AI-Powered Applications
Integrating LLMs, vector databases, and custom AI agents into production-ready pipelines.
Architectural Overview
"Bridge the gap between raw AI potential and production-ready applications. We integrate advanced Large Language Models and vector search capabilities into your existing workflows to create intelligent, context-aware digital products."
Core Technical Specifications
LLM Integration (OpenAI, Anthropic)
Vector Search (Pinecone, Weaviate)
Custom AI Agents
Retrieval-Augmented Generation (RAG)
Execution Pipeline
Data Ingestion & Chunking
Extracting enterprise data (PDFs, docs, databases) and splitting it into semantically meaningful text chunks.
Vectorization
Passing chunks through embedding models (e.g., text-embedding-3-large) and storing them in a Vector Database.
Agentic Reasoning
Building LangChain/LlamaIndex agents that can route queries, use tools, and reason through multi-step problems.
RAG Pipeline Deployment
Deploying a FastAPI backend that executes Retrieval-Augmented Generation, fetching context before hitting the LLM.
Infrastructure Blueprint
runtime_topology.yamlUser queries hit a Python FastAPI service. The service executes a semantic search against Pinecone to retrieve top-k relevant documents. The retrieved context is injected into a strict system prompt and sent to an LLM (like GPT-4). Responses are streamed back to the frontend in real-time using Server-Sent Events (SSE).
Production Deployment Stack
Deploy This Architecture
We formulate custom architectural roadmaps and fixed-price sprint quotes in under 4 business hours.