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 WhatsApp chatbot that lets any club member manage their bookings by chatting, powered by an LLM and tooling.

The problem

Give any club user a natural way to book courts, check when the next ones are free or do any booking task, without apps or forms.

Stack decision

An LLM + tooling (function calling) mechanism: the model interprets the natural-language conversation and calls tools that query and operate the club’s booking system, all over WhatsApp.

The technical challenge

Turning free-form requests ("when can I play tomorrow afternoon?") into reliable actions on the booking system — resolving availability, slots and confirmations inside the chat itself.

An MCP that navigates the 250+ server ecosystem to diagnose and fix remote incidents with the power of LLMs.

The problem

Diagnosing and fixing incidents across a 250+ server ecosystem where many resources are only reachable by hopping through the network: cameras accessible only from their remote server, and local databases with temporary data on each server.

Stack decision

An MCP (Model Context Protocol) that exposes tools to the LLM to navigate the network of servers and load balancers, inspect remote servers and cameras, query the local databases and apply fixes, on top of the video-analytics ecosystem’s 443 reverse-proxy/load-balancing architecture.

The technical challenge

Reaching deeply nested resources (cameras and local DBs only accessible by hopping server to server through the load balancers) and exposing that navigation as reliable tools so the LLM can diagnose and repair autonomously.

Impact

Monitoring and diagnosis of a 250+ server production ecosystem, speeding up the resolution of errors on remote servers and cameras.

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.