Jaeger Integrations

While many have ventured into web-to-print solutions, only a select few have managed to craft a truly effective e-commerce strategy. At PageDNA, we go beyond simply providing a digital storefront; we enable your print operations to establish successful e-commerce frameworks. Our platform is designed to increase sales, minimize operational costs, and improve the profitability of your print sales organization. We offer an all-encompassing web-based digital storefront solution, equipped with powerful job management and reporting tools. In today's market, customers demand a sleek and professional look for their storefronts, no matter how they choose to place their orders. Our focus transcends print workflow and ordering automation; we aim to enhance your efficiency while allowing you to save both time and resources. By tailoring your storefront to cater to specific user groups, you can optimize the user experience and simplify navigation. We systematically arrange your products into an easily navigable catalog, ensuring that your branding remains cohesive and professional across all devices, be it desktop or mobile. This meticulous approach can greatly enhance customer satisfaction and foster loyalty, translating into repeat business and long-term success. In essence, we aim to revolutionize your e-commerce presence in the print industry.

Optimize your performance testing, analysis, and reporting workflows to remove the monotonous tasks that developers find unappealing. This optimization allows you to maintain product quality while reducing mental fatigue among your development team, which is crucial in agile settings. Designed to support multiple teams navigating complex agile methodologies, this solution enables efficient performance management even with a smaller number of performance engineers. It encourages a uniform approach to performance testing across the organization, ensuring consistency and reliability. Furthermore, it allows for the exchange of performance testing knowledge among any team involved in any project at any time, thereby embedding performance testing in the developer culture and improving the overall quality of websites and services. Perfana equips teams to evaluate their software's performance not just before production but also with every new build, making the process simple and effective. Our plugin integrates effortlessly with popular CI servers, allowing all teams to validate performance with each build or release, thus reinforcing a continuous focus on quality throughout the development lifecycle. This all-encompassing strategy not only boosts productivity but also cultivates an environment of collaboration and ongoing enhancement, leading to better outcomes for all projects involved. As a result, teams are better equipped to react swiftly to performance issues, ensuring that the final product exceeds expectations.

Diagnosing and fixing performance problems and errors in your microservices involves a thorough examination of root causes through traces, logs, and metrics. By utilizing Aspecto's integrated remote sampling, you can significantly cut down on OpenTelemetry trace costs. The manner in which OTel data is presented plays a crucial role in your troubleshooting capabilities; with outstanding visualization, you can effortlessly drill down from a broad overview to detailed specifics. The ability to correlate logs with their associated traces with a simple click facilitates easy navigation. Throughout this process, maintaining context is vital for quicker issue resolution. Employ filters, free-text search, and grouping options to navigate your trace data efficiently, allowing for the quick pinpointing of issues within your system. Optimize costs by sampling only the essential information, directing your focus on traces by specific languages, libraries, routes, and errors. Ensure data privacy by masking sensitive details within trace data or certain routes. Moreover, incorporate your daily tools into your processes, such as logs, error monitoring, and external events APIs, to boost your operational efficiency. This holistic approach not only streamlines your troubleshooting but also makes it cost-effective and highly efficient. By actively engaging with these strategies, your team will be better equipped to maintain high-performing microservices that meet both user expectations and business goals.

Plano is a comprehensive AI delivery infrastructure platform designed to simplify the development, deployment, and operation of agentic applications. Built as an AI-native data plane and proxy layer, the platform handles the complex infrastructure components required to support modern AI agents at scale. Developers can leverage Plano to manage agent orchestration, intelligent model routing, observability, security controls, context engineering, and policy enforcement through a centralized architecture. The platform enables seamless integration with more than one language model provider through a unified API, reducing the complexity of multi-model deployments. Detailed tracing and monitoring capabilities provide deep insights into agent interactions, helping teams troubleshoot issues, improve reliability, and support reinforcement learning workflows. Plano’s architecture allows developers to continue using their preferred programming languages, frameworks, and development tools without introducing restrictive dependencies. Built-in guardrails help detect and mitigate risks such as jailbreak attempts while enforcing organizational policies across applications. The platform also supports reusable context engineering filters that improve agent responses and operational consistency. Organizations operating in regulated industries can deploy Plano on-premises to maintain greater control over sensitive data and infrastructure. Its configuration-driven approach simplifies deployment by allowing teams to define agent behavior, model preferences, and integrations through a streamlined setup process. By handling the underlying plumbing of AI systems, Plano empowers organizations to focus on innovation, accelerate development cycles, and deliver production-ready AI agents with greater confidence.

OpsVerse's ObserveNow serves as a comprehensive observability solution that integrates logs, metrics, distributed traces, and application performance monitoring into a singular, unified platform. By utilizing open-source technologies, ObserveNow allows for rapid deployment, enabling users to monitor their systems in just minutes without needing extensive technical expertise. It is versatile enough to be utilized across different environments, including public clouds, private clouds, and on-premises setups, while ensuring data compliance by giving users the option to store their data securely within their own network. The platform is equipped with intuitive pre-configured dashboards, alert systems, advanced anomaly detection, and automated remediation workflows, all aimed at significantly reducing the mean time to identify and address issues. Additionally, ObserveNow provides a private SaaS option, allowing businesses to benefit from the SaaS model while keeping their data management within their own infrastructure. This cutting-edge platform not only improves operational efficiency but also operates at a much more affordable price point compared to traditional observability solutions on the market today. Ultimately, ObserveNow empowers organizations to take control of their observability needs while optimizing performance and reducing costs.

Tracetest is an innovative open-source testing framework that allows developers to create and run both end-to-end and integration tests through the use of OpenTelemetry traces. This framework not only checks the final outcomes but also examines each step of the process, ensuring that all components of a distributed system function correctly. It integrates smoothly with widely-used testing frameworks like Cypress, Playwright, k6, and Postman, enhancing testability and visibility without requiring any changes to the current codebase. By leveraging trace data, Tracetest identifies issues such as incorrect service interactions or performance bottlenecks that might be overlooked with traditional testing methods. It also works effectively with various observability platforms and can be easily incorporated into CI/CD pipelines to support continuous testing efforts. Moreover, Tracetest includes synthetic monitoring capabilities that aid in the proactive detection of performance challenges, safeguarding user experience. This versatile tool not only strengthens testing precision but also fosters increased assurance in the dependability of distributed systems, making it an essential asset in modern software development. Ultimately, the use of Tracetest contributes to a more robust and reliable software delivery process.

NudgeBee is an AI-powered Agents and Agentic Workflow platform designed for modern SRE, CloudOps, DevOps, and platform engineering teams. It helps organizations reduce MTTR, cut cloud waste, automate Day-2 operations, and scale infrastructure management without increasing headcount. The platform delivers immediate value through pre-built AI Assistants: an AI SRE Agent for automated incident triage, root cause analysis, and remediation guidance; an AI FinOps Assistant for continuous cloud and Kubernetes cost optimization; and an AI K8sOps Agent for natural-language cluster operations and maintenance. These assistants work out of the box, no model training or prompt engineering required. For processes unique to your environment, NudgeBee's visual no-code Workflow Builder provides 20+ action categories, 25+ production-ready templates, and AI-native nodes including A2A (Agent-to-Agent) and MCP (Model Context Protocol) support. Teams can build workflows that span multiple clouds, Kubernetes clusters, databases, ticketing systems, and communication channels, all with human-in-the-loop approval gates. What makes NudgeBee different is a live semantic Knowledge Graph that understands your infrastructure topology in real time. Zero data ingestion, the platform queries your existing observability tools (Prometheus, Datadog, Grafana, Loki, and 49+ others) in place, eliminating data egress costs and compliance concerns. Enterprise-ready with RBAC, MFA, immutable audit trails, BYOM (Bring Your Own Model supports GPT, Claude, Gemini, Bedrock, Ollama etc), and flexible deployment options including self-hosted, cloud-SaaS, and on-prem managed. SOC-2 Type II compliant and ISO 27001 certified.

Hyground acts as an AI-powered co-pilot tailored for DevOps and Site Reliability Engineering (SRE), providing a holistic operational intelligence platform that embeds itself within the customer’s Kubernetes environment while ensuring that no data is transmitted off-site. This advanced tool connects with more than 21 enterprise systems to evaluate incidents using diverse sources like logs, metrics, traces, and Kubernetes events. Engineers can ask questions in simple language and obtain insights that are customized to their unique datasets, which eliminates the necessity of learning complex query languages. The AutoRCA feature converts alert webhooks into independent root-cause analyses, sending notifications directly to platforms such as Slack or Teams. The investigation begins as soon as an alert is triggered, rather than waiting for an engineer's intervention, enabling clients to achieve reductions in mean time to resolution (MTTR) by as much as 85%. Utilizing Google’s Agent Development Kit, Hyground adopts a multi-agent framework that adapts by continuously learning from the customer’s infrastructure as it evolves. Each incident resolved contributes to the expanding knowledge base, ensuring that operational runbooks stay current and pertinent for upcoming challenges. Consequently, by promoting real-time insights and ongoing learning, Hyground significantly enhances the efficiency and effectiveness of teams in their operations. With this innovative approach, organizations can focus more on strategic initiatives rather than being bogged down by reactive troubleshooting.

The ContextForge MCP Gateway is an open-source solution acting as a Model Context Protocol (MCP) gateway, registry, and proxy, providing a unified endpoint for AI clients to access tools, resources, prompts, as well as REST or MCP services within complex AI environments. This system operates in conjunction with various MCP servers and REST APIs, streamlining processes related to discovery, authentication, rate-limiting, observability, and traffic management across numerous backend systems, and supports multiple transport mechanisms such as HTTP, JSON-RPC, WebSocket, SSE, stdio, and streamable HTTP; it also possesses the ability to convert legacy APIs into MCP-compliant tools. Moreover, it includes an optional Admin UI that allows users to configure settings, monitor activities, and access logs in real-time, while being designed to scale from single-instance setups to large multi-cluster Kubernetes environments, utilizing Redis for federation and caching to boost both performance and resilience. This architecture makes the ContextForge MCP Gateway not only a facilitator of seamless interactions within intricate AI architectures but also a highly adaptable platform that can meet the diverse demands of various operational contexts. Ultimately, the platform enhances the overall efficiency and effectiveness of AI integrations, ensuring that users can maximize their technological investments.