Your question lands as plain English — "what's our remote expense policy?" — and your database fires back an answer pulled from a document that says "home office reimbursement." Not one matching word. Just matching meaning. That's Retrieval-Augmented Generation. We convert your databases, documents, and scattered files into pgvector embeddings so Claude searches by semantic intent, not 1998-era keyword matching. Your team asks natural questions. The AI cites specific passages from your actual data — no guessing, no hallucination. When your 22-year veteran retires Friday, their institutional knowledge doesn't walk out the door with them, because it's indexed, searchable, and cited. We call this your Second Brain. Every policy, every process, every hard-won lesson from the past decade becomes accessible to anyone who can type a question. The knowledge stops living in heads and email drafts. It lives in your system, where turnover can't kill it.
Onde os projetos falham
Conformidade
Document RAG
Database Natural Language
Multi-Source Ingestion
Citation and Verification
Access Controls
Continuous Ingestion
O que construímos
Writing SQL just to answer a business question creates friction so high most questions never get asked
Ten thousand documents sitting in SharePoint are invisible to anyone who doesn't already know the exact folder path
Keyword search fails the moment someone writes 'workforce restructuring' and you search 'staff reduction' — same meaning, zero results
New hires spend six months learning where information lives before they can even start learning the information itself
Teams copy sensitive data into ChatGPT because there's no other way to get answers — generic responses, real security risk
Your most senior employee retires and decades of unwritten expertise vanishes the day they leave
Nosso processo
Data Audit
Ingestion Pipeline
Search Interface
Access Controls
Launch + Tune
Perguntas frequentes
What is RAG?
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.
What types of data can you ingest?
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.
How accurate is semantic search?
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.
How much does RAG development cost?
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.
Do you store my data?
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.
How long does RAG setup take?
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.
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.