What does Zeliot do?
Zeliot is building a real-time data streaming platforms that help enterprises develop, scale, and operate production-grade streaming applications faster using AI-driven workflows, managed Kafka infrastructure, and industry-specific streaming capabilities.What exactly does “AI-Driven Pipeline” mean?
“AI-driven” in Condense means the platform assists users with pipeline generation, optimal connector configuration, logic validation, Monitoring and even auto-tuning performance based on real-time metrics. The goal is to reduce human intervention in setup, monitoring, and scaling decisionsHow is Condense better than using any fully managed kafka platform or building on top of Kafka/Flink myself?
Condense removes the complexity of self-hosted Kafka or assembling a Flink + Kafka + Prometheus stack. It provides an end-to-end, production-ready system with built-in observability, no-code/low-code builder, AI agents, and vertical-specific components, all managed in your cloud with optimized costIf we deploy in our own cloud (BYOC), how much does Zeliot actually control or access?
Zero data leaves your cloud. Condense runs within your cloud account (AWS, GCP, or Azure), and Zeliot has no default access to your data. You maintain complete control, which supports compliance and data residency requirementsWhat happens under the hood when traffic spikes or my pipeline lags?
Condense’s AI agents monitor backpressure, lag, and system health. If a spike occurs, the platform auto-scales Kafka and connectors based on forecasts before bottlenecks disrupt flow. You don’t need to manually tune partitions or rebalance workloadsIs this just a prototype tool, or is Condense really used in production at scale?
This is production-grade. One of India’s leading vehicle OEMs migrated real-time event streams to Condense and now handles 1 Gbps peak load and 350 MBps sustained throughput fully managed, 99.95% SLA, zero cloud provider lock-inHow is Condense different from managed Kafka providers?
Condense goes beyond managed Kafka by combining Kafka infrastructure, connectors, transforms, observability, governance, AI-assisted development, and streaming operations into one unified platform.Can developers build custom streaming applications on Condense?
Yes. Condense includes an AI-driven IDE, custom applications framework, Git integration, and support for building custom connectors and transformsIs Condense suitable for enterprises with compliance and data sovereignty requirements?
Yes! Since Condense runs inside your cloud through BYOC deployment, enterprises maintain ownership, control, and governance of their infrastructure and streaming data.Is Condense built on Kafka?
Yes! Condense is powered by fully managed Kafka and abstracts away operational complexity such as broker management, scaling, failovers, upgrades, security, and observability.What is Condense?
Condense is an AI-first streaming platform that unifies how real-time data pipelines are built and managed. Running as a BYOC (Bring Your Own Cloud) deployment, Condense continuously manages and scales deployments while optimizing costs, creating a closed intelligent loop where pipelines are built faster, run autonomously, and continuously adapt to live dataCan I use Apache Spark with Condense?
Yes. Condense integrates with Apache Spark for advanced analytics, batch processing, and machine learning workloads. Real-time streams processed in Condense can seamlessly feed Spark jobs for downstream analytics or hybrid streaming-batch use casesCan I deploy Condense without using a cloud marketplace?
Yes. Condense can be deployed directly, without going through a cloud marketplace. This is ideal for enterprises with custom procurement, private cloud setups, or strict governance requirements. But required to contact Condense TeamDoes Condense offer a free tier or trial?
Yes. Condense provides a free trial that allows teams to explore core platform capabilities, deploy connectors, and build real-time pipelines before moving to productionWhat are industry connectors in Condense?
Industry connectors are pre-built, domain-specific connectors and transformations designed for vertical use cases such as mobility, logistics, automotive, and IoT. These connectors accelerate time-to-value by reducing custom integration and stream logic effortWhat AI agents are integrated into Condense?
Condense includes built-in AI agents that assist across the lifecycle of real-time data systems:- Pipeline creation and configuration
- Stream logic validation and testing
- Continuous monitoring and anomaly detection
- Capacity forecasting and autoscaling recommendations
These agents reduce manual effort and help teams operate streaming systems reliably at scale.
What exactly is Vapr’s role as a “Supervisor”?
Vapr acts as an autonomous routing interface. It takes your technical intent and delegates it to a hierarchical workforce. Simple requests go to L1 specialists for immediate action, while complex tasks go to L2 agents for multi-step planning and logical design.How does the Kafka Agent simplify data retrieval?
It automates the manual process of checking topics and schemas. You can instantly list topics, get the last message for validation, check consumer group details, and retrieve schema metadata without running manual CLI commandsCan Vapr diagnose system failures?
Yes. The Grafana Monitoring Agent is built for Root Cause Analysis (RCA). It fetches the latest alerts and uses tools like ExecuteQuery and GetConnectorLogs to diagnose exactly why an alert occurred.What can the Kubernetes Agent do currently?
Yes. The Grafana Monitoring Agent is built for Root Cause Analysis (RCA). It fetches the latest alerts and uses tools like ExecuteQuery and GetConnectorLogs to diagnose exactly why an alert occurred.How does Vapr handle complex coding tasks?
Vapr uses a Sequential Thinking agent to plan complicated tasks step-by-step. The Coding Agent then generates the code, creates the necessary files/folders, and the Testing Agent generates test cases for validationDoes Vapr integrate with my existing Git workflow?
Yes. You can pre-select GitHub or GitLab. Vapr can create repositories, fork projects, manage branches, and push code changes or Pull/Merge Requests directly to your namespaceIs Vapr capable of making changes to my infrastructure?
Currently, the K8s and Kafka agents are primarily focused on Read/Retrieval (L1). However, the Coding Agent can create and update files in your repositories to facilitate changes through your existing CI/CD pipelinesWhat “Coming Soon” features should I expect for Kafka?
Future updates will move the Kafka Agent from retrieval to management, including the ability to CreateTopic, UpdateTopic, and Publish Messages directly to topics in various formats.Will Vapr be able to manage Kubernetes resources directly?
Yes. The roadmap includes full CRUD (Create, Read, Update, Delete) capabilities for Kubernetes Users, Secrets, Services, Jobs, and Config MapsHow does Vapr ensure it doesn’t perform critical actions by mistake?
For multi-step tasks, Vapr uses its Planning Agent to create a transparent, step-by-step execution path. It is designed to keep you “in the supervisor’s seat,” ensuring actions align with your exact intentIs there a safety mechanism for autonomous actions?
Yes, Vapr uses a verified approval loop to ensure total system safety. It ensures that no agent acts alone; every technical resolution is cross-referenced against your system’s live state and requires your final review and approval before any executionWhat specialized agents are available in the workforce?
The workforce includes a team of experts: the Kafka Agent for streams, the Kubernetes Agent for infrastructure, the Grafana Agent for diagnostics, and the Coding Agent for logical builds. Each is designed to handle specific layers of your data lifecycle autonomously and report back to VaprHow do I access and control the AI agents?
You can launch the interface by clicking “Ask Vapr” or using the Command + K shortcut to type your intent in plain English. Vapr automatically analyzes your request, identifies the necessary agents, and presents a transparent plan for you to review and approve before deploymentWhat is Vapr and how does it manage AI agents?
Vapr is an autonomous AI supervisor and intelligent routing interface within the Condense platform. It manages a hierarchical workforce where simple tasks are routed to L1 specialist agents for immediate action, while complex requirements are delegated to L2 agents for multi-step planning and logical designWhat is the core benefit of using AI Agents in Condense?
AI Agents in Condense allow you to build code, manage clusters, and audit streams by turning complex technical intent into finished results. A single supervisor coordinates expert agents to deliver production-ready systems in seconds, reducing boilerplate code and manual infrastructure bottlenecksHow does Vapr coordinate between different specialized agents?
Vapr acts as a centralized command layer for your infrastructure. You provide a high-level goal, and Vapr orchestrates the appropriate mix of L1 specialists and L2 architects by delegating specific technical responsibilities to each expert agentHow do agents share information during a task?
Agents exchange data in real time through Vapr, allowing shared context across tools and workflows. For example, a Grafana alert can automatically trigger a Kubernetes scaling action or a Git update because agents continuously communicate findings back to the central supervisorIs there a safety mechanism for autonomous actions?
Yes. Vapr uses a verified approval loop to ensure system safety and operational transparency. No agent executes actions independently. Every technical resolution is validated against the live system state and requires final user review and approval before executionWhat specialized agents are available in the workforce?
The workforce includes several specialized agents, including:- Kafka Agent for stream operations
- Kubernetes Agent for infrastructure management
- Grafana Agent for diagnostics and monitoring
- Coding Agent for application logic and development
Each agent is designed to autonomously handle a specific layer of the data lifecycle and report results back to Vapr, with additional specialized agents continuously being added to expand the workforce capabilities