database
Find the right data
Prepare documents and records so the AI can search the right source material before it answers.
AI case study
Private AI that answers from your data.
I build AI workflows that search your trusted content first, then return answers with useful source context through a clean API.
What this solves
Useful AI needs trusted context.
A simple chatbot is not enough when users need answers from product docs, policies, records, or internal knowledge. The system has to retrieve the right content, protect the data path, and return a response the product can use.
settings_input_component
Route the model
Keep the app connected through one stable API while models, providers, prompts, and fallbacks can change behind it.
verified_user
Control access
Keep retrieval scoped by workspace, metadata, and permission rules so sensitive context stays in the right place.
How it works
A simple path from content to answer.
The goal is not a flashy demo. The useful system is the path from trusted source content to a grounded answer inside the product workflow.
01 / Ingest
Prepare the content
Documents, markdown, product data, transcripts, and structured records are normalized before indexing.
02 / Index
Make it searchable
Embeddings, metadata, namespaces, and vector stores make the source content retrievable and scoped.
03 / Retrieve
Choose the right context
Hybrid search and reranking keep the answer grounded in the right passages, tenant, and workflow.
04 / Generate
Return an API response
The application receives a clean API response with answer text, source references, and traceable metadata.
Gateway Example
$ POST /v1/chat/completions
{
"model": "local-rag-router",
"tenant": "workspace-a",
"retrieval": "policy-docs"
}
A gateway keeps application code stable while models, providers, vector stores, and routing rules evolve behind a controlled interface.
Security Shape
lock
Local-first option
Run private inference paths where sensitive data should stay inside owned infrastructure.
hub
Tenant-aware retrieval
Use namespaces, filters, and metadata rules so retrieval never crosses the wrong workspace.
fact_check
Traceable answers
Attach source references and retrieval metadata so product teams can inspect why an answer was produced.
AI Delivery
Add intelligence where it supports the product.
I can help connect retrieval, model routing, backend APIs, and UI workflows into a usable AI feature instead of a disconnected demo.