AI “What Went Wrong?” — An Incident Detective Using Gemma 4 and Laya

Likhitha Indukuri — Thu, 08 Oct 2026 10:29:16 +0000

What if an AI system could look at an incident scene, identify the visible evidence, suggest what might have happened, and then evaluate those possible explanations based on the evidence?

That is the idea behind our hackathon project, AI “What Went Wrong?” — Incident Detective, built by **Team Tech Fusion.

What We Built

Our system takes two inputs:

An image of an incident scene
A short description or context about the incident

For example, the user could upload an image of a messy laboratory workspace and provide context such as:

“This incident happened in an electronics laboratory.”

The system then performs the analysis in two stages.

Stage 1 — Gemma 4: See and Understand

We use Gemma 4's multimodal capabilities to analyze the uploaded image together with the provided context.

Gemma extracts:

Visible objects
Visible conditions
Evidence
Possible explanations
Uncertainties

The explanations are treated as hypotheses, not confirmed facts.

For example, Gemma might identify:

Stains on a work surface
A bottle lying near the stained area
Protective gloves
Residue on containers

and suggest possible explanations such as:

Accidental spill
Routine laboratory activity
Material handling issue

The output is returned as structured JSON so that it can be passed directly to the next stage.

Stage 2 — Laya: Evaluate the Evidence

The structured output generated by Gemma is then passed to Laya, an open-source decision engine.

Laya evaluates the possible explanations against the evidence identified by Gemma and produces a confidence score for each hypothesis.

The dashboard then displays the explanations as cards, with the highest-confidence explanation highlighted as the most likely explanation.

The goal is not to claim certainty, but to make the reasoning process transparent:

Gemma sees → Evidence is extracted → Laya evaluates → Explanations are ranked

Architecture

text
User
│
├── Incident Image
└── Incident Context
│
▼
React Frontend
│
▼
FastAPI Backend
│
▼
Gemma 4
│
▼
Structured Evidence

Hypotheses
Uncertainties │ ▼ Laya │ ▼ Ranked Explanations
Confidence Scores │ ▼ React Dashboard

Technology Stack

Gemma 4 — multimodal incident scene analysis
Laya — evidence-based hypothesis evaluation
Python — AI/backend implementation
FastAPI — backend API
React + Vite — frontend dashboard
Google GenAI SDK — Gemma API integration

Why This Approach?

A simple vision model can tell us what is visible in an image, but our goal is to go one step further.

We want to separate:

What can be seen from what might have happened.

Gemma handles scene understanding and hypothesis generation, while Laya independently evaluates those hypotheses using the evidence.

This separation gives us a clearer and more explainable pipeline instead of asking one model to directly decide what happened.

Responsible AI

The system is designed as an incident hypothesis and reasoning assistant, not as a forensic or safety authority.

The output represents possible explanations based on the available evidence. It should not be treated as definitive proof or as an accusation against a person.

We also explicitly preserve uncertainties so that the system can communicate what cannot be determined from the available information.

Links

GitHub Repository:
[Add your GitHub repository link here]

Laya:
https://github.com/NandhaKishorM/laya

Gemma:
https://ai.google.dev/gemma

Team: Tech Fusion

What's Next?

We are currently integrating the individual modules into a complete application.

Our next steps include:

Connecting the React upload interface to the FastAPI backend
Passing user-provided images and context to Gemma
Connecting Gemma's structured output to Laya
Building the final incident analysis dashboard
Testing the system across different types of incident scenes

The core idea is simple:

Gemma sees. Laya decides. Our system explains.

Built as part of our hackathon project by Team Tech Fusion

DEV Community: Likhitha Indukuri