import { Pipeline } from '@gargantua/core';
import { SchemaRegistry } from './registry';

const pipeline = new Pipeline({
  source: 'enterprise-lake',
  transforms: [
    normalize({ encoding: 'utf-8' }),
    deduplicate({ key: 'entity_id' }),
    enrich({ provider: 'knowledge-graph' }),
  ],
});

async function ingest(stream) {
  const schema = await SchemaRegistry
    .resolve(stream.metadata);
  return pipeline.run(stream, { schema });
}

export const DataMastery = {
  ontology: buildOntology(sources),
  validate: (record) => schema.check(record),
  pipeline: pipeline.connect(),
};

from transformers import AutoModel
from gargantua.cognitive import Agent

class CognitiveEngine:
    def __init__(self, config):
        self.model = AutoModel.from_pretrained(
            config.base_model,
            quantization='int8',
        )
        self.agent = Agent(
            reasoning='chain-of-thought',
            tools=config.tool_registry,
        )

    async def inference(self, prompt):
        context = await self.agent.plan(prompt)
        embeddings = self.model.encode(context)
        return self.agent.execute(
            embeddings,
            temperature=0.7,
            max_tokens=4096,
        )

terraform {
  required_providers {
    gargantua = {
      source  = "gargantua/ecosystem"
      version = "~> 3.0"
    }
  }
}

resource "ecosystem_platform" "main" {
  name     = "enterprise-mesh"
  region   = var.deployment_region
  scaling  = {
    min_nodes = 3
    max_nodes = 120
    strategy  = "predictive"
  }

  engagement_layer {
    analytics = true
    realtime  = true
    cdn       = "edge-optimized"
  }
}

const nexus = await connect({
  endpoint: process.env.NEXUS_URL,
  auth: { type: 'bearer', token },
});

await nexus.stream('telemetry', {
  window: '5m',
  aggregate: 'p99',
  filter: (e) => e.latency > 200,
});

model = Sequential([
  layers.Dense(512, activation='relu'),
  layers.Dropout(0.3),
  layers.Dense(256, activation='relu'),
  layers.Dense(num_classes, activation='softmax'),
])

model.compile(
  optimizer=Adam(lr=3e-4),
  loss='categorical_crossentropy',
  metrics=['accuracy', 'f1_score'],
)
Gargantua Group logo Gargantua Group
Jieen Chen

Jieen Chen, PhD

AI Software Engineer

Dr. Jieen Chen is the academic director and lecturer of the cybersecurity master’s program at Kepha Institute of Columbia International University (CIU). He works as an AI software engineer at Gargantua Group, where he designs AI solutions leveraging Large Language Models, multimodal models, and knowledge graphs.

At CIU, Dr. Chen serves on the academic administrative team and lectures in computer networks and coding, while conducting research at the intersection of AI and cybersecurity.

Jieen earned his doctoral degree in computer science from the University of Tübingen, Germany, after completing a master’s degree in photonics. His doctoral research focused on machine‑learning approaches to the design of computational photography systems.