Full-Stack AI Architecture

How it works.

The system, step by step — each stage is an agent or service with a clear job and a hand-off to the next.

01 · Infrastructure Design

GPU orchestration on AWS/GCP or serverless inference endpoints for scalability.

02 · Backend Logic

Python/FastAPI or Node.js backends managing chains, memory, and tool execution.

03 · Model Fine-Tuning

Training Llama-3 or Mistral on your proprietary dataset for domain expertise.

04 · Frontend Experience

Next.js interfaces optimized for streaming text and generative UI components.

Built for production, owned by you.

Proven frameworks, not thin wrappers. Open any capability for the technique, the stack and an implementation sketch.

Private LLM Hosting

Running open-source models within your VPC for total data sovereignty and privacy.

Private AI infrastructure — Technique: Dockerized Inference via vLLM

For ultimate privacy, we deploy open-source models (Llama-3, Mixtral) on your own cloud infrastructure (AWS SageMaker or Kubernetes). We use vLLM or TGI (Text Generation Inference) containers for high-throughput token generation. Your data never leaves your VPC; you own the weights, the inputs, and the outputs.

Stack: Kubernetes, vLLM, HuggingFace, AWS EC2 g5.xlarge

Multi-Modal Integration

Combining Text, Image (Stable Diffusion), and Audio models into a single application flow.

Unified Generative Pipeline — Technique: Latent Space Orchestration

We build apps that see, hear, and speak. We chain models together: GPT-4 for logic, ElevenLabs for voice, and Stable Diffusion for visuals. We manage the messy I/O between these models—converting audio buffers to text, text to prompts, and images to base64—creating a seamless, magic-like user experience.

Stack: OpenAI Vision, ElevenLabs API, Stability AI, FFmpeg

Evaluation Pipelines

Automated 'LLM-as-a-Judge' testing frameworks to ensure model quality doesn't degrade.

CI/CD for LLMs (LLMOps) — Technique: RAGAS & G-Eval Metrics

Software breaks; AI drifts. We implement Evaluation pipelines that run every time you update a prompt or model. We use a 'Judge Model' (GPT-4) to grade your app's responses against a 'Golden Dataset' for correctness, tone, and hallucination. We track these metrics in dashboards to prevent regression in production.

Stack: DeepEval, LangSmith, Arize Phoenix, MLflow

Real-Time Streaming

WebSocket architecture to stream AI tokens to the user instantly, reducing perceived latency.

Instant-Feel UX Architecture — Technique: Server-Sent Events (SSE) / WebSockets

Waiting 10 seconds for a whole paragraph feels broken. We implement streaming response architectures. The backend streams tokens to the frontend as they are generated. We build custom React hooks to handle this stream, parsing markdown and code blocks on the fly, making the application feel alive and responsive.

Stack: FastAPI StreamingResponse, Vercel AI SDK, React Server Components

Typical stack: Next.js, Python/FastAPI, Docker/Kubernetes, HuggingFace, AWS Bedrock

From experiment to production.

We scope it on your stack, ship a measured first system, and run it against a baseline you can read.