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Tecnologías emergentes
Investigación aplicada y prototipado
Investigación estructurada de nuevas tecnologías frente a cuestiones de negocio concretas, mediante análisis del panorama, estudios de viabilidad y prototipos funcionales con conclusiones claras.
Visión general
Not every promising technology is ready, and not every ready technology fits a given problem. Applied research closes that gap with evidence: what the technology can do today, what it would take to use it, and where the real risks lie.
We run research as a disciplined engineering activity, with a stated question, a reviewed method, reproducible experiments and an honest write-up. Some prototypes lead directly to products. Others end with a clear recommendation to wait, and reaching that conclusion early has real value.
Aplicaciones de negocio
- 01
Technology feasibility studies
Focused investigations into whether an emerging capability, such as a new model architecture, sensor type, protocol or platform, can meet specific requirements for accuracy, latency, cost or integration in your environment.
- 02
Proof-of-concept builds
Working prototypes that test the riskiest technical assumption first, using real or representative data, so stakeholders can see and evaluate the capability instead of relying on vendor claims.
- 03
Benchmarking and evaluation
Structured comparison of competing models, frameworks or vendors on your own data and tasks, using reproducible test harnesses that can be rerun as the available options improve.
- 04
Research translation
Turning academic papers and open-source research into engineering assessments: what has actually been demonstrated, under what conditions, and what would be needed to apply it in production.
Enfoque de ingeniería
- 01
Question and criteria
We agree on the research question, the evaluation criteria and the result that would justify proceeding before any experiment runs, so findings cannot be reinterpreted after the fact.
- 02
Landscape review
We survey published research, open-source projects and commercial offerings to understand where the field currently stands and to avoid rebuilding what already exists.
- 03
Experiment design
Experiments are designed to isolate the riskiest assumptions, with baselines, controls and datasets documented so your team or an outside reviewer can reproduce the results.
- 04
Prototype build
We build the minimum working system needed to test the hypothesis, favoring speed of learning over polish while keeping the code clean enough to reuse.
- 05
Findings and recommendation
Results are reported plainly, including negative and inconclusive findings, with a recommendation to proceed, adapt, wait or stop and the reasoning behind it.
Tecnologías y plataformas
Research tooling
- Python
- Jupyter
- PyTorch
- Hugging Face
- Weights & Biases
Experiment management
- MLflow
- DVC
- Docker
- GitHub Actions
Prototyping
- TypeScript
- Next.js
- FastAPI
- Streamlit
- Unity
Hardware and edge
- NVIDIA Jetson
- Raspberry Pi
- Arduino
- ROS 2
Los nombres de tecnologías son marcas registradas de sus respectivos titulares y no implican asociación ni respaldo.
Qué recibirá
- Research brief with question and success criteria
- Landscape review with annotated references
- Reproducible experiments and datasets
- Working prototype with source code
- Findings report with recommendation
- Production pathway outline where results support it
Lo que preguntan los directivos
What if the research shows the idea will not work?
Then the engagement has done its job. A clear, evidence-based negative result prevents a larger commitment to something that would not have succeeded. We report those findings as carefully as positive ones, and often identify an adjacent approach worth considering instead.
Who owns the intellectual property from a research engagement?
Ownership is set in the engagement contract, and client-specific work products are typically assigned to the client. We identify any pre-existing tools or open-source components up front, so licensing obligations are understood before development begins.
How do prototypes move into production?
A prototype is built to learn, not to scale. When results justify it, we plan a production path that addresses architecture, security, data governance and integration, reusing prototype components that hold up and rebuilding those that were deliberately kept simple.
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