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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.

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Custom Database AI Integration

Custom database AI integration connects a large language model to your proprietary data through retrieval-augmented generation (RAG), using pgvector to store and search high-dimensional embeddings of your records, documents, and structured tables. Instead of writing SQL or relying on a data analyst, any team member types a natural-language question and the system retrieves the relevant data, passes it to the model as grounded context, and returns a cited answer. The result is a queryable knowledge layer built on data you already own, with no fine-tuning and no hallucinated facts.

What is holding your current website back?

Common gaps we find in nearly every audit.

Two engineers field every data question because no one else can write SQL, creating a permanent bottleneck that slows sales, ops, and support decisions.
Risk: As headcount and data volume grow, those engineers spend more time answering ad hoc queries than building product, and decisions get delayed waiting in their queue.
Keyword search across 10,000 PDFs or a large document store returns dozens of partial matches that staff must manually review to find the one relevant clause or record.
Risk: Teams stop trusting internal search, fall back on memory or Slack, and critical information in your own database effectively disappears from daily operations.
Off-the-shelf AI tools hallucinate because they have no access to your actual data, so staff either distrust the answers or, worse, act on fabricated figures.
Risk: A single confident wrong answer in a customer proposal or compliance report erodes trust in the tooling and exposes the business to avoidable errors and rework.

How We Build This Right

Every safeguard, built in from Day 1.

Data stays in your infrastructure

Embeddings are generated and stored in your own pgvector instance, whether that is a self-hosted PostgreSQL server or a managed cloud database you control. No proprietary records are sent to a third-party vector store.

Auditable retrieval, not black-box generation

Every answer surfaces the source rows, document chunks, or record IDs used to construct the response. Teams can verify the origin of any answer, and you retain full logs of what was retrieved and when.

Role-scoped access controls

Query permissions mirror your existing access model. We integrate with your authentication layer so users only retrieve data their role is authorised to see, preserving the data governance policies already in place.

What We Build

Purpose-built features for your industry.

pgvector embedding pipeline

We build an ingestion pipeline that chunks, embeds, and indexes your structured tables, documents, or mixed data sources into pgvector. The pipeline runs on a schedule or event trigger so your index stays current without manual intervention.

Natural-language query interface

A clean web UI or API endpoint accepts plain-English questions from any team member. No SQL knowledge required. The interface returns direct answers alongside the source records so users can drill into the underlying data if needed.

Grounded RAG with Claude

Retrieved context is passed to Claude with strict prompting that constrains the model to what was actually found in your database. The system is built to return no answer rather than a fabricated one when relevant data does not exist.

Incremental re-indexing and drift monitoring

We instrument the pipeline to detect schema changes, new document uploads, or record deletions and re-embed affected content automatically. A monitoring layer alerts on index staleness so answers do not silently drift from reality.

Built on a Modern, Secure Stack

Claude APIpgvectorSupabaseOpenAI EmbeddingsVercelPostgreSQLMongoDB

Our Development Process

From discovery to launch. Quality at every step.

01

Data audit and schema mapping

1 week

We review your existing databases, file stores, and access patterns to identify which data sources answer the questions your team asks most often. We document schema, record volume, update frequency, and any fields that require access restrictions before writing a line of code.

02

Embedding pipeline build and indexing

1-2 weeks

We build the ingestion pipeline, define chunking strategies appropriate to your data types, generate embeddings, and populate the pgvector index. You get a staging environment where you can run test queries against real data before anything touches production.

03

RAG layer and interface development

1-2 weeks

We wire the retrieval layer to Claude with prompt architecture that enforces grounding and citation. We build the query interface, whether a web UI for internal staff, an API for your existing tools, or both, and integrate it with your authentication system.

04

Accuracy review, handover, and monitoring setup

1 week

We run structured accuracy tests with your team using real questions from your ops, sales, or support workflows, iterate on retrieval parameters based on failure cases, document the system, and configure alerting for index drift and pipeline errors before handover.

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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.
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