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
Singularity AI Labs

AI Product
Launch Camp

A 3-week intensive program where teams of high school students design, build, and ship a working AI-powered product. From Zero to Shipped in 15 days.

3 Weeks Intensive
Ages 13–16
No prior coding required

Developed in Partnership With

Gargantua Education Quest X XPRIZE

Backed by STEMNovation, the original program from Valley Christian Schools that Danny and Hannah Kim started.

Program Overview

Singularity AI Labs's AI Product Launch Camp is a 3-week intensive program where teams of high school students build and ship a working AI-powered product. This is not a lecture series.

Students leave with a live product, a business case, a pitch deck, a demo video, and a professional portfolio — tangible proof that they can build with the most important technology of their generation.

Human-Centered Design

AI Engineering

Business Strategy

Who This Is For

This program is designed for highly curious, self-directed students who are motivated to build something real—not students who need to be told what to do at every step.

Selection Criteria

Genuine Curiosity

Beyond surface-level awareness. Has explored AI tools on their own.

Bias Toward Action

Initiative matters more than grades. Prioritizing students who start things.

Collaboration

Works well with others, communicates openly, can lead and follow.

Resilience

Products break. Plans change. Feedback is treated as information, not failure.

The Ultimate Pathway

Feeder Program to the XPRIZE Team

This camp is the exclusive proving ground for our elite 6-Month AI Incubator. We are aggressively scouting the next generation of visionary tech founders. Only the absolute top-performing AI masters from this camp will receive an invitation to join our long-term competitive teams, gaining access to partner resources and the chance to compete on the global XPRIZE stage.

Program Structure

Week 1

Discover & Design

Problem discovery, team formation, AI fundamentals, UI/UX design, business model canvas.

Goal: Validated Problem & Pitch
Week 2

Build & Iterate

Technical build sprints, workflow automation, AI integration, user testing, business validation.

Goal: Working End-to-End MVP
Week 3

Ship & Launch

Product polish, pitch preparation, demo day rehearsal, public showcase.

Goal: Demo Day Presentation

Daily Rhythm

9:00–9:15
Standup
Teams share progress, plans, and blockers.
9:15–10:30
Morning Session
Core instruction and guided exercises.
10:30–10:45
Break
10:45–12:00
Workshop / Lab
Hands-on building, team exercises, or guest sessions.
12:00–1:00
Lunch
1:00–2:15
Afternoon Sprint
Team project work with facilitator check-ins.
2:30–2:55
Sprint Continued / Review
Continued build time or peer review sessions.
2:55–3:00
Daily Retro
Whole-class reflection: one learned, one blocker, one win.

Instructors &
Guest Experts

The program is led by a dedicated facilitator ensuring high pace and team cohesion, supported by three industry experts who visit weekly for specialized instruction and hands-on team mentorship.

NK

Nick Kim

AI Industry Expert

AI product strategy, prompt engineering, the modern AI stack, industry trends. Former Google/YouTube leader shaping how students evaluate AI's real-world feasibility.

In-person weekly
DK

Danny Kim

XPRIZE Mentor & Quest X Founder

Cornell CS. World's most decorated K-12 XPRIZE mentor with an $800K win. Expert in leading elite student teams through global engineering challenges.

In-person weekly
EJ

Edward Jones

CTO & CS Professor

Computer science foundations, system architecture, scaling AI products, technical review. Teaching students how to break big problems into buildable pieces.

Remote weekly

Team Structure
& Roles

Students form teams of 5 on Day 1 around problems they want to solve. Each member takes a primary role, but everyone contributes across functions.

Product Lead

Owns the problem statement, user research, and product roadmap. Runs standups.

Design Lead

Owns UI/UX in Figma, user flows, and visual identity. Leads user testing.

Engineering Lead

Owns technical architecture, code repo, and deployment. Manages build sprint.

AI/Automation Lead

Owns AI integrations, prompt engineering, and n8n workflow automation.

Business Lead

Owns the business model canvas, competitive analysis, and pitch deck narrative.

Product
Guardrails

Students can build any AI product, but it must fit strict constraints to keep scope manageable and outcomes high-quality.

Must Do

  • • Solve a real problem for an identifiable user
  • • Use AI as a core feature (not a bolt-on)
  • • Be demonstrable in a live 5-minute demo
  • • Have a clear one-sentence value proposition
  • • Include at least one automated workflow

Must Not Do

  • • Require real money transactions
  • • Collect real personal data from external users
  • • Require native mobile apps (web-based only)
  • • Depend on paid enterprise API accounts
  • • Clone a major existing product (e.g. ChatGPT)

The 15-Day Sprint

A day-by-day look at how students go from zero to shipped.

Week 1

Discover & Design

Theme: Find a real problem, form your team, understand your user, design your solution, and validate that someone actually wants it.

Day 1: Discovery

Problem ideation, team formation around seeds, AI tool setup.

Day 2: Research

Prompt engineering workshop, cross-team user interviews.

Day 3: Architecture

Guest Experts: Nick Kim & Edward Jones on approach.

Day 4: Business & UI

Guest Expert: Danny Kim. Technical prototyping & feasibility.

Day 5: Week 1 Pitch

3-minute Problem+Solution pitch. Scope lock.

Week 2

Build & Iterate

Theme: Build the product. Every day is a sprint. Test with real users. Iterate. Make it work.

Day 6: Build Sprint 1

AI-assisted coding workshop. Core feature scaffolding.

Day 7: Automation

n8n workflow crash course. Integration sprint.

Day 8: AI Deep Dive

Guest Experts: Nick Kim & Edward Jones review code.

Day 9: User Testing

Guest Expert: Danny Kim. Technical stress-testing & MVP refinement.

Day 10: MVP Lock

Feature freeze. Only bug fixes from here.

Week 3

Ship & Launch

Theme: Polish the product, craft the story, rehearse the pitch, and launch to a live audience.

Day 11: Polish

Bug bash, landing page build, demo video recording.

Day 12: Pitch Craft

Nick Kim & Danny Kim: Storytelling & deck workshop.

Day 13: Rehearsal

Edward Jones Q&A prep. Two rounds of full dress rehearsal.

Day 14: Final Setup

Portfolio assembly, tech checks, reflection exercise.

Day 15: Demo Day 🚀

Public showcase, judge Q&A, product expo.

Assessment &
Portfolio

No Traditional Exams

Assessment is 100% portfolio and demonstration-based. What students leave with is exactly what they would show an employer or college admissions officer.

A working AI product demonstrated live
A 90-sec demo video for portfolios
A pitch deck & business case
GitHub repository with code contributions

Required Open-Source/Free Tools

AI Coding Tools (Claude Code, Cursor) LLM Assistants (Claude, Gemini) Figma n8n (Workflow Automation) GitHub