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The Golden Suite is built so each tool stands alone but composes into a single pipeline. You can pick just the piece you need, or run the full chain end to end.

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The pipeline flow

1

InferMap aligns schemas

Auto-maps messy source columns to a known target schema with confidence scores and human-readable reasoning.
2

GoldenCheck profiles and validates

Discovers quality rules from the data itself: encoding, format, nullability, anomalies.
3

GoldenFlow standardizes

Normalizes phone numbers, dates, addresses, and categorical spelling with 92 built-in transforms.
4

GoldenMatch deduplicates

Blocks, scores, clusters, and synthesizes golden records using fuzzy, exact, probabilistic, and LLM scoring.
5

GoldenPipe orchestrates

Runs the whole chain with adaptive logic. It skips transformation if no issues are found and explains every decision.
6

GoldenAnalysis reports

Runs read-only analyzers over any stage’s outputs — match rates, cluster distribution, quality rollups — with cross-run trend and regression detection.

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Polyglot by design

The same engine is implemented across languages so you can run it wherever your data lives. Every surface is exercised in CI: the Python test matrix (3.11 to 3.13), the TypeScript parity suite (scorer outputs matched to four decimals), and the Rust extension crates. The Postgres extension is built and smoke-tested against PostgreSQL 15, 16, and 17, the native acceleration kernels are parity-checked against the pure-Python path, and per-crate Rust line coverage is measured in CI.

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AI-native surface

Every package ships an MCP server; the service-shaped packages also ship a REST API and an agent surface. Across the suite there are ~110 MCP tools (GoldenMatch alone has 69). The AutoConfigController is visible from every interface: the web ControllerPanel, the TUI (Ctrl+A), the CLI, REST endpoints, Postgres functions, DuckDB UDFs, and MCP/agent tools. Add the remote MCP server to Claude Desktop or Claude Code:

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Repository layout

The suite is a single monorepo:

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Production paths

  • Postgres sync and daemon mode for continuous deduplication.
  • Review queues for human-in-the-loop correction.
  • dbt integration for warehouse-native pipelines.
  • GitHub Actions for pull-request data-quality gates.
  • Airflow DAGs (drop-in examples, TaskFlow API, Airflow 2.7+).
  • The Rust extension layer for matching directly inside Postgres or DuckDB.