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.
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.
What is holding your current website back?
5 years of business data, searchable only by people who write SQL.
How We Build This Right
Every safeguard, built in from Day 1.
Document RAG
We ingest your PDFs, Word docs, emails, and web pages directly into pgvector. Semantic search then finds relevant passages based on what they *mean*, not just whether the words match. And every answer comes back with citations -- specific documents, page numbers, the actual passage. You know exactly where the information came from.
Database Natural Language
Ask your database a question in plain English. The AI figures out what you're asking, translates it into the right query, pulls the data, and hands you a real answer. No SQL. No ticketing a data analyst. No waiting until Thursday.
Multi-Source Ingestion
PostgreSQL, MongoDB, Confluence, Notion, Google Docs, file shares, external APIs -- it all gets indexed into one searchable knowledge base. Your team stops asking "which system has that?" because the answer is just: this one.
Citation and Verification
Every single answer includes citations -- source document, page, the specific passage it drew from. Users can verify before they act on anything. That's not a nice-to-have, that's fundamental. Answers grounded in your actual data don't hallucinate, because the AI isn't filling gaps from its training -- it's reading your documents.
Access Controls
HR data stays visible to HR. Financial data stays with finance. Document-level and collection-level access controls mirror whatever permission structure you already have. The AI respects your org's boundaries -- it doesn't flatten them.
Continuous Ingestion
New documents get indexed automatically as they're added. Your knowledge base stays current without anyone manually re-indexing anything. Add a policy update on Monday, and it's searchable by Monday afternoon.
What We Build
Purpose-built features for your industry.
Writing SQL just to answer a business question creates friction so high most questions never get asked
Ask about customer churn and surface documents mentioning retention risk, subscriber loss, cancellation patterns -- meaning-based retrieval, not word matching
Ten thousand documents sitting in SharePoint are invisible to anyone who doesn't already know the exact folder path
Institutional knowledge persists through turnover because it lives in your searchable system, not in heads that walk out the door
Keyword search fails the moment someone writes 'workforce restructuring' and you search 'staff reduction' -- same meaning, zero results
Every answer cites specific source documents and passages -- the AI shows its work instead of fabricating plausible-sounding nonsense
New hires spend six months learning where information lives before they can even start learning the information itself
Fifty thousand documents search just as fast as five hundred -- pgvector scales to enterprise volume without hitting performance walls
Teams copy sensitive data into ChatGPT because there's no other way to get answers -- generic responses, real security risk
Embeddings live in your Supabase instance, queries process in memory -- you own the infrastructure and control where your data sits
Your most senior employee retires and decades of unwritten expertise vanishes the day they leave
RAG becomes the foundation for customer chatbots, workflow automation, and AI assistants -- one indexed dataset powers your entire AI stack
Built on a Modern, Secure Stack
Our Development Process
From discovery to launch. Quality at every step.
Data Audit
Week 1We start by cataloging your data sources, document types, and where the highest-value search use cases actually are. Then we plan the ingestion and chunking strategy before writing a single line of code. Getting this part right saves a lot of pain later.
Ingestion Pipeline
Week 2-3Next we build the data processing pipeline -- cleaning the raw data, chunking it intelligently, generating embeddings, and indexing everything. Then we test search quality against queries we *know* the answers to, so we're validating against reality, not just hoping it works.
Search Interface
Week 4-5From there we build the search interface or API -- natural language in, AI-generated answers with citations out. And it integrates into whatever tools your team already uses, not some separate platform they have to remember to open.
Access Controls
Week 6We implement document-level permissions, user authentication, and audit logging. Who can see what, who searched for what, when -- all tracked. This isn't bolted on at the end, it's built in from the start.
Launch + Tune
Week 7-8Then we go live. We monitor search accuracy, track what people are actually querying, and add new data sources based on real demand -- not guesses. First 30 days are free support while you're getting comfortable with the system.
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