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.
We Build on the Best Technologies
Our expertise covers modern cloud platforms and tools that power reliable, cost-efficient, and scalable data solutions.
Our Services
From raw data to AI-driven insights — GK Codelabs delivers end-to-end, cloud-native solutions built for scale, speed, and business impact.
Batch Data Processing
We design and manage batch pipelines to process massive datasets reliably — optimized for cost, performance, and your business cadence.
Real-Time Data Processing
Ingest, process, and act on data as it happens with low-latency pipelines that keep your business informed and responsive.
ETL-as-a-Service
Extract, transform, and load data across sources with scalable, fully managed pipelines tailored to your logic — no engineering lift needed.
Voice AI Agents
Inbound/outbound calling agents (support, collections, appointment booking, lead qualification) with barge-in/interruption handling and CRM/telephony integration.
Agentic Process Automation
Multi-agent systems (LangGraph/CrewAI) that execute multi-step business workflows end-to-end (e.g., invoice-to-reconciliation, lead-to-CRM enrichment, support-ticket triage-and-resolution)
Document & Contract Intelligence Agents
Extraction, classification, and structured-data pipelines for unstructured documents (contracts, invoices, claims), feeding both databases and downstream RAG. This one is a strong bridge because it's literally "data engineering + AI" in one product.
Your AI Application - From First Call to Production
We Audit Before We Quote
No discovery theatre. In a 45-minute call we map your sources, volumes, formats and the one workflow costing your team the most hours — then we go away and look at it properly. If we don’t think we’re the right fit, we say so on the call.
You get back a written audit: where your data actually breaks, what it’s costing you, and a fixed-scope plan with a timeline and a price. It’s yours to keep, and you can hand it to another vendor if you want.
The Data Layer Goes First
This is the step almost every failed AI project skips. Teams wire an LLM to a messy source, the demo dazzles, and it collapses the moment real volume, bad records and edge cases arrive.
So we build the boring part first: ingestion, schema, deduplication, validation tests and monitoring, whichever suits your stack. By the end of week one your data is clean, versioned and observable. Everything after this is built on ground that holds.
Then We Build the Agent
Now the AI layer goes on top — a document and contract intelligence agent, a voice agent that handles inbound calls, or a multi-agent workflow that closes a loop end to end.
Every answer is grounded in your own data and cites its source, so your team can check the agent’s work instead of trusting it blindly. It plugs into the CRM, telephony, ticketing or warehouse you already run. You see working builds mid-sprint, not at the end.
We Prove It Before You Trust It
“It works on the demo file” is not a result. We run the system against your genuinely difficult cases — the scanned contract, the malformed invoice, the caller who interrupts halfway through, the day traffic triples.
You receive an accuracy scorecard with real numbers: extraction accuracy per field, hallucination and fallback rates, latency under load, and exactly where the system hands off to a human. If a number isn’t good enough, we fix it before go-live — not after.
Deployed in Your Cloud, Owned by You
It ships into your AWS, GCP or Azure account — bring-your-own-cloud by default. Infrastructure as code, dashboards, alerting, a written runbook, and a handover session with your engineers.
You own the repository and the infrastructure on day 30. No proprietary black box, no lock-in, no hostage situation. Keep us on a monthly retainer to run and improve it, or take it in-house and run it yourself. Both are fine by us.
Are You Making These Data Mistakes?
We see common challenges across industries. Here’s where businesses get stuck:
Large volumes of data but not optimized for insights.
Wasting hours on manual reporting.
Costly infrastructure setups that don’t scale.
AI projects stalled at proof-of-concept.
See Our Work in Action
Watch how we transform raw data pipelines into actionable dashboards, with AI agents enabling real-time insights.
Your AI Pilot Shouldn't Die at the Demo Stage
Twenty minutes with our engineers and you'll leave with a clear view of what's blocking your data or AI project — and what it would take to ship it. No obligation, no deck.
Trusted Data Solutions, Proven by Numbers
We take pride in the measurable value we bring to our clients.
0+
Pipelines Deployed
0+
Projects Completed
0+
Certified Experts
0M+
Monthly Active Rows
What Our Clients Say
Trusted by global enterprises and fast-growing startups to deliver reliable, scalable, and intelligent data solutions.
Industry-Specific Data Solutions
We understand that every industry has unique data challenges. Our solutions are tailor-made to deliver results that matter.
IT Service Companies
IoT
Finance
Media Agencies
SaaS
Ecommerce
Ed-Tech
Health Care
News & Events
Data Engineering Fundamentals
In the world of modern data infrastructure, two approaches dominate — Batch Processing and Real-Time (Streaming) Processing. Both are essential, but knowing when to use which can drastically impact cost,…
Learn More
Batch vs. Real-Time Data Processing — Which One Do You Need?
In the world of modern data infrastructure, two approaches dominate — Batch Processing and Real-Time (Streaming) Processing. Both are essential, but knowing when to use which can drastically impact cost,…
Learn More
Why Cloud-Native Data Engineering Is the Future
The era of on-premise data systems is fading fast. As data grows exponentially, businesses are turning to cloud-native data engineering — building systems that are agile, scalable, and designed to…
Learn MoreAI 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.