Welcome to SAL AI,

A Startup Redefining AI for the Benefit of Humanity

SAL AI is a newly launched technology startup on a mission to design and deploy scalable multi-agent AI systems capable of addressing some of humanity’s most critical and complex challenges.

Our team brings a foundation in strategic business consulting, systems design, and innovation leadership. Building on that experience, we are now focused on engineering intelligent systems that can learn, collaborate, and act autonomously, supporting high-stakes environments such as disaster response, global health, public safety, and enterprise-ready solutions.

These AI systems are designed not just for efficiency, but for impact—built to operate at scale, adapt in real time, and ultimately save lives. As we evolve, our platforms will be adaptable for embodied robotics, enabling physical intervention in life-critical scenarios.

We believe AI should be a force for public good—transparent, collaborative, and ethically grounded. At SAL AI, we’re not just building technology. We’re building the infrastructure for a safer, more resilient future.

If you're a partner, investor, researcher, or mission-driven technologist, we invite you to join us on this journey.

Salvador Beas

Founder

sbeas@sal-ai.com

www.sal-ai.com

AI ORCHESTRATION

The A.C.T.I.V.E. Multi-Agent Architecture

To build truly autonomous and reliable AI, we need more than a collection of tools; we need a unified architecture. The A.C.T.I.V.E. (Adaptive, Collaborative, Trustworthy, Iterative, Verifiable, Efficient) framework is a blueprint for developing sophisticated multi-agent systems that can learn, reason, and collaborate with verifiable trust.

This architecture integrates a state-of-the-art technology stack with a revolutionary evaluation fabric and a next-generation engine for on-the-fly learning, enabling agents to tackle complex, dynamic problems in both commercial and scientific domains.

Architectural Layers

The system is composed of four deeply interconnected layers, designed to support the entire lifecycle of an intelligent agent—from its foundational tools to its most advanced reasoning capabilities.

🧠 Layer 1: The Agent Core & Foundational Stack

This layer provides the essential components for each agent's existence and operation. It is built using the modern RAG stack to ensure modularity, scalability, and power.

LLMs (The Brain): The core generative and reasoning engine. Choice of model (LLaMA 3, Gemini, Claude) is tailored to the agent's specific function (e.g., creativity, analysis, safety).

Frameworks (The Skeleton): Orchestration tools like LangChain and LlamaIndex manage the complex internal workflows of each agent and facilitate communication between them.

Vector Databases (The Memory): High-speed vector stores like Qdrant, Chroma, or Weaviate serve as the long-term, scalable memory for each agent, allowing for instantaneous retrieval of vast amounts of contextual knowledge.

Data Extractors (The Senses): Tools like FireCrawl and MegaParser act as the agent's senses, ingesting and structuring raw data from documents, websites, and other sources into usable knowledge.

Embeddings & Access (The Universal Language): Models from OpenAI, BAAI, and SBERT, accessed via platforms like Hugging Face and Groq, translate all information into a universal language that the entire system can understand.

✅ Layer 2: The Evaluation & Trust Fabric (ORE)

This is the system’s nervous system, a continuous, automated verification layer that ensures every agent's output is reliable, consistent, and grounded in truth. It is powered by the Open RAG Eval (ORE) framework.

Human-Aligned Scoring (The "Magic"):

Retrieval (UMBRELA): Before an agent can even think, this component checks if it retrieved the right information, scoring its relevance on a scale from "unrelated" to a "perfect answer."

Generation (AutoNuggetizer): After the agent responds, this pipeline deconstructs its output into "nuggets" (atomic facts) and verifies each one against the source data. This process is critical for evaluating nuanced or technical information, ensuring accuracy down to the chip-spec level.

Key Trust Metrics: The fabric constantly measures:

Groundedness: Is the agent's answer based on the facts it was given?

Factuality & Citations: Is the agent telling the truth, and can it show its sources?

Consistency: Will the agent provide the same reliable answer to the same question every time? This is non-negotiable for regulated or safety-critical tasks.

This fabric provides a live dashboard to monitor and compare the performance of all agents, making trust a measurable and transparent feature of the architecture.

🔬 Layer 3: The Dynamic Representation Engine (LaTF)

This is the most advanced layer, transforming agents from knowledgeable retrievers into genuine learners capable of scientific discovery. It addresses a fundamental limitation of most generative AI: the assumption that understanding is fixed.

What it is: Latent Thermodynamic Flows (LaTF) is a groundbreaking framework that unifies representation learning (learning what is important in the data) and generative modeling into a single, seamless process.

The Power of On-the-Fly Learning: Instead of relying on static, pre-trained knowledge, agents equipped with the LaTF engine can learn and adapt their understanding of the world in real time. This is a game-changer for:

Complex Systems: Modeling proteins, RNA, or thermodynamic ensembles where the rules change with the environment (e.g., temperature).

Data-Sparse Fields: Generating accurate predictions and exploring possibilities even far beyond the limits of the training data.

True Scientific Discovery: Allowing an agent to form and test its own hypotheses about the underlying dynamics of a system.

When an agent uses LaTF, the "nuggets" evaluated by the ORE fabric represent something profound: not just retrieved facts, but emergent truths discovered by the AI itself.

The Unified Architecture in Action: A Multi-Agent Future

This architecture enables a new class of multi-agent systems that progress along the GenAI/Agentic Transformation Journey—from simple automation to fully autonomous, trusted collaboration.

Imagine a system for discovering new battery materials:

A "Data Agent" uses the Foundational Stack to ingest the latest materials science papers into a Vector DB.

A "Research Agent" queries this database to summarize known chemical stabilities, with its output continuously verified for factual accuracy by the ORE Trust Fabric.

For unexplored chemical combinations, a "Modeling Agent" activates its LaTF Engine. It doesn't just search for answers—it learns the underlying molecular principles and generates novel, stable structures that have never existed before.

A "Validation Agent" uses the ORE Fabric to score the novelty, consistency, and theoretical soundness of the generated structures, flagging the most promising candidates for human review.

This is more than just RAG; it's a closed-loop system of inquiry, discovery, and verification, paving the way for AI that doesn't just answer our questions but helps us find answers to questions we haven't even thought to ask.