Ananya

the problem

• Compliance audits take weeks of manual cross-referencing against regulatory frameworks
• Every new regulation or policy update means starting from scratch

how it works

• Upload policy docs, pick a framework (GDPR, SOC 2, HIPAA), get a gap analysis in minutes
• LangGraph multi-agent pipeline — one agent retrieves clauses, one maps gaps, one generates the report
• Dual vector stores: FAISS for fast keyword retrieval, Chroma for semantic search
• Every finding cites the exact clause and policy section it references
• Next.js frontend, FastAPI backend, deployed to GCP Cloud Run with CI/CD via GitHub Actions

results

• Supports three regulatory frameworks out of the box
• Dual vector store approach improved retrieval relevance over single-store setups
• Fully cited reports — no black-box findings

the problem

• Firebase docs are massive and fragmented across guides, references, and tutorials
• Built-in search struggles with questions that span multiple products

how it works

• 1,070+ doc chunks embedded into Pinecone for semantic search
• Gemini API generates grounded answers with citations from retrieved chunks
• Exposed via MCP — plugs into any MCP-compatible AI coding assistant
• Node.js backend, React frontend, MongoDB for conversation history
• Containerized and deployed on GCP

results

• Answers grounded in actual docs, not hallucinated content
• Chunking strategy and embedding quality were the biggest levers for answer quality
• MCP integration makes the tool usable beyond its own UI

the problem

• Informal ride-sharing relies on manual coordination — forums, DMs, no guarantees
• No way to see routes on a map, book instantly, or handle payments in one place

how it works

• Post rides, search by route, book seats with live map visualization
• RabbitMQ queue handles concurrent bookings — last seat goes to one person, rest get notified
• Next.js + Redux frontend, Express.js API backend
• Dockerized and deployed to AWS ECS with MongoDB

results

• Async booking queue eliminated race conditions on seat availability
• Map visualization made route evaluation instant vs. reading text descriptions
• API stays responsive under concurrent booking pressure

the problem

• Stations run empty in high-traffic areas while others overflow with unused bikes
• Restocking is reactive — crews respond after stations are already empty

how it works

• Airflow-orchestrated pipeline runs monthly: ingest data, engineer features, train, evaluate
• Features include time-of-day patterns, weather correlations, seasonal trends
• MLflow tracks every experiment — hyperparameters, metrics, artifacts
• DVC versions training data alongside code
• Dockerized on GCP with GitHub Actions CI/CD

results

• Runs autonomously with monthly retrains, no manual triggers
• Easy to compare model iterations and roll back underperformers via MLflow
• DVC solved the "which data did this model train on" problem

the problem

• Hospitals run multiple EHR systems that don't sync with each other
• Stale or conflicting patient data across systems is a safety risk

how it works

• Kafka event backbone — each source system publishes record updates to topics
• Stream processor applies conflict resolution (timestamp-based, field-level merging)
• PostgreSQL serves as the single source of truth
• Airflow batch job runs periodic reconciliation as a safety net

results

• Handles high-throughput updates without bottlenecking
• Field-level conflict resolution preserves concurrent updates instead of dropping one
• Batch reconciliation catches edge cases where event ordering gets tricky

the problem

• NYT Strands isn't just find-the-word — every cell must be used exactly once with no overlaps
• Words must also fit a hidden theme, adding a semantic constraint on top of the spatial one
• The search space grows exponentially with grid size, making brute force infeasible

how it works

• Trie + DFS pass extracts every valid word traceable through adjacent cells in the grid
• SBERT ranks candidates by semantic similarity to the puzzle theme — so thematically relevant words score higher
• CSP solver picks the final subset of words that tiles the grid perfectly with no gaps or overlaps
• Dancing Links (DLX) and Genetic Algorithm implemented as alternative solvers to benchmark tradeoffs
• Pygame visualization shows the solver working through the grid step by step in real time

results

• Successfully solves Strands grids end-to-end given a letter grid and theme
• SBERT ranking significantly narrows the candidate pool before the CSP stage, reducing solve time
• Three solver approaches benchmarked — CSP with semantic pre-ranking performed best overall

the problem

• Complex image processing workflows require professional editing software with steep learning curves
• Non-technical users need accessible tools without sacrificing capability

how it works

• Built in Java using OOP and MVC architecture — modular, extensible codebase with clean separation of concerns
• Command pattern dispatch system supports 10+ image operations: compression, RGB/HSV/grayscale conversions, intensity adjustments, and matrix-based filtering
• Three execution modes from a single codebase — Swing GUI, interactive CLI, and batch script processing
• Split-view preview lets users compare original and processed images side by side before committing
• Histogram alignment tools for color correction and levels adjustment

results

• ~90% JUnit test coverage ensuring reliable transformations across all operations
• Reduced reliance on professional editing software for common image processing tasks
• Extensible architecture — adding new operations requires no changes to existing code