Building production-ready AI systems. LLMs · RAG · Multi-Agent Workflows · Computer Vision · Real-Time STT · FastAPI

🤖 AI/ML              ⚙️ Backend              💾 Data                 🚀 MLOps
LLMs · RAG · Agents   FastAPI · APIs         PostgreSQL · MongoDB    Docker · AWS
Evaluation · CV       WebSockets · Async    Vector DB · Graphs      GitHub Actions
Hallucination ctrl    Production Scaling    Redis · Indexing        Kubernetes · CI/CD

AI Ecosystems:

Cloud & DevOps:

Use Case: Enterprise document Q&A with <5% hallucination rate. Analyzes logistics & legal documents with high-precision extraction and grounding.

Architecture: Agentic LangGraph pipeline → Hybrid vector + graph retrieval (Memgraph) → Judge-first validation → Grounded answer generation with telemetry

Key Features:

• Cyclic agentic loops with fast-fail fallbacks
• Strict grounding node (faithfulness scoring)
• Context relevance validation before generation
• Chunking strategy optimized for fact-density
• Confidence scoring (model + retrieval + faithfulness weighted)

Stack: LlamaIndex · LangGraph · Memgraph 3.0 · LiteLLM · FastAPI · Streamlit

Metrics: Hallucination detection, retrieval confidence, post-generation audit

Use Case: Analyze lease documents, extract structured data (parties, financial terms, dates), perform Q&A with source citations.

Architecture: FalkorDB graph + ChromaDB vector hybrid search → FastAPI backend → ADK chat interface → Structured extraction templates

Key Features:

• PDF upload & multi-document indexing
• Structured lease summary extraction (parties, dates, financials, options)
• Source citation for every answer (page numbers)
• API endpoints for upload, delete, chat, extract, evaluate
• Automated quality testing

Stack: Google ADK · FalkorDB · ChromaDB · FastAPI · OpenAI · Docker

Use Cases: Real estate analysis, contract review, compliance checks, automated lease summaries

Use Case: Conversational clinical intake for healthcare workflows. Supports text chat (MVP) and real-time voice with Whisper STT + OpenAI TTS.

Architecture: FastAPI backend + Streamlit UI + LiveKit voice room → Azure OpenAI LLM → Whisper STT/TTS pipeline → Real-time voice worker daemon

Key Features:

• Dual-mode interface (text chat + real-time voice)
• LiveKit WebRTC for browser-based voice (cloud or self-hosted Docker)
• OpenAI Whisper STT with clinical language tuning
• OpenAI TTS for agent responses
• Session-based state management
• Automatic brief generation on session end
• Structured intake slots & red flag detection
• Multimodal response capability (text → voice in same flow)
• One-command startup (Windows PowerShell + Linux bash scripts)

Stack: FastAPI · Streamlit · LiveKit · Whisper STT · OpenAI TTS · Azure OpenAI · LiteLLM · Docker

Workflow: Client starts text/voice session → AI asks intake questions → Collects structured data → Flags medical concerns → Generates brief summary

Use Case: Complete OpenTelemetry-compatible observability for AI agent MCP servers. Monitor tool usage, analyze performance, contextualize agent behavior.

Architecture: Automatic instrumentation of FastMCP/MCP servers → OpenTelemetry exports → Shinzo dashboard for insights

Key Features:

• One-line instrumentation for MCP servers
• Agent usage pattern analysis
• Tool call contextualization
• Performance metrics & latency tracking
• Multi-platform support
• Flexible export configuration
• Bearer token auth for security

Stack: OpenTelemetry · MCP SDK / FastMCP · Python · Observability standards

Metrics: Tool invocation rates, latency, error tracking, agent behavior patterns

Use Case: Conversational financial advisor for UAE expats navigating mortgage decisions. Provides "buy vs. rent" analysis, affordability calculations, and mortgage guidance using deterministic tools.

Architecture: Google ADK agent → LiteLLM (Groq) for ultra-fast LLM → Deterministic Python tools (NO hallucination on numbers) → FastAPI backend + vanilla JS frontend with streaming SSE

Key Features:

• Conversational "buy vs. rent" analysis with break-even calculations
• Deterministic financial tools (EMI, affordability, eligibility checks)
• UAE-specific mortgage rules & regulations (20% down, LTV limits, DBR)
• Streaming responses via Server-Sent Events (SSE)
• Lead capture at natural conversation stops
• Glassmorphism UI for modern UX
• Groq integration for sub-second response times
• Pytest test suite for financial tools
• Docker + Google Cloud Run deployment

Stack: Google ADK · LiteLLM · Groq llama-3.3-70b · FastAPI · Vanilla JS/HTML5/CSS3 · Pytest · Docker · Cloud Run

Tools:

• tool_calculate_mortgage — EMI, interest, upfront costs
• tool_assess_affordability — Max budget by DBR rules
• tool_compare_buy_vs_rent — Break-even tenure analysis
• tool_check_eligibility — Expat/national/self-employment rules
• tool_get_uae_mortgage_rules — Central Bank regulations

Building: AI/ML systems · Production LLM applications · Agentic workflows · Real-time backends · Computer vision pipelines

Made with ⚡ & 🧠 by Aaditya Aaryan
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