SIH26117/Smart Automation

Air-gapped industrial AI workbench application

A self-hosted, air-gapped AI workbench application for industrial users to perform secure tasks on local GPU servers without internet access.

Mangalore Refinery and Petrochemicals Limited (MRPL) · Software

Teams already in: Not read yet
Buildable in 36 hours: ModerateOur estimate, from their wording
Hard blockers: NoneNothing you must source first
Submissions close: 15 Sept9 days left

Make this one of our two Compare with others

Signing in saves it for your whole team — everyone on your invite link sees the same two entries. Anything you shortlisted while signed out comes with you.

Submit on sih.gov.in Dataset

Whether this suits your team

What you would have to build

Decomposed from what the description asks for. Nothing added.

01
Local Backend and Routing
A backend that supports multiple open weight models and routes tasks to the correct model based on requirements.
02
Agentic Task Engine
An engine that plans multi-step work, executes local tools, and iterates on tasks.
03
Multimodal and OCR Processor
A component using on-device OCR and vision models to process scanned PDFs, drawings, and images.
04
Local Knowledge Base Connector
A connector that grounds responses in local manuals, SOPs, and past correspondence.
05
Deliverable Generator and Sandbox
A system to run code in a sandbox and output real files like Word, Excel, and presentations.

What they ask for, and what they leave open

Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.

They state

The jury will check whether the system operates entirely offline using visible network monitors or logs to prove zero external calls. They will test model auto-selection across different tasks, end-to-end agentic workflows, document processing, and sandbox code execution.

They never say

Specific hardware models or GPU VRAM limits available at the venue
Exact open-weight models preferred by the organization

A jury can still ask about these. Decide them deliberately rather than by accident.

What they'll ask you

Generated from the brief's own wording and the competition's published rules — never from a guess about what this ministry prefers.

Specific hardware models or GPU VRAM limits available at the venue?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Exact open-weight models preferred by the organization?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.

9 more— the ones a panel gets to if there is time

The brief asks for local backend and routing. How would you build that?
Decoded from SIH26117 itself — A backend that supports multiple open weight models and routes tasks to the correct model based on requirements. The brief asks for it by name.

The data

What the organisers attached, and what the brief assumes you can get.

Dataset from the organisers: Provided — open it
Does the brief need data?: Yes, and they gave it to you.

Also from Mangalore Refinery and Petrochemicals Limited (MRPL)

Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.

Take this to an AI

Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.

Who has this problem, what already exists, and what you would have to find out.

# Context: India's Smart India Hackathon 2026

I am on a student team entering SIH 2026. Problem statement SIH26117, set by Mangalore Refinery and Petrochemicals Limited (MRPL).
Its official title, word for word: "Sovereign On-Premise Agentic AI Workbench using Open-Weight Multimodal LLMs for Confidential Industrial Work"
Theme: Smart Automation. Category: software.

In plain terms: A self-hosted, air-gapped AI workbench application for industrial users to perform secure tasks on local GPU servers without internet access.

## The organisers' full description, verbatim

• Background Refineries, PSUs, defence-linked manufacturing units and government offices generate a lot of routine but sensitive knowledge work. Approval notes, board presentations, engineering calculations, code for internal tools, review of scanned drawings and inspection reports. None of this can go through cloud AI assistants like Claude or Codex because the underlying data is confidential: Piping & Instrument Diagrams, financials, vendor negotiations, unreleased designs, internal correspondence, confidential business strategies etc. Company policy keeps this data on premises, so people either do the work manually resulting in productivity gain, or they quietly paste confidential material into public tools anyway. Open weight large reasoning models have reached a point where a genuinely useful assistant built on them is realistic. But nothing deployable exists today that industrial users can actually work with the way they use Claude or Codex. • Description The idea is a self-hosted, air gapped AI workbench running entirely on the organization's own GPU server. Nothing leaves the premises. The backend should not be locked to one model. It needs to support multiple open weight models at once and automatically pick the right one for a given task based on what that task needs, a coding request handled differently from a document summary request. New open weight models should be addable later without redesigning the system, since this space is moving fast. The assistant also needs to actually act like an agent. Plan out multi step work, call local tools such as file read and write, code execution in a sandbox, spreadsheet work, internal document search, and iterate on a task instead of answering once and stopping. It needs to handle more than text too: scanned PDFs, handwritten notes, engineering drawings, photographs, read through on device OCR and vision models. Output should be real deliverables, approval notes, PPT/Word/Excel files, working code, calculations with steps shown, not just chat replies. And it needs to ground itself in the organization's own manuals, SOPs and past correspondence through a local knowledge base connector, again with nothing going external. • Expected Solution A working local deployment, demonstrable on a single workstation or server with a mid range GPU (use a smaller open weight model if 120B class hardware isn't available at the venue), that shows model auto selection across at least two different task types. An agentic task carried through end to end, for example reading a scanned inspection report, pulling out key findings and drafting an approval note as a Word file. A coding task run and verified in a sandbox. A multimodal task involving image or scanned document understanding. The system should also show, through logs or a visible network monitor, that no external calls are made at any point. That's the actual proof of the sovereign claim, not just a statement of it.

## What the brief never says

- Specific hardware models or GPU VRAM limits available at the venue
- Exact open-weight models preferred by the organization
## Constraints that are not negotiable

- The judged build is a prototype demonstrated in roughly 36 hours by students, not a product.
- A jury evaluates it against this problem statement's own wording, not against general merit.
- We submit on sih.gov.in; the problem statement number and title above must match exactly.
- Your solution must not have appeared in any previous event or programme, of any sort.
- The organisers attached a dataset, so build against theirs rather than inventing one.

## What I want from you

1. Who actually has this problem in India, and what they do about it today.
2. What already exists — government systems, open-source projects, published research — that we should not rebuild.
3. For each item under 'what the brief never says', what the realistic answer is likely to be and how I could verify it.
4. The two or three things about this domain that a student team would get wrong without local knowledge.
## How to answer

- Everything above between quotation marks is the organisers' own wording. Treat it as the requirement.
- Do NOT invent requirements the brief does not state. If something matters and is not stated, say so and ask me, rather than assuming.
- Where you are inferring rather than reading, mark it as your inference.
- Do not guess what this ministry wants from its name. Its wording above is the only evidence.

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Problem statement facts assembled by Wynit from Mangalore Refinery and Petrochemicals Limited (MRPL)'s published brief for SIH 2026.
The plain-English summary, the list of parts and the evaluator read are our interpretation of that text; the quoted title and the full description are theirs.

The government's original description (462 words)

• Background Refineries, PSUs, defence-linked manufacturing units and government offices generate a lot of routine but sensitive knowledge work. Approval notes, board presentations, engineering calculations, code for internal tools, review of scanned drawings and inspection reports. None of this can go through cloud AI assistants like Claude or Codex because the underlying data is confidential: Piping & Instrument Diagrams, financials, vendor negotiations, unreleased designs, internal correspondence, confidential business strategies etc. Company policy keeps this data on premises, so people either do the work manually resulting in productivity gain, or they quietly paste confidential material into public tools anyway. Open weight large reasoning models have reached a point where a genuinely useful assistant built on them is realistic. But nothing deployable exists today that industrial users can actually work with the way they use Claude or Codex. • Description The idea is a self-hosted, air gapped AI workbench running entirely on the organization's own GPU server. Nothing leaves the premises. The backend should not be locked to one model. It needs to support multiple open weight models at once and automatically pick the right one for a given task based on what that task needs, a coding request handled differently from a document summary request. New open weight models should be addable later without redesigning the system, since this space is moving fast. The assistant also needs to actually act like an agent. Plan out multi step work, call local tools such as file read and write, code execution in a sandbox, spreadsheet work, internal document search, and iterate on a task instead of answering once and stopping. It needs to handle more than text too: scanned PDFs, handwritten notes, engineering drawings, photographs, read through on device OCR and vision models. Output should be real deliverables, approval notes, PPT/Word/Excel files, working code, calculations with steps shown, not just chat replies. And it needs to ground itself in the organization's own manuals, SOPs and past correspondence through a local knowledge base connector, again with nothing going external. • Expected Solution A working local deployment, demonstrable on a single workstation or server with a mid range GPU (use a smaller open weight model if 120B class hardware isn't available at the venue), that shows model auto selection across at least two different task types. An agentic task carried through end to end, for example reading a scanned inspection report, pulling out key findings and drafting an approval note as a Word file. A coding task run and verified in a sandbox. A multimodal task involving image or scanned document understanding. The system should also show, through logs or a visible network monitor, that no external calls are made at any point. That's the actual proof of the sovereign claim, not just a statement of it.