Roberto Bellido

The Methodology

Precision Engineering for AI

R.A.G.

RETRIEVAL AUGMENTED GENERATION

We don't just deploy models; we ground them in your reality. By architecting robust retrieval systems, we ensure your AI hallucinates less and retrieves more. Real-time data injection meets generative capabilities for actionable, truthful intelligence.

D.O.L.L.S.

DATA ORIENTED LARGE LANGUAGE SYSTEMS

A bespoke architectural framework designed by Roberto Bellido. DOLLS shifts the focus from model-centric to data-centric AI. We treat data not just as fuel, but as the structural integrity of the system, optimizing for throughput, context-awareness, and domain specificity.

System Architecture

Scalable, high-availability backend designs specifically optimized for AI workloads and vector database integration.

Game Intelligence

NPC behavioral trees enhanced by LLMs, dynamic narrative generation, and procedural content systems.

Vector Search

Implementation of Pinecone, Milvus, or Weaviate for semantic search capabilities within enterprise data.

Custom AI Agents

Autonomous agents built on the DOLLS framework capable of multi-step reasoning and tool usage.

CODE

The DOLLS Protocol

Bridging the gap between raw data streams and Large Language Model inference.

Structured Context Injection
Latency-Optimized Pipelines
Deterministic Output Guards

class DOLLS_Engine:
    def __init__(self, data_stream):
        # Initialize Retrieval Augmented Generation
        self.memory = VectorStore(data_stream)
        self.model = LLM_Interface(temp=0.2)

    def synthesize(self, query):
        context = self.memory.retrieve(query)
        return self.model.generate(
            prompt=query,
            grounding=context
        )

Let's Build

Ready to integrate RAG and DOLLS architecture into your ecosystem?

RB.