance improvements across data-heavy workflows. We addressed performance at the data and processing layer by optimizing SQL queries, introducing intermediate data layers, and refactoring report generation logic. This reduced average response times by more than 60 percent.

Asynchronous task processing enabled high-volume analytics jobs to run without blocking user interactions, while continuous performance and health monitoring with automated alerts ensured early detection of degraded services.

By the end of this phase, marketing data feeds, campaign metrics, and call-tracking data were all processed automatically, powering real-time ROI dashboards for each practice. Similarly, data-anomaly alerts enabled proactive oversight, while AWS CI/CD automation reduced manual intervention and release risk.

Phase 4. Adopting AI with an eye on ROI

By the time the AI wave reached businesses, the company already had the right foundation to capitalize: clean data flows, reliable performance, and automated workflows. This made it possible to introduce AI as a natural extension of the platform, not a risky experiment bolted on top.

We introduced several AI-driven features to the SaaS platform with a clear impact on specific business outcomes. For example, we integrated AI-powered call scoring for sentiment analysis, tone detection, and call quality assessment. Additionally, we used OpenAI's API to automatically extract the call handler’s name from conversations, generate summaries of calls, and highlight key insights for marketing and ROI optimization.

These AI-driven improvements reduced manual review time, increased accuracy, and provided real-time insights to dental marketing teams.

Impact

A SaaS platform built for long-term adaptability

The long-term modernization of the SaaS platform fundamentally changed what the business could sustain. Where many SaaS products struggle with each major technology shift, a continuous modernization mindset positioned the company to absorb change and capitalize on it without losing momentum.

Throughout the modernization journey, we achieved several improvements that helped turn the dental SaaS platform into one of the most reliable platforms in the market:

• 99.99% uptime due to improved architecture and redundancy
• 90% reduction in manual reviews with AI
• 60% reduction in average response time
• Fully-automated marketing and ROI workflows
• Faster release velocity and overall platform performance

The modernization initiative began as an effort to resolve performance issues, but over time, it became a part of the company culture. It’s a model that supports growth, enables new capabilities like AI, and positions the company to stay competitive as customer needs and the market evolve.