Analytics Dashboard Pro
by Enterprise Team • Updated 3 minutes ago
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'],
)
Architecture, acquisition & pipeline ops
We architect the stack and source the signal — from infrastructure to training-ready datasets.
CEO
Nick Kim is the founder and CEO of Gargantua, a data and applied-AI firm. Previously at Google and YouTube, where he scaled Music and Premium to 30+ countries and launched YouTube Shopping. He now works with AI labs on data strategy, video licensing, and annotation methodology.
CTO
20 years of software engineering across Google and Lockheed Martin; guides technical transformation with integrity and excellence.
Principal AI & ML Engineer
Mathematical prodigy and former Google engineer building innovative ML and LLM solutions.
ML Research Scientist
Marcus Schwarting, PhD, is a machine learning researcher and research engineer at Gargantua, where he focuses on model evaluation, data structures, and algorithmic methods. He is also an AI researcher at SUNY Buffalo.
Director of Data Science & ML Engineering
Lead data scientist at YouTube focusing on recommendations and causal inference; ex‑Expedia, former CTO at Neurobat AG (28% energy savings).
AI Software Engineer
AI engineer and CIU academic leader designing LLM, multimodal, and knowledge‑graph solutions; PhD in ML‑driven computational photography (University of Tübingen).