# Problem Addressed > SourceSync.ai is a managed RAG (Retrieval-Augmented Generation) infrastructure platform that continuously syncs data from 15+ external sources into AI-ready knowledge bases. - Skill: `tools-only/problem-addressed-16` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add tools-only/problem-addressed-16` - Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/problem-addressed-16/raw - Safety review: pending (external: skill-scanner PASS, skillspector CAUTION) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tools-only (https://skillmd.com/u/tools-only) - Updated: 2026-09-29 - Page: https://skillmd.com/skills/tools-only/problem-addressed-16 --- # SourceSync.ai **Research Date**: 2026-02-23 **Source URL**: **GitHub Repository**: (MCP server) **API Reference**: **Version at Research**: SaaS platform (no version; REST API v1) **License**: Proprietary SaaS (BYOC — Bring Your Own Cloud) --- ## Overview SourceSync.ai is a managed RAG (Retrieval-Augmented Generation) infrastructure platform that continuously syncs data from 15+ external sources into AI-ready knowledge bases. Teams configure ingestion once — connecting cloud storage, SaaS wikis, websites, or raw text — and SourceSync automatically cleans, chunks, and embeds content, keeping it fresh as sources change. It exposes the resulting knowledge base via a REST API with semantic and hybrid search, and through an MCP server for direct AI assistant integration. **Core Value Proposition**: Eliminate custom RAG pipelines by providing auto-syncing, multi-source knowledge bases accessible over a standard API — with BYOC storage and embeddings so no data ever touches SourceSync's infrastructure. --- ## Problem Addressed | Problem | Solution | |---------|----------| | Building RAG ingestion pipelines requires significant custom engineering | Pre-built connectors for 15+ sources (cloud storage, SaaS, websites) eliminate bespoke ingestion code | | AI knowledge becomes stale as source documents are updated | Automatic sync keeps vector indexes fresh when upstream sources change | | Semantic search alone misses keyword-important queries | Hybrid search combines vector similarity with keyword matching, with configurable weights | | Multi-tenant AI apps require isolated knowledge per customer | Namespace + tenant ID model provides scoped data isolation without separate deployments | | Storing AI data in vendor cloud creates compliance concerns | BYOC model: file storage (S3-compatible) and vector DB (Pinecone) are customer-owned; SourceSync never retains data | | Switching embedding models requires re-indexing everything | Per-namespace embedding model config (OpenAI, Claude, Jina) allows different strategies per use case | --- ## Key Statistics | Metric | Value | Date Gathered | |--------|-------|---------------| | Pricing — Pilot | $99/month (30M chars, 30k retrievals) | 2026-02-23 | | Pricing — Pro | $299/month (150M chars, 150k retrievals) | 2026-02-23 | | Pricing — Team | $999/month (750M chars, 750k calls) | 2026-02-23 | | Enterprise | Custom / unlimited | 2026-02-23 | | Free tier | 14-day money-back on paid plans | 2026-02-23 | | Connectors (live) | Google Drive, Dropbox, OneDrive, Box, Notion | 2026-02-23 | | Connectors (coming soon) | Confluence, GitBook, S3, GCS, Azure Blob, Slack, Gmail, Salesforce, Zendesk, Intercom, SharePoint | 2026-02-23 | | Scraping integrations | Firecrawl, ScrapingBee, Jina | 2026-02-23 | | Embedding providers | OpenAI, Claude, Jina | 2026-02-23 | | MCP Server | `sourcesyncai-mcp` v1.0.11 on npm | 2026-02-23 | | Notable customer | SiteGPT.ai | 2026-02-23 | --- ## Key Features ### Namespace-Based Knowledge Isolation - Each **namespace** is a fully independent knowledge base with its own storage, vector DB, and embedding model - Namespace CRUD via REST API or MCP tools (`create_namespace`, `list_namespaces`, etc.) - Multi-tenant isolation via `X-Tenant-ID` header — single API key can serve multiple end-users with separate data ### Multi-Source Data Ingestion - **Direct ingestion**: raw text, URL lists, sitemap crawl, full-website deep crawl (configurable depth/page limits) - **Cloud storage connectors (live)**: Google Drive, Dropbox, OneDrive, Box - **SaaS connectors (live)**: Notion - **Coming soon**: Confluence, GitBook, S3, GCS, Azure Blob, Slack, Gmail, Outlook, Salesforce, ServiceNow, Zendesk, Intercom, SharePoint - All ingestion is asynchronous — `get_ingest_job_run_status` polls completion - Connector reuse: system deduplicates OAuth connections across tenants automatically ### Search - **Semantic search**: vector similarity via `topK` retrieval - **Hybrid search**: configurable `semanticWeight` + `keywordWeight` for precision/recall balance - Retrieved documents include parsed text content URLs for downstream full-text access ### Document Lifecycle Management - Filter, update metadata, delete, and force-resync individual documents - `fetchUrlContent` retrieves raw parsed text from document content URLs ### Bring Your Own Cloud (BYOC) Model - Customer provides S3-compatible bucket, Pinecone index, and embedding API keys - SourceSync processes and routes data but does not retain it - Supports enterprise security and compliance requirements ### MCP Integration - Full MCP server (`sourcesyncai-mcp`) maps all REST API operations to 28 MCP tools - Drop-in for Claude Desktop, Cursor, Windsurf, Claude Code — zero SDK required - See `research/mcp-ecosystem/sourcesyncai-mcp.md` for detailed MCP tool inventory --- ## Technical Architecture ### Data Flow ```text ┌──────────────────────────────────────────────────────────────────┐ │ SourceSync.ai Platform │ ├──────────────────────────────────────────────────────────────────┤ │ │ │ Source Connectors Processing Pipeline │ │ ┌─────────────────┐ ┌───────────────────────────┐ │ │ │ Google Drive │ │ 1. Clean & extract text │ │ │ │ Dropbox / Box │──────► │ 2. Chunk content │ │ │ │ OneDrive │ │ 3. Embed (BYOE model) │ │ │ │ Notion │ │ 4. Store in BYOC storage │ │ │ │ URLs / Sitemaps │ └───────────┬───────────────┘ │ │ │ Raw Text │ │ │ │ └─────────────────┘ ▼ │ │ ┌─────────────────────┐ │ │ Customer Infrastructure: │ Namespace Index │ │ │ ┌──────────────────────┐ │ (Customer Pinecone) │ │ │ │ S3-compatible bucket │◄───│ │ │ │ │ Pinecone index │ └──────────┬──────────┘ │ │ │ Embedding model keys │ │ │ │ └──────────────────────┘ ▼ │ │ ┌─────────────────────┐ │ │ Consumer Layer: │ REST API / MCP │ │ │ ┌──────────────────────┐ │ - semantic_search │ │ │ │ Claude / Cursor / AI │◄───│ - hybrid_search │ │ │ │ Custom app via API │ │ - getDocuments │ │ │ └──────────────────────┘ └─────────────────────┘ │ └──────────────────────────────────────────────────────────────────┘ ``` ### Namespace Configuration Stack | Layer | Customer-Provided | |-------|------------------| | File Storage | S3-compatible endpoint + credentials | | Vector Store | Pinecone API key, environment, index | | Embedding Model | OpenAI / Claude / Jina API key + model | ### Authentication Model - **API Key**: Bearer token in `Authorization` header (per subscription) - **Tenant ID**: Optional `X-Tenant-ID` header for multi-tenant data isolation - **OAuth Connections**: Per-connector OAuth flows for cloud storage / SaaS, with automatic connection reuse across tenants --- ## Installation & Usage ### REST API (No SDK) ```bash # Semantic search against a namespace curl -X POST https://api.sourcesync.ai/v1/search \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "X-Tenant-ID: tenant_XXX" \ -H "Content-Type: application/json" \ -d '{ "namespaceId": "namespace_XXX", "query": "how do I configure embeddings?", "topK": 5 }' ``` ### MCP Server (for AI assistants) ```bash # Claude Desktop / Claude Code config env SOURCESYNC_API_KEY=your_key npx -y sourcesyncai-mcp ``` ```json { "mcpServers": { "sourcesyncai-mcp": { "command": "npx", "args": ["-y", "sourcesyncai-mcp"], "env": { "SOURCESYNC_API_KEY": "your_api_key", "SOURCESYNC_NAMESPACE_ID": "your_namespace_id" } } } } ``` ### Example: Ingest a website, then search it ```bash # 1. Ingest a website (async) curl -X POST https://api.sourcesync.ai/v1/ingest \ -H "Authorization: Bearer YOUR_API_KEY" \ -d '{"namespaceId":"ns_XXX","ingestConfig":{"source":"WEBSITE","config":{"url":"https://docs.example.com","maxDepth":3}}}' # 2. Poll for completion curl https://api.sourcesync.ai/v1/ingest/job/run_XXX -H "Authorization: Bearer YOUR_API_KEY" # 3. Hybrid search when complete curl -X POST https://api.sourcesync.ai/v1/search/hybrid \ -H "Authorization: Bearer YOUR_API_KEY" \ -d '{"namespaceId":"ns_XXX","query":"getting started","hybridConfig":{"semanticWeight":0.7,"keywordWeight":0.3}}' ``` --- ## Relevance to Claude Code Development ### Applications - **Skill reference grounding**: Ingest all `SKILL.md` files and `references/` directories into a SourceSync namespace; query via MCP during skill execution for evidence-based answers - **Research directory search**: Ingest `./research/**/*.md` for semantic search across all research entries — surface relevant prior art when creating new entries - **Plugin documentation RAG**: Index plugin README files and `plugin.json` manifests for grounded plugin discovery and comparison ### Patterns Worth Adopting - **BYOC / BYOE separation**: The model of "we process, you own the data" cleanly separates vendor responsibility from data sovereignty — applicable pattern for plugin designs that handle user secrets - **Namespace isolation maps to plugin scope**: SourceSync namespaces parallel Claude Code's plugin-scoped contexts; one namespace per project keeps retrieval signals clean - **Async ingestion with polling**: `get_ingest_job_run_status` pattern is reusable for any long-running knowledge base operation in agent workflows ### Integration Opportunities - **Drop-in MCP context layer**: Add `sourcesyncai-mcp` to Claude Code's MCP server config to give all skills a live, searchable knowledge base without any RAG code - **Auto-synced research entries**: Connect GitHub repository (via webhook or scheduled job) to SourceSync, so `./research/` changes are automatically indexed and searchable - **Competitive context grounding**: Index competitor docs / changelogs in a namespace; query during skill creation to surface integration opportunities ### Comparison with Related Tools | Feature | SourceSync.ai | Microsoft GraphRAG | Local Memory (`local-memory`) | |---------|--------------|-------------------|-------------------------------| | Hosted/managed | Yes (SaaS) | No (self-hosted) | No (local) | | Auto-sync from sources | Yes | No | No | | Multi-source connectors | Yes (15+) | No (files only) | No | | Hybrid search | Yes | No (graph-based) | No | | BYOC storage | Yes (required) | No | Yes | | Knowledge graph | No | Yes | No | | Free tier | No (paid only) | Yes (open source) | Yes (open source) | | MCP server | Yes | No | No | | Multi-tenant | Yes (X-Tenant-ID) | No | No | --- ## References - [SourceSync.ai Website](https://sourcesync.ai) (accessed 2026-02-23) - [SourceSync.ai Pricing](https://sourcesync.ai/pricing) (accessed 2026-02-23) - [SourceSync.ai Connectors](https://sourcesync.ai/connectors) (accessed 2026-02-23) - [API Reference — Authentication](https://sourcesync.ai/api-reference/authentication) (accessed 2026-02-23) - [MCP Server Repository](https://github.com/scmdr/sourcesyncai-mcp) (accessed 2026-02-23) - [MCP Server on Smithery](https://smithery.ai/server/@pbteja1998/sourcesyncai-mcp) (accessed 2026-02-23) --- ## Freshness Tracking | Field | Value | |-------|-------| | Last Verified | 2026-02-23 | | Version at Verification | REST API v1 (SaaS, versioned per release) | | Next Review Recommended | 2026-05-23 | **Change Detection Indicators**: - Monitor connector roadmap for new live connectors (Confluence, GitBook, Slack are high-value) - Track pricing changes — no free tier currently; watch for freemium tier announcement - Verify vector store backend expansion beyond Pinecone - Check for SDK releases (currently REST-only with no official SDK) - Watch for self-hosted / open-core variant announcements