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
Nick Kim

Nick Kim

CEO

Nick Kim is the founder and CEO of Gargantua, a technology firm working at the intersection of data and applied AI. His career spans large-scale consumer platforms and frontier machine learning — building products at Google and YouTube, and now building the data that multimodal models are trained on.

At YouTube, Nick led engineering and product initiatives across the platform's monetization and distribution businesses. He drove the global expansion of YouTube Music and Premium to more than 30 countries, built YouTube TV's third-party integration platform and the mobile carrier partnerships that ran on it, and launched YouTube Shopping — work that required getting engineering, licensing, and commercial terms to land at the same time, in dozens of markets at once.

He founded Gargantua in 2023. Its data practice serves labs training video-language and multimodal reasoning models, working with research teams on the part of the problem vendors usually skip: which benchmarks the model needs to move, and what the training distribution is missing to move them. That spans eval gap analysis and dataset design, exclusive video acquisition and licensing, and annotation methodologies built for learnable signal rather than throughput. Several of those methodologies have been co-designed with and adopted by client science teams.

The firm also builds applied AI products — knowledge-graph systems for institutional memory, conversational agents grounded in proprietary corpora, and tooling that makes an organization's own data queryable in natural language.

Nick holds a bachelor's degree in mechanical engineering from UC Berkeley and began his career founding a consulting and investment firm focused on financial technology for institutional asset managers. He is based in Seoul.