Projects

End-to-end AI products — from research prototypes to production deployments. Every project measured with real numbers, not just demos.

RAGProductionKnowledge Graph

GraphRAG Module

Knowledge graph + RAG module as part of the company's broader RAG system. Proposed and led the project from concept to production-ready code.

🔍 Reranking Evaluation

Systematic evaluation across two datasets. Landed on configuration that improved metrics by +7-11 percentage points over fixed baseline, while keeping per-query latency under 2 seconds — the deciding factor for production recommendation. Integrated into production.

🐛 Critical Bug Resolution

Traced and resolved a significant bug in chunk retrieval during entity-filtering work. Once fixed, recall improved by roughly 20-80% depending on settings, across multiple datasets.

🧬 Ontology Extraction

Experiment-driven product improvement through systematic ontology extraction from corpora. Research doesn't always resolve in one pass — the iteration itself produced the improvement.

⚡ Production Status

System is production-code-ready. For complex corpora it requires meaningful compute, so tuning has been part of the work (postponed due to current focus on research project), but considered a delivered, completed piece.

PythonDjangoElasticsearchChromaLLM APIsRAG
AgentFull-StackStartup

SceneFlow Video Agent

Co-founded startup project — handled the entire technical stack while participating in product positioning, fundraising, market research, and beta testing guidance. Video agent with comprehensive tool integration.

🤖 Agent Engine

7000+ line engine with L0/L1/L2 routing. L0 list/get skips the LLM entirely — 200 L0 + 200 L2 classify_turn calls stayed under 2000ms wall budget.

🎨 Media Generation

Integrated Seedream (image) and Seedance (video) generation. Persisted n=1 Ark Seedream image and n=1 Seedance 5s 720p video with downloadable outputs.

💰 Cost Tracking

DeepSeek cloud cost_sum 6.16e-06 USD (n=2 synthetic). Bedrock Nova Micro cost_sum 1.05e-06 USD. Ollama stays null, not 0.0 — honest accounting.

🔌 MCP & Tools

Read-only MCP retrieve over studio HTTP. SceneFlow MCP limited to list/get only — security-first design with tool-use capabilities.

PythonFastAPINext.jsDocker ComposeLangGraphMCPSeedreamSeedance
RAGWriting App

IntelliScribe

Knowledge-ingest + writing application. Full-stack with FastAPI backend, Next.js frontend, and Docker Compose deployment. Chat and generate share SSE streaming.

🔄 RAG Migration

Replaced in-repo Chroma with studio HTTP retrieve. Measured p95 228.0ms at n=50, 7.67 rps, warm 8-doc index, no LLM on the path.

📊 Retrieval Quality

20-question writing eval scored recall@5 = 1.0 (20/20) on fixture overlap retriever. That number is the question-set baseline, not live Chroma quality.

⚡ SSE Streaming

OpenAI-compatible path uses stream=true and yields SSE deltas. 13 tests green. Stub still slices 12 chars on purpose for offline tests.

🏷️ AI Disclosure

Disclosed AI generation as JSON boolean ai_generated on studio and product /health endpoints.

FastAPINext.jsDocker ComposeSSEPostgreSQLpgvector
InfrastructureRAG

ai-studio RAG Services

Modular RAG service layer providing retrieval, evaluation, and cost tracking to multiple AI products. Designed for scalability and extensibility — each component can be independently deployed and measured.

🧩 Modular Architecture

Separate modules for retrieval, evaluation, cost tracking, and health monitoring. Each service can be consumed independently by different products, enabling flexible composition.

📈 Eval Framework

Systematic evaluation with measured quality metrics. Recall@5 0.933, MRR 0.778, p50 194ms. Honest about what each number represents.

💰 Cost Tracking

Per-query cost calculation from public token pricing. DeepSeek 6.16e-06 USD, Bedrock Nova 1.05e-06 USD. Ollama stays null, not 0.0 — transparent accounting.

🔌 Service Interface

HTTP API for retrieval, OpenAI-compatible streaming, health endpoints with AI disclosure. Products consume services, not implementations — enabling backend changes without breaking clients.

PythonFastAPIpgvectorPostgreSQLEval Framework
ResearchKGQAPublished

NLQxform — KG Question Answering

Master's thesis research on knowledge graph question answering. Built a language model-based question to SPARQL transformer, then extended it into an interactive scholarly QA system.

📝 CEUR 2023 Workshop

NLQxform: A Language Model-based Question to SPARQL Transformer
PDF | Code

🎓 SIGIR 2025 Demo

NLQxform-UI: An Interactive and Intuitive Scholarly Question Answering System
PDF | Code

PyTorchTransformersSPARQLKnowledge GraphsNLP