GCP | Sparkflows

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

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.

DEPLOY

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.

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

Alloy DB & 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