Sintérgica Labs · Applied AI research
AI research from Mexico, published and transferred to industry
We are the research lab of Sintérgica AI, in Xalapa, Veracruz. We conduct fundamental and applied research in Artificial Intelligence and Data Science and transfer it to the productive sector.
- Founded
- 2024
- Headquarters
- Xalapa, Veracruz
- Active lines
- 7
- Ongoing projects
- 8
We generate knowledge and put it into practice
The lab generates scientific knowledge and technology through original research in artificial intelligence, data science and related disciplines.
Sintérgica Labs is the research lab of Sintérgica AI, a Mexican industrial research lab in artificial intelligence. What we research reaches Sintérgica AI's products. Data curation and fine-tuning address a technical problem: global models know little about Mexican Spanish and regulation. That is where Lattice Na'at and Séeb come from. Information retrieval over logistics regulation addresses another: finding the right document.
The lab operates 40 hours a week with resident researchers at bachelor's, master's and doctoral level, in stays of six months to a year, with the goal of publishing at least one paper per semester. Applied research is contracted in two modes: with publication, at a lower cost, or confidential.
Sintérgica Labs is the lab's trade name. Today it operates as a division of Sintérgica AI, and its legal entity will be the Laboratorio de Investigación Industrial en Inteligencia Artificial (LIIIA), A.C., currently being incorporated.
We carry out fundamental and applied research in Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Evolutionary Computation, Intelligent Systems, Optimization and Collective Intelligence. As a cross-cutting axis we work on AI alignment, ethics, governance and constitutionality, to create transparent, explainable, safe and socially responsible models.
Beyond scientific research, the laboratory promotes technology transfer, the development of highly specialized talent, national and international collaborations and participation in high-impact projects that strengthen the country's scientific and technological ecosystem.
General objective
To conduct scientific research and technological development in Artificial Intelligence and related areas through a responsible innovation approach, grounded in principles of ethics, governance and social responsibility, generating knowledge, methodologies and applications that contribute to the advancement of science, innovation and the solution of real problems.
Mission
To generate scientific knowledge and develop technology through applied research in artificial intelligence, data science and related disciplines, with interdisciplinary collaboration and researcher training, to address scientific, industrial and social problems.
Vision
To be a lab recognized in Mexico and abroad for its scientific contributions, technology transfer and researcher training, and a reference in linking academia, industry and government.
What the lab pursues
From the laboratory to publication, to transfer and to the classroom.
01
Research
- Develop original research in AI, Machine Learning, NLP, Philosophy and Ethics of AI, Computer Vision, Evolutionary Computation and related areas.
- Publish results in high-impact scientific journals and conferences.
- Foster interdisciplinary research with other areas of knowledge.
02
Governance and responsible development
- Design, develop and evaluate AI models and systems according to the laboratory's framework of ethics, governance and constitutional principles.
- Define methodologies for transparency, traceability, explainability, safety and accountability.
- Establish evaluation, audit and continuous improvement processes across the entire model life cycle.
- Promote national and international good practices in AI governance.
03
Technological development
- Develop prototypes suitable for transfer to the productive sector.
- Design AI algorithms, models and systems with practical applications.
- Promote the generation of intellectual property and specialized software.
04
Talent development
- Train researchers through research projects, theses and residencies.
- Train professionals in emerging AI-related technologies.
- Support the development of young researchers.
05
Partnerships
- Establish collaborations with universities, research centers, companies and government bodies.
- Take part in national and international research and innovation projects.
06
Outreach
- Share progress through conferences, workshops, seminars and outreach publications.
- Organize academic events in industry and government settings.
- Bring AI and Data Science closer to society.
07
Innovation
- Identify technology transfer opportunities.
- Drive the creation of solutions with economic and social impact.
- Promote science and technology based entrepreneurship.
Seven active lines
Fundamental and applied research, taking the context of Mexico and Latin America as the starting point.
| No. | Research line | Category |
|---|---|---|
| 01 | AI alignment, governance and constitutionalityMethodologies for AI models and systems to operate ethically, transparently, safely and aligned with constitutional principles, regulatory frameworks and human values. | Governance |
| 02 | NLP for native languages of MexicoNatural Language Processing models and resources for the preservation, analysis and generation of text in Mexico's indigenous languages, promoting their inclusion in AI technologies. | Data and languages |
| 03 | Data curation for Mexico and Latin AmericaMethodologies for collecting, cleaning, annotating and validating datasets representative of the region, toward more accurate, inclusive and contextualized AI models. | Data and languages |
| 04 | Explainability of neural networks in NLP tasksMethods to interpret, analyze and explain the decisions of neural networks applied to NLP, strengthening their transparency, reliability and comprehension. | Explainability |
| 05 | Efficient knowledge distillation from language modelsTechniques to transfer knowledge from complex models to smaller, more efficient ones, preserving performance and reducing computational requirements. | Efficient models |
| 06 | Fine-tuning language models with few resourcesStrategies to adapt pre-trained models to specific tasks with limited amounts of data and compute, maintaining high performance and efficiency. | Efficient models |
| 07 | Neural network performance through evolutionary computationEvolutionary Computation methods to optimize architectures, hyperparameters and training processes of neural networks in NLP tasks. | Optimization |
Eight ongoing research projects
The lab's active work, according to its institutional curriculum (July 2026).
- 01
Training language models with data from Mexico.
- 02
Data extraction from large-scale language models.
- 03
Classifiers and traditional machine learning for question-answering systems.
- 04
Retrieval-augmented information systems for Mexican logistics laws and regulations.
- 05
Efficient stigmergic swarms: using digital ants to make local models more efficient.
- 06
The problem of human dependence on language models and anthropomorphism.
- 07
The problem of the concept of learning: applying new definitions of learning in Machine Learning.
- 08
What is similarity? Applying new definitions of similarity to language models and NLP systems.
Who does the research
The laboratory team and their roles.
| Name | Role |
|---|---|
| MCD. Axel Javier Jara Mújica | President · Data Scientist |
| MIA. José Clemente Hernández Hernández | Chief Scientist and Research Lead |
| Luis Feliciano Bautista Romero | Data Scientist |
| Alejandra Cano | Data Curation |
| Axel Jesús López Flores | Data Curation |
Publications and talent development
One published paper, two more in the publication process and a Mexican dataset of 8,754 records with a public version.
Scientific output
2026
Using Classifiers for Question-Answering: a Mexican Dataset Case Study
Bautista-Romero, Luis-Feliciano; Hernández-Hernández, José-Clemente
Proceedings of the XVIII Mexican Congress on Artificial Intelligence (COMIA), Cuernavaca, Morelos
Published in Communications in Computer and Information Science (Springer), with the Tecnológico Nacional de México, Veracruz campus. 83% accuracy and 78% F1, versus 67% for traditional approaches.
Professional internships
Luis Feliciano Bautista Romero
Tecnológico Nacional de México, Campus Veracruz
Edson Martínez Hernández
Tecnológico Nacional de México, Campus Veracruz
Where we have been
The laboratory's participation in academic, government and industry settings.
| Date | Activity | Institution |
|---|---|---|
| 9 Oct 2025 | Workshop lead Student Colloquium«Ethics in science: uses and limits of AI» | Instituto de Ecología, A.C. (INECOL) · SECIHTI |
| 21 Nov 2025 | Organizer First Veracruz AI SummitSintérgica Labs as organizing institution | Sintérgica Labs |
| 18 Feb 2026 | Speaker Inventors' Day«The Journey of an AI: from Research to Market». Veracruz Innovations That Transform | COVEICYDET · Veracruz Ministry of Education (SEV) |
| 26–27 Mar 2026 | Workshop lead Academic Bodies Meeting«Applying AI to higher education» | Tecnológico Nacional de México · SEP · SEV |
| 29 Apr 2026 | Speaker Applied AI for Institutional Communication | Organismo Público Local Electoral (OPLE) Veracruz |
Two ways to take part
One for those who wish to join the research. One for the institutions and organizations that wish to support it.
Laboratory repositories
The code and models the lab publishes are on GitHub and Hugging Face.
Partnerships and joint initiatives
The laboratory seeks strategic partnerships and joint initiatives in research, technological development and innovation.
Universities and research centers
Agreements and projects
- Formal research collaborations
- Thesis projects and residencies
- Joint workshops and seminars
- Interdisciplinary research
Government bodies
Governance and training
- Good practices in AI governance
- Academic events in public settings
- Applied research projects
- Technical staff training
Companies
Technology transfer
- Prototypes transferable to the productive sector
- Intellectual property and specialized software
- Real problems as case studies
- Science based entrepreneurship
Funding bodies
High-impact projects
- Funding for research lines
- National and international projects
- Indicators and evidence of track record
- Scientific, technological and social impact
Contact
MIA. José Clemente Hernández Hernández
Scientific Director
- clemente.hernandez@sintergica.ai
- +52 228 139 2643
- Xalapa, Veracruz
Contact the laboratory
State the reason for your interest and the institution you represent. The laboratory's director replies directly.