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System Architecture
Under the Hood
Hybrid AI pipeline combining pre-trained LLMs with fine-tuned local models for privacy and performance.
Inference Engine
Orchestration layer managing prompts, context windows, and model fallback strategies.
LangChain, Python, FastApi
Vector Store
High-dimensional database for semantic search and Retrieval-Augmented Generation (RAG).
Pinecone, Milvus, pgvector
Model Layer
Fine-tuned models deployed on GPU clusters for specialized tasks (Vision, Classification).
Hugging Face, PyTorch, CUDA
Engineering Lifecycle
From concept to deployment.
Data Prep
Cleaning, labeling, and vectorizing datasets.
Training
Fine-tuning base models on domain data.
Integration
Connecting AI endpoints to the main app.
Evaluation
Testing against benchmarks and edge cases.
Deployment
Model serving with auto-scaling GPUs.