Rely.io provides an internal developer portal that integrates with critical tools to create a centralized software catalog, offering real-time visibility into service ownership, documentation, and operational metrics. This platform enhances engineering productivity by streamlining access to essential information and automating routine tasks, thereby reducing ticket operations and SLA violations.
Funding
$2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
JLBSFounders
Product
Problem
Software companies with distributed architectures face challenges in efficiently delivering high-quality software at scale due to fragmented information across various tools and teams. This lack of centralized visibility hinders understanding, operating, and building new software, leading to reduced engineering productivity, increased ticket operations, and SLA violations.
Solution
Rely.io provides an internal developer portal that integrates with critical engineering tools to create a centralized software catalog, offering real-time visibility into service ownership, documentation, and operational metrics. The platform consolidates data from sources like Kubernetes, Terraform, CI/CD pipelines, cloud environments, and monitoring tools, presenting it in a unified view. Rely.io enhances engineering productivity by streamlining access to essential information, automating routine tasks, and enabling self-service workflows. The platform also includes an AI assistant trained on the company's data, allowing developers to quickly find answers to questions across their entire engineering stack.
Target Audience
Rely.io is designed for platform engineers, SREs, DevOps, product engineers, and engineering leaders in software companies with distributed architectures.
Features
- Software catalog with centralized data on ownership, documentation, deployments, on-call schedules, SLOs, and operational maturity
- Integrations with AWS, Azure, GCP, Kubernetes, Datadog, GitHub, GitLab, Jira, and more
- Customizable data model to represent assets in the software ecosystem
- AI Assistant for searching across engineering knowledge base without query languages
- Scorecards and leaderboards to promote adoption of engineering standards and track progress
- Developer self-service capabilities to automate tasks and reduce ticket operations
- Engineering performance tracking with metrics and monitoring of performance trends
- Automated dependency mapping and unified insights
- Customizable organizational standards