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.
HOW IT WORKS
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.
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01 - 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.
02 - 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.
03 - 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. Business 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.
Drag-and-drop canvas
Gemini integration
GKE deployment
Multi-agent orchestration
BigQuery pushdown
No Python required
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.
Gemini & Vertex AI
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Embed Gemini models directly into agent workflows for LLM-powered reasoning, generation, and decision steps — no prompt engineering infrastructure needed.
Models + LLM
BigQuery
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Push data transformation and analytics workloads directly into BigQuery — keeping processing close to the data for performance, cost efficiency, and scale.
Analytics + Scale
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
GKE / Cloud Run
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Deploy production agents to Google Kubernetes Engine or Cloud Run with full horizontal scaling, observability, and enterprise access controls built in.
Deployment + Scale
AlloyDB & Vector DBs
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Use AlloyDB for operational and agent-related data, alongside vector database integrations for embedding storage and semantic retrieval in RAG workflows.
Storage + Retrieval
KEY CAPABILITIES
Built for production agentic AI
Six core capabilities that make Sparkflows the complete platform for building, running, and governing enterprise agents on Google Cloud — from first design to scaled deployment.
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Visual no-code / low-code Agentic Designer
Drag-and-drop agent creation for business and technical teams alike. Combine data wrangling, ML, and LLM nodes into complete agent workflows without engineering overhead.
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Dual-engine execution: Low Latency & Big Data
Run agents across real-time online workloads and high-volume batch pipelines from the same platform — with both a Low Latency Engine and Big Data Engine available per use case.
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Gemini-based orchestration & A2A communication
Coordinate multiple agents with Gemini-powered orchestration, structured execution across workflows, and A2A communication protocols for interoperable, modular systems.
Governance, RBAC & agent registries
Enterprise-ready access controls, audit logs, and reusable agent registries let IT and governance teams enforce standards while business teams move fast.
PLATFORM FEATURES
Everything your enterprise needs
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.
Manufacturing
Demand Forecasting Agent
Smart Sourcing Advisor
Order Issue Resolution Agent
Shift Scheduling Optimization
Supply Chain
Intelligent Supply Chain Agent
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Smart Sourcing Advisor
Recommendation Agent
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Order Issue Resolution Agent
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Sales & CRM
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AI Assistant Agent
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Leads Response Agent
Technician Ticket Allotment
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Document Intelligence Assistant
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Healthcare
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Patient Triage & Intake Agent
Care Intelligence Agent
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Clinical Workflow Automation Agent
Consumer Engagement Navigation
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Finance
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Financial Statement Analyzer
Duplicate Invoice Checker
Attrition Prediction Agent
Credit Risk Scoring Agent
GKE-native deployment & scaling
Deploy agents as Kubernetes workloads on GKE with full horizontal scaling, rolling updates, health monitoring, and cost management — all from the Sparkflows dashboard.
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Kubernetes-native agent packaging
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Horizontal scaling on demand
-
Real-time monitoring and alerting
CONNECTIVITY
80+ connectors.
Any cloud, any source.
Connect to any data source across any cloud or enterprise system — zero custom code required.
Google Cloud
AWS
Azure
Enterprise
Streaming & APIs
BigQuery
Dataproc
AlloyDB
Cloud Storage
Pub/Sub
Spanner
Firestore
S3
RedShift
RDS
Glue
Kinesis
DynamoDB
Athena
Blob Storage
Synapse
SQL DB
DataLake
Cosmos DB
Event Hub
Oracle
Salesforce
SAP
Snowflake
MySQL
PostgreSQL
MongoDB
Salesforce
JDBC
Kafka
Flink
gRPC
WebSocket
GraphQL
REST APIs
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.
30+
Classification
Predict categories and binary outcomes in agent decision steps
30+
Regression
Continuous value prediction for pricing, scoring, and forecasting
12+
Clustering
Unsupervised grouping for segmentation and anomaly detection
10+
Forecasting
Time-series and demand forecasting for operational agents
10+
Deep Learning
Neural networks for complex vision, NLP, and pattern tasks
BUSINESS VALUE
From fragmented experiments
to an agentic operating model
Sparkflows includes integrated ML that can be dropped directly into any agent workflow
— from feature engineering to deep learning — without separate MLOps infrastructure.
10×
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
What teams build with
Sparkflows on GCP
From operational automation to intelligent analytics — across every enterprise domain.
Predictive maintenance agents
Fraud detection pipelines
Supply chain demand forecasting
Customer churn prediction
HR analytics agents
Document intelligence
Real-Time Decisioning
Inventory optimisation
RAG-powered knowledge agents
Document intelligence
Quality control automation
Sales forecasting agents
Multi-agent workflows
Compliance monitoring
Pricing optimisation