Ops Assist: AI-Powered Manufacturing Troubleshooting with Gemma 4

This is a submission for the Gemma 4 Challenge: Build with Gemma 4

What I Built

I built Ops Assist, an AI-powered manufacturing troubleshooting assistant designed for factory operators and industrial troubleshooting workflows.

The idea came from working in manufacturing environments where troubleshooting systems are often static, difficult to search, and unable to adapt to unusual combinations of symptoms or operator descriptions.

Traditional troubleshooting systems typically work like rigid decision trees:

Select department
Select machine
Select issue
Follow a predefined guide

That works for known/common failures, but real-world manufacturing issues are rarely that clean.

Operators usually describe problems in natural language:

“The conveyor only drifts after restarting.”

“The vacuum gripper randomly drops cartons during high-speed cycles.”

“The fault clears itself sometimes but comes back under load.”

I wanted to explore what happens when you combine AI reasoning with industrial troubleshooting workflows.

Ops Assist allows operators to enter:

machine symptoms
alarm or error codes
issue descriptions
severity levels
operational context

The app then uses Gemma 4 to generate:

likely causes
recommended diagnostic checks
safety reminders
escalation guidance
plain-English troubleshooting summaries

The project is currently an MVP prototype focused on validating AI-assisted troubleshooting workflows for manufacturing environments.

Screenshots

Demo Walkthrough:

The demo walks through:

entering a machine issue
AI-powered troubleshooting analysis
structured industrial troubleshooting output
safety and escalation guidance
real-time Gemma-powered response generation
Code

GitHub Repository:

npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev

Technical Overview
Stack
Next.js 16
TypeScript
Tailwind CSS
Gemini API
Gemma 4 26B A4B Instruct
App Router architecture
Architecture

The application uses a simple server-side AI architecture:

Frontend Form
↓
Next.js API Route
↓
Gemma 4 via Gemini API
↓
Structured JSON Response
↓
Industrial Troubleshooting Dashboard

The frontend collects operational issue details and sends them to a server-side API route.

The API route:

builds the troubleshooting prompt
injects safety constraints
requests structured JSON output
validates/parses the response
returns normalized troubleshooting data to the UI

The frontend then renders:

anomaly summaries
likely causes
recommended checks
escalation procedures
safety reminders
How I Used Gemma 4

Ops Assist uses the gemma-4-26b-a4b-it model through the Gemini API.

I chose the 26B A4B model because it offered a strong balance between:

reasoning capability
structured output quality
response speed
inference efficiency

Manufacturing troubleshooting is heavily reasoning-based. Operators rarely describe issues in perfectly structured ways, so the AI needs to interpret incomplete or inconsistent symptom descriptions and generate useful operational guidance from them.

Gemma 4 powers the core troubleshooting engine by:

interpreting operator-entered symptoms
identifying likely fault patterns
generating troubleshooting recommendations
providing safety reminders
recommending escalation paths

One challenge during development was balancing response quality with reliability.

Early versions of the troubleshooting prompt occasionally produced:

timeouts
malformed JSON
overly verbose outputs

To stabilize the MVP workflow, I:

reduced prompt complexity
constrained output structure
limited response lengths
implemented fallback handling
added JSON extraction/validation safeguards

This resulted in a much more reliable interactive troubleshooting experience.

I also found the use of an open model particularly interesting in the context of manufacturing environments.

Factories are often cautious about:

operational privacy
production data exposure
proprietary process information
external cloud dependencies

Open/local-capable models like Gemma create interesting possibilities for future on-premise industrial AI systems where operational data can remain inside the facility.

Example Use Cases

Ops Assist currently supports troubleshooting scenarios such as:

conveyor belt drift
intermittent jams
vacuum/gripper failures
sensor alignment faults
hydraulic pressure fluctuations
startup/shutdown inconsistencies
machine fault escalation guidance

Example operator input:

“Conveyor belt drifts sideways after stop/start cycles and causes jams. Only one lane is affected.”

Example Gemma output:

possible belt tension imbalance
startup torque inconsistencies
worn guide rails
roller alignment checks
escalation recommendation
lockout/tagout safety reminders
Future Improvements

Future versions of Ops Assist could expand into:

image-based troubleshooting
alarm screenshot analysis
multimodal diagnostics
maintenance history tracking
machine-specific troubleshooting knowledge
retrieval-augmented SOP/manual integration
local/on-premise AI deployment
Final Thoughts

This project was a really fun way to explore practical AI applications outside of the usual chatbot or productivity app space.

As someone working in manufacturing while transitioning deeper into tech, it was interesting building something inspired by real operational workflows and seeing how well Gemma 4 handled reasoning-heavy troubleshooting tasks.

Thanks for checking out Ops Assist.