Case studies

How we bring AI to the core of operations.

Implementation stories with companies and institutions, told by the people at the center who led them: the starting problem, what we built and what changed.

Technology acceleration · AI‑First

Supporting Éner on its path to becoming an AI‑First company.

With Éner, an energy efficiency and renewable energy company, we are running a technology acceleration cycle so that AI becomes part of how the company works.

The challenge: Éner already used AI for specific tasks. The next step was to bring it into everyday processes and enable its teams to build solutions on their own.
What we did: We work month by month with its teams to choose processes and redesign them with AI, with ongoing support and an AI agents hackathon to learn by building.
Where it is headed: The cycle runs until the end of the year. The goal is for Éner to finish with processes already transformed, teams with the tools in hand and a plan to keep moving forward.

Applied data science · Training

Six years working with CMPC on data and artificial intelligence.

What began as occasional advisory work is now an annual applied data science agreement, with a team from the center working alongside the company.

The challenge: An industrial operation of this scale produces far more data than it can use. The task was to turn that data into better decisions and train people to use it.
What we did: We started in 2020, still as the Data Science Unit, with process analysis and with Gesfire, software that supports decision-making at the wildfire command center. Since 2022, an agreement has focused on finding and solving data opportunities at its plants, along with several training programs.
Where it is headed: The agreement has been renewed every year, and nearly a hundred CMPC professionals have taken our programs.

Read the storyGesfire, el software que agiliza la toma de decisiones en incendios rurales

AI automation · Training

Maritano brings AI agents into its processes.

Maritano, the soap factory in Talcahuano, wanted to move from trying out AI tools to using them in its operations.

The challenge: Much of the daily work was spread across spreadsheets, emails and systems. The question was what to automate first and how to do it well.
What we did: Together with the company, we chose the processes with the most potential and are automating them with AI agents, along with a course for its team.
Where it is headed: Maritano will have its first automated processes and a team able to keep expanding them.

Optimization · Surgical management

Artificial intelligence to schedule surgeries in public hospitals.

With GesNova Salud, we developed the smart surgery scheduling module of its surgical management system, designed to reduce waiting lists.

The challenge: Scheduling operating rooms is a puzzle: patients on waiting lists, medical teams, rooms and equipment all have to fit together at once.
What we did: We built an engine that produces the surgical schedule from the waiting lists and those constraints, based on rules and expert knowledge.
Outcome: The system launched in public hospitals in December 2025, with the goal of improving operating room use by 20 %.

Read the storyUdeC contribuye con IA a optimizar cirugías en hospitales públicos

Technology acceleration

Parque San Pedro charts its AI transformation roadmap.

We are supporting Empresas Parque San Pedro in a technology acceleration program to decide where AI adds the most value to the organization.

The challenge: In a service where the way people are treated matters most, the task was to find where technology helps without losing that personal touch.
What we did: We work with a company committee to prioritize initiatives and design solutions, with an AI agents hackathon for its teams.
Where it is headed: The program runs until early 2027 and aims to leave a clear roadmap and the first working prototypes.

Optimization · Computer vision · Training

With Arauco, from the pulp mill to the forest.

One of the center’s longest-standing partnerships: optimization models, AI image analysis and training for its teams.

The challenge: Planning a mill’s supply or monitoring plantations are decisions with a lot of data and a lot at stake.
What we did: We developed a model to plan pulp supply and took part in Deep-Hub, a platform that analyzes drone and satellite imagery for forest management.
Where it is headed: Seven years of work that now also include machine learning and AI training for the company’s professionals.

Read the storyDeep-Hub cierra con éxito su proyecto de IA para imágenes aéreas

Data platform

A platform for Chile’s climate change strategy.

For CONAF, and later with FAO, we developed part of the platform that manages information for the National Strategy on Climate Change and Vegetation Resources.

The challenge: The strategy brings together information from many sources and territories, and it needed a shared place to track its progress.
What we did: We built web systems to manage land parcels, plan and monitor actions and estimate their impact on CO₂ emissions.
Outcome: A platform that centralizes the strategy’s information, developed in two stages.

Read the storyPlataforma para la gestión de recursos vegetacionales y cambio climático

Training · AI agents

The team behind the Avonni Prize learned to build its own AI agents.

For the ForoInnovación Foundation, organizer of the Avonni National Innovation Prize, we led a hands-on AI agents workshop.

The challenge: A small team with a heavy workload wanted to save time with AI without piling up tools.
What we did: A preliminary conversation to choose the right tools, followed by an in-person session where each person built agents for their own work.
Outcome: A team that now uses AI agents for its recurring tasks.

Training · AI agents

QPharma trained its team in AI agents.

For QPharma, a pharmaceutical distributor, we delivered training in artificial intelligence agents tailored to its team.

The challenge: The company wanted its team to go from using AI occasionally to relying on agents in their daily work.
What we did: A hands-on in-company course, built around the company’s real tasks.
Outcome: A trained team with concrete ideas for applying AI agents in its operations.

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