We transform your business with Artificial Intelligence

- Empower your business with our RAG technology.
- Our advanced system allows you to easily interact with unstructured databases and documents to obtain accurate answers in natural language.
- Optimize your processes and make informed decisions with our RAG solution.

Solution Deployment Method

At Artifex International Consulting Group, we understand that every biotechnology project is unique. Our approach is based on a structured process to ensure success from initial design to final implementation.

Below we describe the key stages of our deployment method:

We develop a customized conceptual scheme, aligned with the most urgent and critical needs you have identified as a priority. This conceptual design forms the foundation upon which we will build the solution.

Based on the conceptual design, we develop a detailed plan to execute a Proof of Concept (POC). This phase is crucial to validate the feasibility and effectiveness of the proposed solution before proceeding with full implementation.

After validating the POC, we implement an MVP Solution, focused on the essential functionalities and prioritized according to your specific requirements. The MVP allows for a fast and efficient integration, offering an effective solution tailored to your needs.

Finally, we closely monitor the results in production. Our DevOps approach ensures a constant flow of improvements and updates, continuously optimizing the solution to maintain optimal performance.

Improve your team's productivity

Streamline your workflow with our RAG platform and get accurate answers quickly and easily.

How Our Solutions Work

Recovery Augmented Generation (RAG):

Improves the accuracy of responses generated by language models by combining specific information retrieval with text generation. This allows researchers to access relevant data and generate coherent and contextual reports.

Mixture of Agents (MOA):

We use a collaborative multi-agent approach to solve complex problems. MOA integrates different models to address specific challenges, offering more robust solutions tailored to particular needs.

Large Language Models (LLMs):

These advanced models enable fluid text processing and generation, providing support in the generation of scientific reports, article writing and complex data analysis.

Machine Learning Techniques:

We apply machine learning algorithms to analyze large volumes of data, identify patterns, and make accurate predictions that inform the development of new biotechnological solutions.

Integration into your own business system

Our Commitment

We are committed to resource optimization, project scalability, and the use of advanced technologies. We use innovative techniques such as Mixture of Agents (MOA) and Large Language Models (LLM) to improve efficiency in your research and development processes.

Challenges of the Structured Recovery Augmented Generation (RAG).

Retrieval Augmented Generation (RAG) improves the performance of chatbots based on large language models (LLM), but faces some challenges:

Hallucinations:

RAG is designed to minimize hallucinations by providing more accurate and relevant responses by leveraging specific contextual data.

Fine-tuning complexity:

While tuning RAG models requires careful planning, this process ensures that models are perfectly tailored to needs, effectively combining retrieval data with response generation.

Data quality and bias:

Results depend on the quality of the data retrieved. Biased data will lead to biased answers, so it is crucial to ensure the impartiality of the information.

Lack of evaluation metrics:

The quality of the data retrieved translates into more accurate and useful answers. We work to ensure that the data is unbiased and of high quality, maximizing the effectiveness of the responses generated.

Our Objective

We support our clients in each of the following stages:

1.- Design Proof of Concepts - Adapted to your priorities.
2.- Plan the Proof of Concept (POC) in Detail - To validate feasibility.
3.- Implement the MVP Solution - Focused on essential functionalities.
4.- Track and Optimize Results in Production (DevOps) - Continuous improvements and updates.
5.- Process post-translational modifications - Analysis of structural proteins and enzymes, as well as studies on protein folding and molecular docking.

Support in Scientific Research

Are you interested in generating new scientific knowledge about the fundamental mechanisms of your research subject in basic research? Would you like to receive support from GenAI RAG to improve your clinical research or therapies? Select all that apply:

Basic Research - Generation of fundamental knowledge.
Applied Research - Application of knowledge to specific problems.
Applied Research Development - Expansion and deepening of applied research areas.
Technological Development - Creation and advancement of innovative technologies.

Services Offered

Identification of Knowledge Gaps - Analysis to identify areas not explored in current research.

Scientific Hypothesis Development – Creating hypotheses based on existing data or emerging trends.

Simulation of Theoretical Models - Generation of predictive models to validate hypotheses.

Generation of Scientific Reports and Articles - Support in the writing and presentation of results in scientific publications with GenAI.

Study of Structural Proteins and Enzymes - We evaluate and model the three-dimensional structure of proteins and enzymes to better understand their functions and applications.

Research in Protein Folding and Molecular Dynamics - We use tools such as RoseTTAFold, RFDiffision, MAFFT, Autodock 4, Autodock Vina, PyMol and GROMACS for the analysis and simulation of protein folding and molecular docking.

Generation of Scientific Reports and Documentation - We help generate scientific reports and articles by combining recovered data with text generation with several layers of GenAI Models. This ensures accurate and well-structured documentation, essential for communicating research findings and results, with a capacity of 1000 whitepapers.