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

Our Services

Harness the power of data and AI. Explore how we transform raw information into intelligence, engineer bespoke cognitive systems, and architect platforms that scale engagement and revenue.

Data Ontology Design

Advanced analytics and insight generation from messy, heterogeneous data.

We convert raw, messy data into structured assets that fuel analytics and AI. Our pipelines clean, normalize, and model your data to reveal actionable insights.

  • Data ingestion, cleansing, normalization, and feature engineering
  • Metrics definition, dashboards, experimentation, and decision support
  • Modern data stack architecture and governance

Cognitive System Engineering

Bespoke ML and generative AI systems tailored to your workflows.

From prototypes to production, we design and implement intelligent systems that deliver measurable outcomes. We embed AI where it creates leverage, with reliability and safety.

  • Model selection, fine‑tuning, evaluation, and monitoring
  • Agents, retrieval, and workflow automation
  • Secure deployment, observability, and lifecycle management

Ecosystem Architecture

Platforms that scale engagement, collaboration, and monetization.

We design and build collaborative platforms that unify data, services, and users. The result: resilient ecosystems that accelerate growth and unlock new revenue streams.

  • API and event architectures for interoperable systems
  • Identity, permissions, and multi‑tenant design
  • Monetization, marketplace, and partner integrations