Now accepting Q2 projects — limited slots available. Get started →

Your Database Knows Everything. Your Team Can't Find Anything.

If you're a founder watching SQL queries bottleneck your ops team, you've reached the moment when data becomes a liability instead of an asset.

You have a PostgreSQL database with 5 years of business data. Or a MongoDB collection with millions of documents. Or 10,000 PDFs in a file share. Right now, getting answers requires writing SQL queries or asking someone who can. We ingest your data into pgvector embeddings and connect Claude so anyone can search your entire dataset in natural language and get answers with citations to source documents.

Get Your RAG Quote See How RAG Works

Built on a Modern, Secure Stack

Claude APIpgvectorSupabaseOpenAI EmbeddingsVercelPostgreSQLMongoDB
Social Animal

Ready to discuss your your database knows everything. your team can't find anything. project?

Get a free quote
Related Resources
Solution
Manufacturing AI Integration
Solution
Healthcare AI Integration
Solution
Logistics AI Integration
Solution
Salesforce AI Integration
Solution
Ecommerce AI Integration
Solution
Hospitality AI Integration

Frequently Asked Questions

RAG -- Retrieval-Augmented Generation -- works like this: we ingest your documents or database into vector embeddings stored in pgvector. When someone asks a question, the AI searches semantically -- by meaning, not keywords -- pulls the relevant passages, and writes an answer that cites your actual source documents. It can't hallucinate because it's not filling in blanks from training data. It's reading your stuff and summarizing what it finds.
Pretty much anything digital. PostgreSQL, MongoDB, MySQL databases. PDFs, Word docs, Excel files. Confluence, Notion, Google Docs. Emails. API data from external systems. If it's digital and you own it, we can ingest and index it.
Semantic search handles the vocabulary mismatch problem that breaks keyword search. Ask about "employee termination clauses" and it finds separation agreements and end-of-employment provisions -- different words, same meaning. And we tune retrieval for precision, because honestly, 5 highly relevant results beat 50 vague ones every time.
Simple RAG over a document library under 1,000 documents runs $3,000 to $8,000. Enterprise RAG -- multiple data sources, access controls, workflow integration -- is $15,000 to $40,000. Both scale to millions of documents as your needs grow.
Your data stays in your Supabase instance or your existing database. Embeddings are stored right alongside your data. Claude processes queries in memory without retaining your content anywhere. You control the infrastructure -- we're not holding your data hostage.
Simple document RAG typically takes 2 to 3 weeks. Multi-source enterprise RAG runs 6 to 10 weeks -- the extra time is mostly data cleaning, chunking optimization, and accuracy validation against real queries. Rushing that part is how you end up with a system that *looks* like it works but gives bad answers.
More solutions

Explore related industries

Best Wix Alternatives for 2026: Honest Picks

You outgrew Wix. Here's what actually fits your next stage.

Best Contentful Alternatives for 2026

Your Contentful bill doubled. Here's what actually replaces it.

Best Bluehost Alternatives for 2026

Your year-1 promo expired. Here's what actually beats Bluehost now.
Need enterprise scale?

200+ employee company? Complex multi-tenant, auction, or multi-location requirement? We have a dedicated enterprise capability track.

View Enterprise Hub

Get Your Quote

Most quotes delivered within 24 hours.

Or book a 30-minute call
Get in touch

Let's build
something together.

Whether it's a migration, a new build, or an SEO challenge — the Social Animal team would love to hear from you.

Get in touch →