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AI Integration
pgvector RAGSemantic SearchAny Data Source

Tu base de datos lo sabe todo. Tu equipo no encuentra nada.

Si eres fundador y ves cómo las consultas SQL se convierten en un cuello de botella para tu equipo de operaciones, has llegado al momento en que los datos se vuelven un lastre en lugar de un activo.

3,080
Monthly Searches
RAG and database AI keywords
1M+
Documents Indexed
Scales without limits
<2sec
Search Speed
Regardless of library size
95+
Lighthouse Score
Performance target
What Custom Database AI Integration Actually Does — And What Stays Broken Without It

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.

Dónde fallan los proyectos

Right now, getting answers out of your database means writing SQL -- or tracking down the one person on your team who can So most questions just don't get asked. The friction is too high, the queue is too long, and by the time you get an answer, the moment's passed. That's a real cost, even if it's invisible on a spreadsheet.
You've got 10,000 documents sitting in a file share somewhere -- maybe SharePoint, maybe a network drive, maybe both Nobody can search them effectively. The knowledge is there. It exists. But it's effectively invisible to anyone who doesn't already know exactly where to look, which defeats the whole point.
Keyword search is honestly pretty limited If you search "staff reduction" and the document says "workforce restructuring," you get nothing. The answer's in your data. But because someone used different words, you can't find it. And that happens dozens of times a day across your organization.
New employees can take three to six months just to learn *where* information lives -- never mind actually learning the information itself And the real kicker? The deep institutional knowledge lives in the heads of your most senior people. It's not written down anywhere. When they're in a meeting or out sick, that knowledge is just... unavailable.
So what does your team do instead? They copy data into ChatGPT No context about your business, your clients, your specific situation. The answers come back generic -- technically reasonable, completely useless for your actual problem. Plus there are real data security questions you probably don't want to think too hard about.
Someone retires after 22 years They walk out the door on a Friday, and decades of expertise -- the *why* behind decisions, the workarounds, the lessons learned the hard way -- goes with them. There's no recovery from that. Or there wasn't, until now.

Cumplimiento

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.

Qué construimos

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

Nuestro proceso

01

Data Audit

We 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.
Week 1
02

Ingestion Pipeline

Next 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.
Week 2-3
03

Search Interface

From 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.
Week 4-5
04

Access Controls

We 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.
Week 6
05

Launch + Tune

Then 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.
Week 7-8
Claude APIpgvectorSupabaseOpenAI EmbeddingsVercelPostgreSQLMongoDB

Preguntas frecuentes

¿Qué es RAG?

RAG -- Retrieval-Augmented Generation -- funciona así: ingerimos tus documentos o base de datos en embeddings vectoriales almacenados en pgvector. Cuando alguien hace una pregunta, la IA busca semánticamente -- por significado, no por palabras clave -- extrae los fragmentos relevantes y redacta una respuesta que cita tus documentos originales. No puede alucinar porque no está rellenando huecos con datos de entrenamiento. Está leyendo tu contenido y resumiendo lo que encuentra.

¿Qué tipos de datos pueden ingerir?

Prácticamente cualquier cosa digital. Bases de datos PostgreSQL, MongoDB, MySQL. PDFs, documentos Word, archivos Excel. Confluence, Notion, Google Docs. Correos electrónicos. Datos de API de sistemas externos. Si es digital y es tuyo, podemos ingerirlo e indexarlo.

¿Qué tan precisa es la búsqueda semántica?

La búsqueda semántica resuelve el problema de desajuste de vocabulario que arruina la búsqueda por palabras clave. Pregunta por "cláusulas de terminación de empleados" y encuentra acuerdos de separación y disposiciones de fin de contrato -- palabras distintas, mismo significado. Además, ajustamos la recuperación para mayor precisión, porque 5 resultados muy relevantes siempre superan a 50 vagos.

¿Cuánto cuesta el desarrollo de RAG?

Un RAG sencillo sobre una biblioteca de documentos con menos de 1.000 archivos cuesta entre $3.000 y $8.000. Un RAG empresarial -- múltiples fuentes de datos, controles de acceso, integración con flujos de trabajo -- va de $15.000 a $40.000. Ambos escalan a millones de documentos según crezcan tus necesidades.

¿Almacenan mis datos?

Tus datos permanecen en tu instancia de Supabase o en tu base de datos existente. Los embeddings se almacenan junto a tus datos. Claude procesa las consultas en memoria sin retener tu contenido en ningún lugar. Tú controlas la infraestructura -- no tenemos tus datos como rehén.

¿Cuánto tarda la configuración de RAG?

Un RAG de documentos sencillo suele tardar de 2 a 3 semanas. Un RAG empresarial multifuente lleva de 6 a 10 semanas -- el tiempo adicional se debe principalmente a la limpieza de datos, la optimización del chunking y la validación de precisión con consultas reales. Saltarse esa parte es la razón por la que se acaba con un sistema que *parece* funcionar pero da respuestas incorrectas.

RAG Development From ,000
Any data source. Semantic search. Citations. Fixed-price.
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