How AI Helped a Mid-Sized SaaS Company Streamline Projects and Scale

👤 Nathan Liston, Director of Data & AI

EXECUTIVE SUMMARY

A mid-sized North American SaaS company serving the public sector sought to reduce project abandonment and expand into private markets. Users often dropped off midway due to the platform’s manual, time-intensive design process, and the small internal team lacked bandwidth to innovate or scale.

Partnering with CleanSlate Technology Group, the company implemented a custom AI-powered solution on AWS—leveraging technologies like SageMaker, Rekognition, and custom CNNs to automate image processing, project grouping, and design workflows.

In just six weeks, a Minimum Viable Product (MVP) launched with over 90% project completion rates, reduced internal workload, and enabled market expansion. A second phase is now underway, focused on enhanced security and advanced AI features.

This success underscores CleanSlate’s ability to drive innovation, streamline workflows, and support long-term growth through intelligent automation.

A North American SaaS-based company with clients in the public sector wanted to enhance their existing product for thousands of institutions and expand their offerings into new markets within the private sector. But the company faced a critical barrier: users frequently abandoned projects due to the level of manual time and effort needed to create and design products with their existing platform.

To break through this challenge and deliver a superior user experience at scale, the company partnered with CleanSlate Technology Group to build an AI-powered solution on Amazon Web Services (AWS). The result was a streamlined, automated platform that provided a superior experience. It reduced the manual effort associated with starting each project and enabled users to instead focus just on refining nearly complete projects.

challenges

High Drop-Off Rates in Project Completion

Although users were excited about the projects they started, many became fatigued with the manual effort and only completed 60–70% of the process. The existing process produced great outcomes when finished, but required too many manual and intensive steps to complete a project.

Additionally, the SaaS company had a small team that was struggling to keep up with the demands of helping public sector clients finish their projects. The company did not have time to explore new market opportunities outside the public sector. The company needed to adopt an automation-first solution that would:

• Increase project completion rates

• Eliminate manual bottlenecks

• Support a high volume of users and projects

• Maintain professional quality

Solutions

AI-Powered Automation Built on AWS

Working closely with the client’s product and technical teams, CleanSlate designed a multi-phase, cloud-native AI solution that reduced friction in the design process and delivered end-to-end automation.

Phase 1: AI-Based Image Processing and Enhancements

• Applied convolutional neural networks (CNNs) to identify important design elements

• Built custom deep learning models to auto-enhance images based on industry standards.

• Delivered automated high-quality image enhancements consistently with minimal user input

Phase 2: Automated Project Groups and Design Selection

• Used AWS Rekognition for object and scene recognition

• Grouped projects by context: event type, similarity, or time

• Recommended templates and designs for many customer groups to each group

• Empowered users with adjustable design preferences

Phase 3: Operationalizing for Scale

• Leveraged AWS SageMaker, Lambda, and serverless architecture for scalability

• Combined traditional machine learning models (e.g., k-nearest neighbors) with proprietary business logic

• Applied design quality scoring and template-matching to ensure consistency

• Designed for elastic performance with minimal manual oversight

Technologies and Services Used

• AWS SageMaker

• AWS Rekognition

• Lambda (serverless compute)

• Custom deep learning models

• Machine learning-based image quality assessment

• Infrastructure-as-Code (IaC) for repeatable deployment

Results

Transformed project workflows with AI and AWS—reducing manual effort, accelerating delivery, and opening new markets.

• 90%+ completion rate in AI-assisted projects

• Increased revenue by enabling the company to expand to private sector industries

• Improved operational efficiency by reducing manual workload

• Accelerated speed to market for new offerings

• Delivered a Minimum Viable Product (MVP) in just 6 weeks

Learn more about the Project Lead: Nathan Liston

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