Madrid, Spain

IVÁN
SOTILLO.

I'm a software engineer who builds AI products end to end.

LLM agents, computer vision, and the backends, integrations and web apps around them. Forward Deployed AI Engineer at Gambooza and published researcher on LLM agents.

tap open · 0.99 CAM 02 · 23:41:07
One of the things I build at Gambooza, as a toy: cameras that count beers. The real one watches the taps of real bars with YOLO models every night.

Work at Gambooza

Gambooza puts cameras over the beer taps of bars and restaurants and checks every pour against the till.

I own projects end to end: the meetings with clients and POS vendors, the backend and the integrations, the client dashboard, the company website and the models that run every night in production.

Forward Deployed AI Engineer at Gambooza

May 2026 – now

  • Python
  • YOLO
  • Pydantic AI
  • OpenClaw
  • Google Cloud Functions
  • Firestore
  • Firebase Hosting
  • Docker
  • Playwright

An agent owners can text

Bar owners ask on WhatsApp, by text or voice note, and the agent answers from their own data: a table of last night's sales, a chart of the week, the weekly report as a PDF or the video clip of the pint that was never charged.

Simple questions go through fixed workflows and open ones through the LLM. Every client gets its own container and can only see its own venues.

Built with OpenClaw, Pydantic AI and Docker.

Gambooza agent online · sees your 3 venues
Example conversations. The agent answers with text, tables, charts, files and video clips.
  • 0.998

    Recall detecting when a tap opens

    Per-venue YOLO models, measured on days held out for testing, with regions of interest that follow the tap when someone bumps the camera. The method now runs in more than 10 venues.

  • 18

    Venues whose till data flows in by itself

    A multi-tenant pipeline on Cloud Functions and Firestore that ingests 72,000 to 180,000 sale lines a week from 6 POS systems and reconciles them with what the cameras saw.

  • 1 min

    To send the weekly reports, down from half a day

    The client dashboard I built took us off Looker Studio and spreadsheets. It reads straight from Firestore and updates itself, so we check the reports and send them in about a minute.

  • −35%

    Nightly processing time per venue

    I redesigned the video pipeline: full-frame HD storage, rotation and cropping on the GPU, and audits that keep an eye on recall every night.

And the software around it

  • POS integrations

    BDP, Ágora, Glop, Square, Revo and Madisa, each with its own adapter. Revo with OAuth and PKCE, through the vendor's certification process.

  • The client dashboard

    Per-venue and group views, benchmarks against the median venue and a sending workflow for every weekly report. It's what took us off Looker Studio.

  • gambooza.com

    Designed from scratch and built by me: a completely new site, off WordPress and onto Firebase Hosting, with Playwright tests in CI and a design system the whole team now builds on.

Research on LLM agents

Before Gambooza I spent a year and a half in the MISO group at Universidad Autónoma de Madrid, building agents that test other chatbots.

Research Software Engineer at MISO group, Universidad Autónoma de Madrid

Dec 2024 – Apr 2026

  • Python
  • LangGraph
  • Django REST
  • React
  • PostgreSQL
  • Celery
  • Docker
  • GitHub Actions
  1. Hi! What can you do?

    I can show you the menu, take your order or tell you our opening hours.

  2. Show me the menu, please.

    Margherita, pepperoni or four cheese. Which one, and what size?

  3. A large pepperoni.

    Anything to drink?

  4. A cola.

    That's €14.50. Shall I confirm the order?

5 functionalities found

Show menu Opening hours Order pizza size · type Add drinks Confirm order total

5 functionalities · transactional chatbot

  • order_full_meal.yaml
  • ask_opening_hours.yaml
  • order_without_drinks.yaml
A made-up example: TRACER chats with a pizzeria bot, maps what it can do and writes the tests.

TRACER

An agent that talks to any chatbot as a black box, maps what it can do and writes the test profiles for it. It reached 94% average precision on real production chatbots. It was my MSc thesis, graded 10/10.

Chatbot Dōjō's public TRACER dashboard with past runs, and the analysis report of one of them.

Chatbot Dōjō

A full-stack platform I designed and built. It runs TRACER, the lab's Sensei user simulator and its correctness checks as one testing pipeline, and I presented it as a tool demo at ICSE 2026.

Publications

  1. Reverse Engineering of Chatbot Behaviour for Automated Testing SN Computer Science, Springer, 2026
  2. Automated Testing of Conversational Agents with Chatbot Dōjō ICSE 2026, Demonstrations track, 2026
  3. Automated Exploration of Conversational Agents for the Synthesis of Testing Profiles ICTSS 2025, Springer, 2025

Along the way I also wrote Chatbot Connector, a Python library that lets the same tests talk to chatbots built on Rasa, LangChain or the OpenAI API, released to PyPI with GitHub Actions.

Projects

  • CertiCamper's landing page: homologate your camper, simply and quickly.

    Client work

    CertiCamper

    A platform for a vehicle engineering consultancy that moved camper homologation out of email threads and into one guided workflow, with roles for clients and engineers.

    • FastAPI
    • React
    • TypeScript
    • PostgreSQL
    • Docker
    • RBAC
  • U.D.I.N. desktop windows: a new modification form and a document generation menu.

    Client work

    U.D.I.N.

    A desktop app for the same consultancy that writes the official engineering documents for vehicle modifications in Spain, replacing repetitive manual paperwork.

    • C#
    • WPF
    • Document generation
  • Mel spectrogram of a jazz drum fill above the ground-truth and predicted drum hits.

    Personal

    Drumscribe AI

    A deep learning model that listens to drum recordings and writes them out as General MIDI.

    • PyTorch
    • Python
    • Audio ML
Portrait of Iván Sotillo

About

I like building whole products, from the database to the interface. Lately most of them have AI inside.

I studied Computer Science Engineering and then Data Science at Universidad Autónoma de Madrid, with a semester at the University of Helsinki and a spring school at Northwestern Polytechnical University in China. I was awarded Excellence Scholarships by the Community of Madrid and by the university, and highest honors in Recommendation Systems, Data Structures and Mobile App Development.

  1. 2026–now Forward Deployed AI Engineer
    Gambooza
  2. 2024–2026 Research Software Engineer
    MISO group, Universidad Autónoma de Madrid
  3. 2024–2025 MSc in Data Science
    Universidad Autónoma de Madrid
  4. 2020–2024 BSc in Computer Science Engineering
    Universidad Autónoma de Madrid

Download my CV (PDF)