By the time last night’s batch tells you a payment failed, a machine overheated, or a user abandoned checkout, the moment to do anything about it is gone. Most teams don’t need “real time” everywhere — they need it in the three or four places where a decision made an hour late is worth nothing.
We build those paths on Kafka, Flink and Spark Structured Streaming, and we build them for the messy reality: events arriving out of order, a device that was offline for an hour, a consumer that falls behind during a traffic spike, a deploy that has to happen without losing a single message. Exactly-once semantics, event-time windows, backpressure handling, and replayable topics — deployed on your cloud, with dashboards that move in seconds instead of hours.
Key Features
Kafka / Flink / Spark Streaming
Exactly-Once Delivery
Event-Time Windowing
Sub-Second Latency
Schema Registry & Evolution
Backpressure Autoscaling
Topic Replay & Reprocessing
Real-Time Dashboards
Key Advantages
One Stream, Many Consumers
The same pipeline feeds dashboards, alerts, ML features and AI agents. You build ingestion once, not once per use case.
Act While It Still Matters
Event to decision in seconds — fraud flags, stock-outs, SLA breaches and churn signals caught inside the window where action changes the outcome.
Exactly-Once, Not "Probably Once
Checkpointing and transactional sinks, so a restart never double-counts a payment or fires an alert twice.
Survives the Spike
Backpressure-aware consumers and autoscaling, so peak day traffic behaves like an ordinary Tuesday.
Late and Out-of-Order Data Handled
Event-time windows and watermarks mean a device that was offline for an hour lands in the right bucket — not today's.
Replay Any Point in History
Retained topics let you fix a bug and re-process the last week — instead of accepting that the numbers are wrong forever.
Trusted by Innovators Across Industries
The data engineering work happens underneath: pipelines that are clean, tested, and production-grade, so the AI layer on top actually holds up. That's the difference between a chatbot that impresses in a demo and an agent your team can put into a real workflow.
What Our Clients Say
Trusted by global enterprises and fast-growing startups to deliver reliable, scalable, and intelligent data solutions.
Frequently Asked Questions
Here are some of the most common questions we receive from businesses exploring our solutions.
What kind of data do you work with?
We handle structured, semi-structured, and unstructured data from various sources, including databases, APIs, files, IoT devices, and streaming platforms.
Can you work in our cloud environment?
Yes, with our Bring Your Own Cloud (BYOC) model, we build and manage data pipelines securely within your existing cloud infrastructure.
What visualizations or reports can you deliver?
We create interactive dashboards and visual reports using tools like Power BI, Tableau, or custom-built frontends tailored to your KPIs.
Is your service scalable as our data grows?
Yes, all our solutions are cloud-native and built to scale with your data volume, user base, and business complexity.
See Our Work in Action
Watch how we transform raw data pipelines into actionable dashboards, with AI agents enabling real-time insights.
AI Agents & LLM Apps Built to Run in Production
Most AI agents fail quietly - on stale data, broken pipelines, or retrieval that returns the wrong context. GKCodeLabs builds AI agents, RAG systems, and LLM applications on data infrastructure we engineer ourselves - so what you ship holds up under real traffic, not just demos.