GCP | Sparkflows Studio

GCP SERVICES

Native to the Google Cloud ecosystem

Sparkflows integrates natively with Google Cloud's core AI and data services — giving you a tightly connected stack from data to model to deployment on GCP.

Sparkflows sits on top of Google Cloud as your visual agent design and execution layer — so any enterprise team can build intelligent agents, and GCP runs them at scale, without assembling a fragmented AI stack.

Three steps from idea
to production agent on GCP

Sparkflows handles the full journey from visual agent design to Gemini-powered execution and GKE deployment — so your team stays focused on outcomes, not infrastructure.

DESIGN

Build agents visually, not in code

Use the Agentic Designer's drag-and-drop canvas to assemble intelligent workflows from data processors, ML nodes, and LLM steps. No Python,no cloud configuration required.

ENRICH

Add LLMs, ML, and any data service

Embed any LLM — Gemini, GPT, Claude, or open-source models — alongside built-in ML algorithms and enterprise data services from any cloud or on-premise system.

DEPLOY

Orchestrate and run on Google Cloud

Deploy agents to GKE with Gemini-based orchestration, multi-agent coordination, & A2A communication. Sparkflows manages runtime, scaling, and monitoring from one dashboard.

AGENTIC DESIGNER

Anyone can build. GCP runs it.

Sparkflows puts a drag-and-drop canvas in front of Google Cloud's AI and data infrastructure. Analysts, data scientists, and engineers all work from the same visual interface — Sparkflows routes processing to BigQuery, Dataproc, and Gemini automatically in the background. No more back-and-forth between data teams and cloud engineering just to update an agent. Design, enrich, deploy — all from one place.

GCP SERVICES

Native to the Google Cloud ecosystem

Sparkflows integrates natively with Google Cloud's core AI and data services — giving you a tightly connected stack from data to model to deployment on GCP.

Models + LLM

Gemini & Vertex AI

Embed Gemini models directly into agent workflows for LLM-powered reasoning, generation, and decision steps — no prompt engineering infrastructure needed.

Analytics + Scale

BigQuery

Push data transformation and analytics workloads directly into BigQuery — keeping processing close to the data for performance, cost efficiency, and scale.

Big Data + Batch

Dataproc

Execute large-scale Spark and Hadoop workloads via Dataproc for high-volume batch processing within agent pipelines — fully managed and serverless.

Big Data + Batch

Dataproc

Execute large-scale Spark and Hadoop workloads via Dataproc for high-volume batch processing within agent pipelines — fully managed and serverless.

Deployment + Scale

GKE / Cloud Run

Deploy production agents to Google Kubernetes Engine or Cloud Run with full horizontal scaling, observability, and enterprise access controls built in.

Storage + Retrieval

AlloyDB & Vector DBs

Use AlloyDB for operational and agent-related data, alongside vector database integrations for embedding storage and semantic retrieval in RAG workflows.

100+ pre-built industry solutions

Accelerate time to value with prebuilt agentic AI solutions ready to deploy and customise — across manufacturing, supply chain, finance, retail, and more.

MACHINE LEARNING

72+ ML algorithms.
All embedded in agents.

Sparkflows includes integrated ML that can be dropped directly into any agent workflow — from feature engineering to deep learning — without separate MLOps infrastructure.

10X

Faster agent development

Visual no-code design dramatically reduces the time from idea to a production-ready agent compared to writing and deploying custom AI code.

30+

Pre-wired enterprise connectors

Data teams stop building and maintaining custom integrations. Every major cloud and enterprise data source — GCP, AWS, Azure, Snowflake, SAP, and more — is connected from day one.

72+

Built-in ML algorithms

Classification, regression, clustering, forecasting, and deep learning embedded directly into agent workflows — no separate MLOps infrastructure required.

USE CASES