Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.
Learn more

NeuBird AI is a Production Ops Platform designed for ITOps, SRE, and DevOps teams running production cloud environments. It uses agentic AI to move operations from reactive incident response to proactive, autonomous production management.
Despite significant investment in monitoring and observability tools, teams still face alert noise, slow root cause analysis, and costly incidents. NeuBird AI solves this by continuously analyzing telemetry across cloud services, applications, and infrastructure to prevent issues, resolve incidents faster, and optimize operations.
Prevent incidents before they happen
NeuBird AI detects early signals of degradation, configuration drift, and anomaly patterns across metrics, logs, traces, and change events. Teams can identify and address issues 30 to 60 minutes before user impact while reducing alert noise by more than 78 percent.
Resolve incidents in minutes
When incidents occur, NeuBird AI automatically investigates across Azure Monitor, Amazon CloudWatch, logs, metrics, traces, and recent changes to identify root cause in minutes. AI driven triage, correlation, and runbook generation reduce mean time to resolution by up to 60 percent while minimizing the need for large war room responses or bridge calls.
Optimize cost, performance, and operations
NeuBird AI continuously analyzes cloud environments to uncover cost savings, performance issues, and gaps in observability. It identifies right sizing opportunities, missing telemetry, and repetitive operational tasks, helping teams reclaim more than 200 engineering hours per month.
Built for production cloud operations
NeuBird AI integrates with AWS services including CloudWatch, as well as Kubernetes and Azure Monitor, and tools like Datadog, Splunk, and PagerDuty.
Learn more
IBM SevOne
Enhance your IT operations with actionable insights derived from application-focused network observability. Are you finding it challenging to manage the increasing complexities associated with contemporary network systems? As digital transformation progresses, network infrastructures necessitate monitoring solutions that mirror their dynamic, adaptable, and scalable nature. Tailored for today's networks, IBM® SevOne® Network Performance Management (IBM SevOne NPM) offers application-centric observability, empowering NetOps to identify, tackle, and avert network performance challenges within hybrid setups. By actively monitoring multi-vendor networks, you can elevate network performance and enhance user application experiences while translating insights into concrete actions across enterprise, communication, and managed service provider landscapes. In addition to merely identifying issues, SevOne NPM integrates leading industry knowledge with cutting-edge analytics, enabling your teams to focus on what truly matters: optimizing network performance and ensuring seamless connectivity. With this powerful tool, organizations can navigate the complexities of modern networking more effectively.
Learn more
Motadata
Most IT teams run separate tools for metrics, logs, flow analytics, and traces, then waste hours stitching the data together during an incident. Motadata ObserveOps replaces those silos with a single unified observability platform that triangulates logs, metrics, and flow data in one view.
The platform is built on DFIT, Motadata's deep learning framework for IT operations, and uses adaptive AI that requires no pre-training. It handles anomaly detection, alert correlation, noise reduction, and predictive monitoring out of the box, and integrates natively with Motadata ServiceOps to turn detected issues into tickets automatically. Available across SaaS, on-premise, private cloud, public cloud, and hybrid deployments for enterprises, SRE teams, and MSPs.
Learn more