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Insight Dataflow · Product overview

Build & Orchestrate AI-Powered Data Pipelines from Any Source to Any Sink

No-code / low-code ETL platform with 100+ connectors, an intuitive visual designer, and a multicloud Airflow framework — structured, unstructured, ML, and streaming workflows up and running in minutes.

How it works

From connector pick to monitored DAG — in four steps

Connect, design, run on your stack, monitor in production — with AI in the loop the whole way.

Step 1

Connect sources & sinks

100+ pre-built connectors. Configuration without coding for the long-tail systems your team already runs.

  • 100+ connectors
  • No-code configuration
  • Schema discovery
Step 2

Design visually

Drag-and-drop designer with AI suggestions, inline preview, and schema validation.

  • Drag-and-drop canvas
  • AI transform suggestions
  • Inline preview + debug
Step 3

Execute anywhere

Multicloud-ready with auto-scaling compute — run on whatever engine you already operate.

  • Airflow / Databricks / Glue / RAY / Spark
  • Auto-scaling compute
  • Existing scheduler integration
Step 4

Monitor & optimize

Real-time monitoring, alerting, lineage tracking, and AI-driven optimization signals.

  • Real-time monitoring + alerts
  • End-to-end lineage
  • AI optimization recommendations
Connectors

100+ connectors across databases, clouds, and streams

Three groupings, one consistent connector layer — no per-source quirks to manage.

Databases & warehouses

Operational + analytics stores

From transactional databases to cloud warehouses — the systems that hold the data and the systems that serve it.

  • PostgreSQL · MySQL
  • MongoDB
  • Snowflake · BigQuery · Redshift
Cloud & data lakes

Object stores & lakehouses

Raw and curated object stores plus modern lakehouse table formats — one connector layer across all of them.

  • AWS S3 · Azure ADLS · GCS
  • HDFS
  • Delta Lake · Iceberg
Streams, APIs & AI

Real-time + vector + LLM

Streaming sources, SaaS APIs, vector databases, and LLM providers — the modern AI data fabric.

  • Kafka · Kinesis
  • REST & GraphQL APIs
  • Pinecone · Weaviate
  • OpenAI & LLM provider sinks
Core platform

Three platform pillars, one operating model

Unified connectivity, a designer everyone can use, and an engine layer that fits your existing stack.

Unified connectivity

One connector layer, every store

  • 100+ connectors across clouds, databases, streams
  • Structured, unstructured, and ML data
  • AWS / Azure / GCP / Kafka native
  • Secure, governed, audited data flows
No-code designer

Drag-and-drop with AI assist

  • AI-assisted transform suggestions
  • ETL, streaming & ML templates
  • Inline preview, debug, schema validation
  • Human-in-the-loop approvals
Engine-agnostic

Run on what you already operate

  • Airflow / Databricks / Glue / RAY / Spark
  • Batch, streaming, ML & GenAI workloads
  • Auto-scaling multicloud compute
  • Slot into existing schedulers
Capabilities

Eight first-class pipeline capabilities

From the visual canvas to agentic AI and lineage — the things production pipelines actually need.

Visual designer

Drag-and-drop pipeline authoring with AI-assisted transforms.

100+ connectors

Databases, warehouses, lakes, streams, APIs, vector DBs, LLMs.

Multicloud Airflow

Native DAG generation that runs the same on AWS, Azure, and GCP.

Agentic AI

Transformation suggestions, anomaly detection, and pipeline optimization.

Real-time streaming

Kafka / Kinesis with exactly-once delivery semantics.

Data lineage

End-to-end tracking from raw source to downstream consumer.

ML & GenAI pipelines

Feature engineering, inference, RAG with vector-DB integration.

Monitoring & alerts

Health tracking, SLA monitoring, intelligent alerting on signal.

Built for

Four teams. One pipeline platform.

Each role gets a tuned workflow — the same pipeline, the right view.

Data Engineering

Replace hand-coded DAGs

  • Visual builder replaces hand-coded DAGs
  • Multicloud orchestration with auto-scaling
  • Built-in schema validation & data quality
  • Version control & CI / CD support
Analytics & BI

Self-service data prep

  • Self-service no-code ETL
  • Pre-built data-prep templates
  • Automated report scheduling & dashboard feeds
  • Data freshness & SLA tracking
ML & AI

Feature pipelines & RAG

  • Feature engineering & data-prep visualization
  • RAG pipeline construction with vector-DB sinks
  • Model training & batch-inference orchestration
  • GenAI endpoint integration
Platform & DevOps

Governed, observable, cost-controlled

  • Centralized governance & access control
  • AWS / Azure / GCP multicloud deployment
  • Resource management & cost optimization
  • Audit trails & compliance reporting
Security & deployment

Your data never leaves your network

On-premise and private-cloud deployment. All processing inside your infrastructure.

In your environment

On-premise and private-cloud deployment. All processing happens within your infrastructure — no data leaves your network.

Compliance-ready

GDPR, HIPAA, SOC 2, and CCPA aligned. Built-in audit trails, encryption at rest and in transit, role-based access control.

Multicloud by design

One abstraction across AWS, Azure, and GCP. Run on Airflow, Databricks, Glue, RAY, or Spark — whichever you already operate.

Ready to build AI-powered pipelines without hand-coding DAGs?

Tell us the sources, sinks, and scheduler you already run — we’ll set up a hands-on walkthrough within 2 weeks.

Request a demo Contact sales
On-premise deployment 100+ connectors Multicloud (AWS / Azure / GCP) Enterprise support