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'],
)
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Data network visualization

Data Partnerships

Advanced video data sourcing for frontier AI models.

Video licensing Ethical data acquisition AI training data

What We Deliver

We provide professional content owners with a straightforward, high-value opportunity to license video archives for AI model training — creating new revenue from existing assets while ensuring responsible, compliant use.

  • Competitive licensing fees tailored to content quality and volume
  • Full handling of legal, compliance, and technical requirements with complete transparency
  • Guaranteed ethical restrictions: training-only use, no redistribution, no generative applications
  • Secure data transfer and processing with strict IP and privacy protections

Why We Partner

Direct partnerships with content providers enable us to source the highest-signal, professional-grade video data that powers breakthrough advances in video understanding — while delivering meaningful incremental revenue and impact for owners.

  • Access to clean, narrative-rich sources that outperform scraped or public-domain alternatives
  • Mutual alignment on responsible AI: fueling intelligence without competing with original media
  • Contribution to real-world applications in safety, analytics, and innovation
  • Long-term relationships with potential for ongoing or expanded licensing

Content We're Looking For

We prioritize high-resolution, full-length professional video with rich real-world complexity, diverse actions, and clear narrative structure.

Sports

Broadcast matches, isolated feeds, telemetry data, or archival footage across global leagues, regional competitions, and varied skill levels

News & Current Affairs

Full programs, segments, or archives from international, national, and regional broadcasters covering diverse events and scenarios

Public Safety & Surveillance

Cleared drone, body-cam, dash-cam, or CCTV footage capturing dynamic real-world interactions and anomalies

Talk Shows & Reality TV

Full episodes, segments, or archival content featuring unscripted conversations, diverse participant interactions, emotional dynamics, and real-life social scenarios

Content that includes existing semantic annotations (e.g., timestamps, entity labels, event descriptions, or transcriptions) will have additional value, as it accelerates high-precision model training.

How It Works

Our partnership process is simple, confidential, and provider-friendly.

  • Initial outreach and inventory review
  • Customized licensing proposal and agreement negotiation
  • Secure data transfer and verification
  • Prompt payment upon completion
  • Ongoing opportunities for additional content

Contact Us Confidentially

Ready to discuss your content? Reach out to our Data Strategy team.

David Kim

Data Strategy

dkim@gargantua.llc