# Golden Suite

> A polyglot data-quality and entity-resolution toolkit. Zero-config, AI-native, MIT-licensed.

## Docs

- [Architecture](https://docs.bensevern.dev/docs/concepts/architecture.md): How the five Golden Suite tools compose into one pipeline across Python, TypeScript, Rust, dbt, and GitHub Actions.
- [Entity resolution](https://docs.bensevern.dev/docs/concepts/entity-resolution.md): The core concepts behind deduplication and record linkage: blocking, scoring, clustering, and survivorship.
- [Scale envelope](https://docs.bensevern.dev/docs/concepts/scale-envelope.md): Pick the right backend for your row count, and avoid the block-size failure modes that dominate ER performance.
- [SQL extensions](https://docs.bensevern.dev/docs/extensions/sql.md): Run GoldenMatch fuzzy matching directly inside PostgreSQL and DuckDB.
- [GoldenCheck CLI](https://docs.bensevern.dev/docs/goldencheck/cli.md): Commands, key flags, domain packs, and the goldencheck.yml config format.
- [GoldenCheck integrations](https://docs.bensevern.dev/docs/goldencheck/integrations.md): Run GoldenCheck as dbt tests and as a GitHub Action that gates pull requests on data-quality regressions.
- [Native acceleration & deep profiling](https://docs.bensevern.dev/docs/goldencheck/native.md): GoldenCheck's optional Rust/Arrow runtime and the deep-profiling checks it powers: composite keys, functional dependencies, fuzzy values, approximate-FD violations, plus --deep, referential integrity, and freshness.
- [GoldenCheck overview](https://docs.bensevern.dev/docs/goldencheck/overview.md): Zero-config data-quality scanning that discovers rules from your data instead of making you write them.
- [GoldenFlow CLI](https://docs.bensevern.dev/docs/goldenflow/cli.md): Commands, flags, and the goldenflow.yaml config format.
- [GoldenFlow overview](https://docs.bensevern.dev/docs/goldenflow/overview.md): Standardize, reshape, and normalize messy data with 76 built-in transforms across 11 categories.
- [Performance](https://docs.bensevern.dev/docs/goldenflow/performance.md): How GoldenFlow stays fast: vectorized Polars fast paths with a per-row fallback, and an optional Arrow-native Rust kernel for phone normalization.
- [ER Agent](https://docs.bensevern.dev/docs/goldenmatch/agent.md): GoldenMatch as an autonomous entity resolution agent: A2A protocol, 31 skills, confidence-gated review queue, and Python API.
- [GoldenMatch API quick reference](https://docs.bensevern.dev/docs/goldenmatch/api-quick-reference.md): Practical examples for the most-used surface. Authoritative type signatures live in goldenmatch/_api.py and goldenmatch/config/schemas.py.
- [Auto-config](https://docs.bensevern.dev/docs/goldenmatch/auto-config.md): How the introspective AutoConfig controller detects column types, picks scorers and blocking, and iterates until it converges.
- [Backends and scale](https://docs.bensevern.dev/docs/goldenmatch/backends-and-scale.md): The Polars, chunked, DuckDB, and Ray backends, and the measured scale numbers for each.
- [Blocking Strategies](https://docs.bensevern.dev/docs/goldenmatch/blocking.md): Reduce the O(N²) comparison space with GoldenMatch's 8 blocking strategies: static, adaptive, sorted neighborhood, multi-pass, ANN hybrid, ANN, canopy, and learned.
- [GoldenMatch CLI](https://docs.bensevern.dev/docs/goldenmatch/cli.md): Every GoldenMatch command and the key flags for the dedupe pipeline.
- [Configuration](https://docs.bensevern.dev/docs/goldenmatch/configuration.md): Full YAML reference for GoldenMatch: matchkeys, blocking, golden rules, standardization, validation, LLM scorer, Learning Memory, output, and backends.
- [Data-quality–aware matching](https://docs.bensevern.dev/docs/goldenmatch/data-quality.md): How GoldenMatch uses GoldenCheck's data-quality signal to improve entity-resolution results, recall, precision, and trust — via fail-open, opt-in bridges.
- [Domain Packs](https://docs.bensevern.dev/docs/goldenmatch/domain-packs.md): Built-in and custom YAML rulebooks that extract structured fields from unstructured product descriptions and other domain-specific text.
- [Evaluation](https://docs.bensevern.dev/docs/goldenmatch/evaluation.md): Measure matching accuracy against ground truth and enforce quality gates in CI/CD pipelines.
- [Identity Graph](https://docs.bensevern.dev/docs/goldenmatch/identity-graph.md): Durable, queryable identity graph with stable entity IDs, evidence edges, and a consistent JSON view across Python, SQL, REST, MCP, A2A, and the web UI.
- [Installation](https://docs.bensevern.dev/docs/goldenmatch/installation.md): Install GoldenMatch from PyPI, Docker, or as a PostgreSQL/DuckDB extension. Optional extras add embeddings, LLM scoring, database sync, and more.
- [Learning Memory](https://docs.bensevern.dev/docs/goldenmatch/learning-memory.md): Persist steward corrections, unmerge decisions, and LLM votes across runs so the same false positive never comes back.
- [LLM Integration](https://docs.bensevern.dev/docs/goldenmatch/llm.md): Use GPT-4o-mini or Claude to score borderline pairs that fuzzy matching alone cannot resolve, with budget caps, model tiering, and iterative calibration.
- [MCP Server](https://docs.bensevern.dev/docs/goldenmatch/mcp.md): Connect GoldenMatch to Claude Desktop, Claude Code, and other MCP clients — 54 tools for autonomous entity resolution, data inspection, Learning Memory, and Identity Graph.
- [GoldenMatch overview](https://docs.bensevern.dev/docs/goldenmatch/overview.md): Zero-config entity resolution for Python and TypeScript: fuzzy, exact, probabilistic, and LLM scoring with golden-record synthesis.
- [Pipeline](https://docs.bensevern.dev/docs/goldenmatch/pipeline.md): GoldenMatch's 10-step pipeline from raw files to golden records, with per-step API references.
- [Privacy-preserving linkage](https://docs.bensevern.dev/docs/goldenmatch/pprl.md): Match records across organizations without sharing raw data, using Bloom-filter PPRL.
- [Python API](https://docs.bensevern.dev/docs/goldenmatch/python-api.md): Complete reference for all 106 symbols exported by GoldenMatch from a single import.
- [GoldenMatch quickstart](https://docs.bensevern.dev/docs/goldenmatch/quickstart.md): Dedupe a file, match two files, write golden records, and configure matchkeys in Python and TypeScript.
- [Reference Data](https://docs.bensevern.dev/docs/goldenmatch/reference-data.md): The five bundled reference-data packs that auto-config picks up automatically: surnames, given names, business, addresses, and NAICS industry codes.
- [REST API](https://docs.bensevern.dev/docs/goldenmatch/rest-api.md): Local HTTP server for real-time matching, cluster browsing, and data steward review.
- [Scoring](https://docs.bensevern.dev/docs/goldenmatch/scoring.md): All GoldenMatch scoring methods: exact, fuzzy, probabilistic, LLM, embedding, and parallel scoring.
- [Streaming & Incremental](https://docs.bensevern.dev/docs/goldenmatch/streaming.md): Single-record matching, micro-batch streaming, and CLI-based incremental matching against existing data in real time.
- [Interactive TUI](https://docs.bensevern.dev/docs/goldenmatch/tui.md): Gold-themed terminal UI for GoldenMatch: 8 tabs for data profiling, configuration, match review, golden records, active learning, export, AutoConfigController telemetry, and Learning Memory corrections.
- [TypeScript API](https://docs.bensevern.dev/docs/goldenmatch/typescript.md): GoldenMatch as an npm package with full feature parity with the Python toolkit: edge-safe core, Node-only additions, and all scoring, blocking, and golden-record strategies.
- [GoldenPipe overview](https://docs.bensevern.dev/docs/goldenpipe/overview.md): The orchestrator that chains data-quality checking, transformation, and deduplication into a single adaptive pipeline.
- [Golden Suite](https://docs.bensevern.dev/docs/index.md): A polyglot data-quality and entity-resolution toolkit. Zero-config, AI-native, MIT-licensed.
- [InferMap overview](https://docs.bensevern.dev/docs/infermap/overview.md): An inference-driven schema mapping engine that aligns messy source columns to a target schema with confidence scores and reasoning.
- [Quickstart](https://docs.bensevern.dev/docs/quickstart.md): Deduplicate a CSV in 30 seconds, then run the full pipeline.
- [ER vendor comparison](https://docs.bensevern.dev/docs/reference/vendor-comparison.md): How GoldenMatch compares to other entity-resolution engines across OSS, identity-graph, cloud-managed, and enterprise MDM tiers.