Platform / Agentic Engine

SwitchIE. The Agentic Engine for Data Engineering.

Q: What is SwitchIE?

SwitchIE is an autonomous agentic engine that discovers, converts, validates and compiles enterprise data pipelines — from 15 to 30 legacy tools into modern platform-native code with zero hallucination.

Q: How does SwitchIE achieve zero hallucination?

SwitchIE uses deterministic, rule-governed compilation validated against the target grammar. Output is 100% syntax-checked — the engine does not guess. It runs on MEDHA, a private small-language-model.

Q: What compilation targets does SwitchIE support?

SwitchIE compiles to PySpark, Spark SQL, Delta Live Tables, Snowflake SQL / dbt, Fabric notebooks, BigQuery / Dataform, Redshift / Glue, and Informatica IDMC.

An autonomous engine that discovers, converts, validates and compiles enterprise data pipelines — from 15 to 30 legacy tools into modern platform-native code.

DAG generationmulti-target compilationdeterministic, syntax-validated outputzero hallucination

15–30

Legacy tools reasoned over per estate

Hallucination (deterministic compilation)

100%

Syntax-validated target code

95%+

Conversion automated end-to-end

System Architecture

From Raw Estate to Target Code.

SwitchIE reasons over a knowledge graph of the enterprise's real metadata, routes work through a private small-language-model, executes autonomous agents, and compiles validated code for the target platform.

The SwitchIE Compilation Pipeline

Layer 01

Data Layer

Sources, schemas, ETL, BI metadata

Layer 02

Knowledge Graph

RDFS / OWL ontology & lineage

Layer 03

MEDHA SLM Router

Private, CPU-only, ~50ms

Layer 04

SwitchIE Agents

Convert · auto-fix · validate

Layer 05

Target Code

PySpark · SQL · DLT · others

Deterministic by construction — every stage is rule-governed and syntax-validated. Nothing reaches the target on probability alone.

What the Engine Does

Autonomous, Not Assistive.

DAG generation

Builds the pipeline graph

SwitchIE parses legacy jobs and reconstructs the dependency DAG automatically — inferring order, joins and transformation flow from source logic rather than manual redesign.

Multi-target compilation

One model, many targets

A decoupled reader and writer normalize the source once, then compile to the chosen platform. Re-targeting is a configuration, not a rewrite.

Deterministic translation

Zero-hallucination by design

Conversion is rule-governed and validated against the target grammar. Output is 100% syntax-checked — the engine does not guess.

Graph & MCP governance

Grounded in real metadata

An RDFS/OWL knowledge graph and Model Context Protocol interface keep every decision anchored to the enterprise's actual lineage — the foundation for GraphRAG-governed retrieval.

Compilation Targets

PySparkSpark SQLDelta Live TablesSnowflake SQL / dbtFabric notebooksBigQuery / DataformRedshift / GlueInformatica IDMC

MEDHA: private, deterministic, CPU-only.

SwitchIE runs on MEDHA, DataSwitch's private small-language-model, with Trident for heavier reasoning. Both execute in your environment — air-gapped deployable, at zero token cost. Source schemas and code never leave your walls.

See SwitchIE on your estate.

Run the engine against a representative sample and get a real automation rate — before you commit to anything.