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Waypoint AI Scales Agentic Support Engineering on AWS with Commit

  • 20 hours ago
  • 3 min read



About Waypoint AI

Waypoint AI builds AI-powered workflow automation for enterprise support and engineering teams. Its core product, Chip, is an AI Support Engineer that autonomously triages, routes, and resolves complex support cases. Chip also answers technical questions, generates postmortems, and surfaces actionable insights for engineering, support, product, and revenue teams.

By reducing the administrative friction associated with customer-reported issues, Waypoint AI helps software engineers stay focused on high-value development work. Its platform can free up approximately 10% to 15% of an engineering organization’s capacity, enabling teams to operate more efficiently and scale customer support without adding unnecessary overhead.

Challenge

As Waypoint AI began engaging larger enterprise customers, data hosting flexibility became a critical requirement. Many organizations needed the ability to choose where their data would be hosted, and AWS quickly emerged as a preferred environment.

To support this demand and move forward with several strategic customer opportunities, Waypoint AI needed to establish a secure, scalable AWS infrastructure that could meet enterprise expectations and accelerate onboarding.

Solution

Commit designed and implemented an enterprise-ready AWS foundation to support Waypoint AI’s migration, customer onboarding, and long-term growth.

  • Multi-account AWS architecture: Built on AWS Organizations, with dedicated accounts for management, security, logging, shared services, and individual environments.

  • Infrastructure-as-code: Used Terraform to enable consistent provisioning, stronger workload separation, improved cost visibility, and lower operational risk.

  • Scalable Cloud Foundation: Implemented a structured landing zone, multi-tier VPC design, private connectivity, Amazon CloudFront, AWS WAF, Amazon Route 53, and support for future European expansion.

  • Containerized application deployment: Deployed workloads using Amazon ECS, Amazon ECR, Amazon RDS for PostgreSQL, and Amazon ElastiCache for Redis, supported by CI/CD processes.

  • Enterprise tenant isolation: Established customer-specific configurations, strict IAM role separation, database isolation, AWS Secrets Manager, Parameter Store, and AWS KMS.

  • Security and operational readiness: Added AWS Identity Center, GuardDuty, AWS Config, Security Hub, Inspector, CloudTrail, CloudWatch, AWS Backup, centralized logging, monitoring dashboards, and operational documentation.

  • AWS Marketplace enablement: Supported onboarding and integration activities to help streamline enterprise procurement.

  • AI governance and future readiness: Advised on Amazon Bedrock governance, usage visibility, access controls, cost management, and AWS-native options for PII masking.

  • Ongoing strategic support: Expanded the engagement to include cloud operations, FinOps, architecture planning, AI governance, and customer deployment support.


Results

The AWS environment gave Waypoint AI a stronger technical and commercial foundation for enterprise growth. The company can now support AWS-hosted customer deployments with greater isolation, security, operational visibility, and repeatability.

  • Successful enterprise customer onboarding: Waypoint AI was able to move forward with key customers that required their infrastructure and data to be hosted on AWS.

  • Scalable customer deployment model: The multi-account architecture, tenant isolation controls, reusable infrastructure-as-code components, and customer-specific deployment patterns created a more structured approach to onboarding additional enterprise customers.

  • Improved security and operational readiness: Centralized logging, identity management, threat detection, backup, monitoring, and audit capabilities strengthened Waypoint AI’s ability to operate production workloads in line with enterprise expectations.

  • Faster procurement through AWS Marketplace: AWS Marketplace enablement gave customers a more familiar purchasing route, helping simplify procurement processes and reduce friction in enterprise software adoption.

  • Greater cost and usage visibility: Ongoing FinOps support, cloud monitoring, and discussions around Amazon Bedrock governance improved Waypoint AI’s ability to track infrastructure consumption, AI usage, and future cloud costs.

  • A foundation for AI architecture evolution: The AWS environment allows Waypoint AI to maintain existing AI dependencies while evaluating Amazon Bedrock and other AWS-native services for future workloads.

  • Long-term strategic growth: What began as a cloud migration developed into an ongoing partnership, with Commit supporting Waypoint AI across cloud management, customer onboarding, architecture, AWS Marketplace activities, and go-to-market expansion.

Customer Quote: 

“Commit for us represents not only a partner for migrating to AWS, but also thinking about how we scale up our go-to-market strategy so that we can serve more customers faster? What started as a one and done of a lift and shift from one cloud provider to another has turned into a long journey together.

Liam Boogar, Co-Founder & CRO



 
 
 

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