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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Gargantua Group
AI data pipeline visualization
For AI Research Labs

Your Research.
Our Data Operations.

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

Data Strategy Acquisition Annotation Pipeline Ops

Your researchers spend 80% of their time on data plumbing — sourcing, cleaning, labeling, reformatting. That's not science. That's ops work.

We're the data ops layer between your research agenda and your training loop. You spec the experiment. We ship the dataset.

3×
faster experiment cycles
κ >.85
inter-annotator agreement
100%
data provenance tracking

Hypothesis → Dataset → Training Loop

Six steps. You own step one. We own the rest.

Step 1

Experiment Brief

You define hypothesis, target modality, model architecture constraints, and acceptance criteria. We translate that into a data spec — class distributions, coverage requirements, edge-case sampling strategy.

Step 2

Data Strategy

Acquisition plan covering source selection, schema design, target class distributions, over-sampling for tail classes, and domain gap mitigation. We know what silently breaks models downstream.

Step 3

Source & Acquire

Licensed content partners, public repositories, and custom collection pipelines. Every sample provenance-tracked and rights-cleared. No gray-area scraping.

Step 4

Annotation & Labeling

Your taxonomy, our annotators. Multi-pass QA, IAA tracking (Cohen's κ), consensus adjudication, and edge-case escalation back to your team. Guidelines co-designed and iterated as ambiguities surface.

Step 5

Pipeline & Delivery

Versioned datasets land in your env — formatted for your framework with dataset cards, distribution stats, stratified train/val/test splits, and known-limitation docs. Plug in and train.

Step 6

Iterate & Refine

Models reveal weak slices → we close the loop. Rebalance distributions, mine hard negatives, expand tail coverage, curate adversarial eval sets. Tight feedback cycles.

We Speak Your Language

Not a vendor. A technical partner that understands your failure modes.

Distribution Shifts & Domain Gaps

We audit for covariate shift between train and deploy distributions and design sourcing to close the gap before it tanks eval metrics.

Annotation Taxonomy Design

We co-design hierarchical labeling schemas — handling multi-label ambiguity, mutually exclusive class boundaries, and annotation guideline iteration.

Dataset Versioning & Reproducibility

Full lineage tracking, deterministic splits, immutable snapshots. Reviewer 2 asks "what data?" — you have a precise, auditable answer.

Bias Auditing & Fairness

Demographic and contextual distribution analysis, representation gap flagging, and targeted eval sets that stress-test fairness before you publish.

Multi-Modal Data Alignment

Temporal synchronization, cross-modal correspondence, and metadata schemas across text, image, video, and audio modalities.

Evaluation Set Curation

Gold-standard eval sets with stratified sampling, difficulty tiers, and adversarial examples. Measure real capability, not benchmark overfitting.

Why Labs Choose Us

Speed

Weeks, not months, from spec to training-ready data. Stale hypotheses are worthless hypotheses.

Label Quality

Multi-pass QA, IAA metrics, consensus adjudication, and per-class quality reports. We quantify annotation certainty so you can trust your supervision signal.

Scale

10K pilot eval set to 10M+ production corpus. Same quality bar, same SLA. Your experiments shouldn't be bottlenecked by data throughput.

ML-Native Team

Ex-Google, DeepMind, YouTube, IBM. We've built ML infra at scale. When your scientist describes the problem, we don't need a tutorial.

Our team comes from

Google
DeepMind
YouTube
IBM
Cornell
Stanford
UC Berkeley

Ready to accelerate your next experiment?

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