schema
Identify the workflow
Understand where AI can actually save time, reduce manual work, or improve the product experience before choosing a model.
AI Product Engineering & RAG Systems
Private AI for trusted data workflows.
I help businesses design and build practical GenAI systems: model selection, RAG over private data, local/cloud LLM setup, model routing, secure APIs, and product-ready AI features.
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.
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Find the right data
Prepare documents, records, transcripts, and product data so the AI can search trusted source material before it answers.
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Choose and route the model
Select the right local or cloud model, then keep the app connected through one stable API while providers, prompts, and fallbacks evolve.
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Control access
Keep retrieval scoped by workspace, metadata, roles, and permission rules so sensitive context stays in the right place.
Where I help
Turn AI decisions into working product outcomes.
Clients understand AI value faster when the work is tied to model choices, private knowledge, business actions, and product delivery.
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Model and provider selection
Compare local models, OpenAI, Claude, Gemini, open-source LLMs, and routing options based on privacy, speed, cost, and workflow needs.
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Private company knowledge search
Build RAG over documents, PDFs, policies, records, project data, transcripts, and internal knowledge bases.
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AI workflow automation
Connect AI with business actions such as support replies, document lookup, reporting, CRM updates, task routing, or internal tools.
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Product-ready AI features
Turn AI from a demo into a usable product feature with UI states, APIs, logs, fallbacks, permissions, and deployment paths.
How it works
A practical path from workflow to shipped AI.
The goal is not a flashy demo. The useful system starts with the business workflow, then connects trusted data and the right model into the product.
01 / Discover
Map the use case
Map the business workflow, users, data sources, privacy needs, and success criteria before selecting the AI approach.
02 / Ingest
Prepare the content
Normalize documents, markdown, product data, transcripts, and structured records before indexing.
03 / Index
Make it searchable
Create embeddings, metadata, namespaces, and vector indexes so the content becomes searchable and scoped.
04 / Retrieve
Choose the right context
Use hybrid search, filters, and reranking to select the right passages for the right tenant and workflow.
05 / Generate
Route the request
Route the request to the right local or cloud model and return grounded answers with source context.
06 / Integrate
Ship it into the workflow
Connect the AI response into the product UI, backend API, automation workflow, or internal dashboard.
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
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Local-first option
Run private inference paths where sensitive data should stay inside owned infrastructure.
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Tenant-aware retrieval
Use namespaces, filters, and metadata rules so retrieval never crosses the wrong workspace.
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Traceable answers
Attach source references and retrieval metadata so product teams can inspect why an answer was produced.
AI stack
Tools I use to build, route, retrieve, and deploy.
The implementation stack stays practical: proven LLM providers, private retrieval, stable gateways, backend APIs, and deployment paths that match the workflow.
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LLMs
OpenAI, Claude, Gemini, local models, llama.cpp, Ollama-compatible workflows.
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Retrieval
Qdrant, vector search, metadata filters, hybrid search, reranking.
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Model gateway
LiteLLM, model routing, fallbacks, provider abstraction, API-compatible gateways.
api
Backend
Node.js, NestJS, REST APIs, auth, service layers, queue/workflow integrations.
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Deployment
Docker, AWS, Linux/WSL2, private/local-first inference paths.
AI Delivery
Build the right AI system for your workflow.
I can help you identify the right AI use case, choose the right model, build RAG over your private data, connect it through a stable API, and ship it inside your actual product or business workflow.