Applied AI & Agentic Systems

A multi-client agentic chat engine with RAG, external tools and generative BI, isolated per client.

The problem

Give each client an assistant that answers any question about their documents and data, connects to their tools and generates charts and dashboards, while keeping data isolated.

Stack decision

FastAPI + LangChain/LangGraph, RAG indexing with pgvector and Cohere Rerank, orchestration of several LLMs by quality/price (OpenRouter, Gemini, Deepseek) and tools via MCP/FastMCP. Fully traceable with Langfuse.

The technical challenge

During indexing, automatically clustering information, detecting discrepancies and allowing manual curation; and isolating data by project_id in a reusable multi-tenant engine.

Impact

A reusable engine in production for several clients (financial Q&A, margin and profitability monitoring with charts).

A platform for at-home therapists with a slot optimizer that minimizes travel and waiting time.

The problem

Cutting travel and waiting time for at-home therapists, bringing several roles (therapists, patients, centers, guardians, admin), clinical records, calendar and payments into a single platform.

Stack decision

Next.js/React + FastAPI/SQLAlchemy on PostgreSQL; Stripe (recurring payments and Connect), Google Calendar (two-way sync) and OpenRouteService for real distances/times.

The technical challenge

The slot optimizer: it scores (0–100) each therapist’s gaps and places every patient in the best one using real locations, penalizing dead waiting time and travel.

Gesture-based highlight detection and player heatmaps from each match’s footage.

The problem

Automatically extracting a match’s best moments and player analytics from already-recorded footage, optimizing cost and processing time.

The technical challenge

Telling the chosen highlight gesture (raised arms) apart from similar in-game moves (smashes, volleys): an algorithm over the joints (YOLO Pose) that catches true positives without adding false ones.

Impact

Processed with GPU workers on RunPod to optimize cost and time, with a strong focus on multithreading.