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Forge 6 · Industrial AI · Powering the Shopfloor

Build the AI that runs
the factory floor.

The engineers built apps. The creators built films. Forge 6 belongs to the builders who make AI do real industrial work, catch a defect on a line, draft a quality document in seconds, predict which supplier is about to slip, tell a plant what a part should cost. Ship a working prototype against one of nine use-cases India's manufacturing sector has actually prioritised.

Your co-pilots Claude GitHub Copilot ChatGPT Gemini Hugging Face
Date
To be announced
Venue
NMG Gurugram
Build
~6-7 hours · solo or pod of 3
Seen by
Auto & manufacturing industry
The brief

Can you build AI that survives contact with a factory floor?

In 2026, India's automotive and manufacturing industry stopped talking about AI and started mandating it on the shopfloor, quality, design, supply chain, operations. The gap holding everyone back isn't strategy or money; it's people who can actually execute it on the floor. That's you. You won't build another to-do app, you'll ship a working prototype against one of the nine AI use-cases the sector has prioritised and the best builds get shown to the industry that needs them.

The exact problem + your starter dataset drop on Sprint morning. You arrive having pre-read the use-cases and set up your stack. We hand you the real thing.
The nine problems · pick your fight

Nine real, KPI-tagged use-cases. Each one moves a business number.

These are the AI use-cases the auto/manufacturing industry is actively prioritising for its supplier base. ⭐ flagship tracks are most demo-able in a day. 🏆 is the Grand Challenge.

Track A ⭐ · Defect-rate ↓

Eagle-Eye QC

A camera that spots and classifies defects on a part, live on the line.

Vision · YOLO-class detector + webcam
Track B ⭐ · Time-to-market ↓

The Quality Co-Pilot

An AI that drafts the dreaded DFMEA / APQP / PPAP quality docs and test cases.

RAG · LLM over a quality corpus
Track C ⭐ · Localization ↑

Should-Cost Engine

A model that estimates what a part should cost, so buyers can localise sourcing.

Python data stack · regression
Track D · Incidents ↓

Guardian Vision

Vision that catches a missing helmet, a restricted zone, a near-miss, before it's an incident.

Vision · safety analytics
Track E · Working capital ↑

Flow State

Optimise stock across the network or re-route logistics around a disruption.

Optimisation · forecasting
Track F · Handling time ↓

Early Warning

Predict which of your suppliers is about to drift on quality or delivery.

ML · supplier sensing
Track G · Future compliance ↑

Green Ledger

Turn India's new carbon rules (CCTS/PAT) into an emissions + credit strategy dashboard.

Analytics · dashboard
Grand 🏆 · First-time-right ↑

First-Time-Right

Predict how a component behaves before it's built or fuse two use-cases into an agentic shopfloor co-worker.

Agentic · Hermes × OpenClaw

You don't need to be a factory expert. The handbook teaches every track in plain language. Mechanical, production, mechatronics and industrial students, you have a real edge here and you're especially welcome.

The stack

A real 2026 industrial-AI bench.

Anchors

  • Claude Code / Copilot / ChatGPT / Gemini / an open LLM, your coding co-pilot and the brain for the GenAI co-pilot + agentic tracks.
  • A vision model (YOLO-class detector / lightweight CNN), the engine for QC and safety-vision, runs on a laptop + webcam.
  • Python data stack (pandas, scikit-learn, a forecasting lib), should-costing, inventory, supplier-sensing, carbon analytics.
  • A RAG setup (vector store + your LLM), for the Quality Co-Pilot drafting real documents.

Encouraged shelf

Hugging Face models · Ultralytics/YOLO · OpenCV · LangChain / LlamaIndex or an agent framework · Hermes-style orchestration + OpenClaw (NMG house agents) · a simple Streamlit / Gradio front-end for the demo · any open optimisation / solver library.

Full setup + per-track recipes are in the Builder Handbook.
How the day works

The Forge Clock

09:00Check-in · stack sanity check · pod formation
09:45Opening · industrial-AI landscape · brief + datasets drop
10:15Forge Clock starts · scope & sketch
11:00Build Block 1 · crude end-to-end thing running
14:00Build Block 2 · improve model, tie to KPI
16:30Freeze + record your showcase clip
17:00Hard stop · demos (3 min + 2 min Q&A)
18:30Winners · showcase notes · NMG fast-track
Scoring · 100 points

Judged like a plant manager would.

Working prototype (runs / demos live)20
Shopfloor impact (KPI moved)20
Use of AI & technical depth20
Real-world fit & data rigor15
Productionizable for a supplier15
Demo & storytelling10

Bonus +5: deployable on a real line (edge-ready / commodity HW / integration path). Judges: NMG senior engineers + invited automotive/industry guests. Demos are recorded for the vendor showcase reel.

What you walk away with

A portfolio piece with industrial credibility.

Shown to industry

Shortlisted builds (with your name) get packaged and shown to real Tier-1 / Tier-2 automotive & manufacturing contacts.

A direct line to NMG

Standout builders fast-track to an NMG Labs seat or a paid project. Cash prizes for the top 3 + a dedicated Girls-in-Forge prize. (Amounts revealing soon.)

Industrial AI Builder cert

The "Industrial AI Builder" certificate + a LinkedIn-ready one-pager of your build.

Gallery

Forge 6 in Photos

A few moments from the floor. Builders mid-sprint, the roundtable, the certificate handout.

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Register · Forge 6

Seats are limited and curated.

Forge 6 runs at NMG Gurgaon on a date to be announced. Seats are limited and curated — drop your details to lock your spot and get the use-case pre-read.

You're registered for Forge 6!

We'll email you the confirmation and next steps.

Contact

The strategy is written. The factories are waiting.

Forge Captain: Ayush Gupta · labs@nmgdigital.com. Follow nmg.labs and the Forge Discord to get the date drop first.

Forge Sprint 06 · Industrial AI · Powering the Shopfloor · NMG Gurgaon · date to be announced · An initiative of nmg.labs by NMG Technologies.

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