Step 01
Bring sources into one searchable system
Add links or pasted text, tag the right client, and make that source available for retrieval across the workspace.
Flagship Product / System Project
contentDB
contentDB stores customer conversations, research, and content in one place so you can query it through MCP in Claude, ChatGPT, or the contentDB web chat while you research, write, or update an article.
Open the live appPrivate beta, free for now.
How it works
contentDB is a workspace database where you can add your company's high-value content from URLs or pasted texts, including podcasts, transcripts, SME-led articles, ebooks, reports, surveys, LinkedIn posts, Reddit threads, and customer chats or calls. You can then query that material from the contentDB web app or from connected AI clients through MCP while writing or doing research.
Step 02
Search with quote context
Find exact moments, surrounding context, and client-tagged material instead of isolated snippets.
Step 03
Turn that stored research into structured, source-grounded answers you can write from.
ChatCoworkCode
holla. i am writing an article about how to get retainer clients for ManyRequests. based on the contentDB mcp, are there any context I can lead with? e.g. productized agency founders who used this retainer model to scale their business, the pain point they had, and how they solved it. let’s start with the pain point they usually faced/had. i am looking for pain/friction points when it comes to getting retainer clients.
✓
Let me search the ContentDB for relevant pain points around retainer clients.
Claude gives a response from your database first, which helps keep the answer grounded and avoids hallucination.
Problem it solves
When writing for clients or your company, useful context is usually scattered across many links, docs, calls, and transcripts. You'd have to spend time on Google searches, interview SMEs, or comb through existing podcasts and reports to find relevant material.
If you rely on Google search or generic AI output, the writing often becomes weak or repetitive. contentDB keeps that context in one place so you can pull relevant quotes, supporting detail, and customer wording quickly via Claude MCP to improve the credibility of your work.
Who it's for
Content strategists, B2B SaaS marketers, writers in general, and agencies with multiple clients.
My role
I built it end-to-end: product direction, UX, data model, ingestion flows, transcript processing, pain-point extraction, semantic retrieval, chat workflows, MCP integrations, multi-tenant client isolation, and deployment setup.
Key features
- Add content from URL or pasted text, including articles, YouTube, LinkedIn, Reddit, podcasts, transcripts, and reports.
- Store customer conversations and transcripts in a structured format with segments, timestamps, and speaker metadata when available.
- Extract pain points and retrieve quote context before and after the match instead of isolated snippets.
- Search and chat from the web app with source-grounded responses.
- Use the same workspace context inside Claude, ChatGPT, and Gemini-compatible tools through MCP.
- Filter by client inside one workspace so answers stay scoped to the correct account.
What makes it different
- Works both as a standalone web product and as an MCP-connected system inside external AI tools.
- Returns source-grounded answers with contextual quotes from your database, not generic AI summaries.
- Built for agency workflows where multiple clients share one workspace but still need clean separation.
Stack
Next.jsNode.js / ExpressSupabase (Postgres/Auth)OpenAI (embeddings + extraction)Gemini (web chat generation)MCP SDKRenderVercel
Why it matters
It makes writing workflows faster and more specific. Instead of starting from generic AI output, I can pull real context from a client's content and customer language from shared calls, then use that material to produce stronger, more credible content.
Use contentDB
contentDB is live in private beta and currently free to use. The product works as a standalone web app and as an MCP-connected system for AI-assisted research and writing.