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 intelligence visualization

Data Mastery

We own the data layer — from infrastructure to content.

Architecture Acquisition Annotation Pipeline Ops

Your data problem is one of two things — you lack the infrastructure to use data well, or you lack the right data entirely. We fix both.

From first-million-user startups to petabyte-scale enterprises. CV, NLP, multimodal. We architect the stack and source the signal.

Data Architecture

Build the stack that makes data usable.

We design and implement the full modern data stack — from raw ingestion through to downstream consumption. That means choosing the right warehouse or lakehouse architecture, defining schema evolution strategies, building idempotent transformation layers, and wiring up observability so you catch data quality issues before they reach production models.

Infrastructure & Modeling

  • Warehouse / lakehouse design (Snowflake, BigQuery, Databricks, Redshift)
  • Ontological frameworks and knowledge graphs for semantic reasoning
  • Dimensional modeling, slowly changing dimensions, and schema evolution
  • ELT/ETL pipeline orchestration (dbt, Airflow, Dagster)

Quality & Consumption

  • Data quality gates, anomaly detection, and freshness monitoring
  • Metrics layers, semantic definitions, and BI dashboard design
  • Feature stores for ML model serving (Feast, Tecton, custom)
  • Experimentation infrastructure and A/B test frameworks

Data Supply

Source the signal your models need.

Training data quality is the single highest-leverage variable in model performance — yet most teams under-invest in it. We handle the full supply chain: identifying the right sources, negotiating licensing agreements, building annotation ontologies, managing labeler workforces, running quality assurance with inter-annotator agreement tracking, and delivering the final dataset in the format your training pipeline expects.

Acquisition & Licensing

  • Licensed video, image, audio, and text content from verified partners
  • Strategic sourcing for hard-to-find domains and long-tail categories
  • Rights management, compliance-first provenance, and audit trails
  • Cost-optimized procurement at volume

Annotation & Delivery

  • Custom annotation ontology design matched to model objectives
  • Multi-tier QA with consensus adjudication and IAA tracking
  • Semantic context layering, bounding boxes, segmentation, and NER
  • Delivery in training-ready formats (TFRecord, Parquet, JSONL, HF Datasets)
Data partnership visualization

Data Partners

Content providers — monetize your archives while advancing responsible AI.

Learn more
AI research lab data pipeline

AI Research Labs

From experiment spec to training-ready dataset. You define the hypothesis — we deliver the data.

See how we work with labs

What You Get

  • ✓ Clean, context-aware training data
  • ✓ High-throughput ingestion pipelines
  • ✓ Full visibility across your ML data stack
  • ✓ Lower data costs via governance + high-ROI acquisition

How We Engage

  • 2–4 WK Data audit & strategic roadmap
  • 4–8 WK Metrics & experimentation foundations
  • 1–3 MO End-to-end data platform build
  • ONGOING Dataset supply & annotation ops
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