Hai Tran Ngoc.

Recoder · AI game production system

What if AI could understand an entire Unity game like an engineer?

Recoder turns project data into context, engineering workflows, and runtime evidence—instead of stopping at generated text.

Hai Tran Ngoc · AI Engineer

The problem

A Unity game does not fit inside a prompt.

Code, assets, scenes, prefabs, configurations, and runtime state live in different formats and evolve together.

01Code
02Assets
03Scenes
04Prefabs
05Configs
06Runtime
A general AI sees a prompt.Recoder sees the project.

Architecture

Four layers turn game data into verifiable work.

The model does not work alone. Data, semantic context, execution, and runtime verification form one feedback loop.

01

Data Foundation

Extracts and normalizes code, metadata, assets, scenes, prefabs, configurations, and runtime data into a shared source of truth.

PostgreSQLMinIOIndexes
02

Knowledge Layer

Maps relationships between classes, functions, assets, and project state so agents retrieve the right context for each task.

Semantic contextRelationshipsMemory
03

Workflow Automation

Coordinates planning, tool calls, multi-step execution, human gates, streaming events, and persistent results.

PlanningTool callingStreaming
04

Runtime Verification

Checks generated work against the running game through traces, logs, state, screenshots, video, and oracle differences.

TraceLogsOracle diff

≠ ChatGPTUnlike ChatGPT, Recoder reads project context, calls engineering tools, and is checked against runtime behavior.

Real product evidence

From project dashboard to a single function.

These are real screenshots from the working data and analysis system—not presentation mockups.

Dashboard screenshot from Recoder
01 · Dashboard178 jobs

Track project ingestion and analysis pipelines.

Script Map screenshot from Recoder
02 · Script Map2,696 classes

Navigate project structure and class relationships.

Class detail screenshot from Recoder
03 · Class detail1,411 functions

Inspect functions, reconstructed code, and dependencies.

Bridge · workflow execution

One request becomes a multi-step engineering workflow.

Bridge loads project context, plans dependencies, calls tools, streams each step, and keeps results for the next action.

Interface concept
BRIDGEActive workflow● STREAMING
LIVE EXECUTION

Reading project context1,411 functions indexed

Resolving dependencies50 methods linked

Calling Unity toolGame systems generated

Persisting artifactresult/game-rebuild-v3

▸ Writing next event to workflow stream…

Iris · runtime verification

Find the first point where behavior diverges.

Iris places the reference trace and candidate trace side by side, then turns the earliest meaningful difference into a focused repair task.

Interface concept
IRISRuntime verificationSCENARIO RUNNING

level_bootstrap_and_first_move

EVENTORACLECANDIDATERESULT
SceneLoaded14 ms15 msPASS
BoardInitialized42 ms44 msPASS
InputAcceptedtruetruePASS
MoveResolvedscore +20score +10DIVERGED
SCREENSHOT
frame_0182
RUNTIME LOG10:42:34 Board initialized with 24 cells
10:42:36 First divergence at MoveResolved.score

Recoder · Data → Context → Action → Verification

Recoder compresses a production process—not just a coding task.

Data → Context → Action → Verification. The result is a workflow a small engineering team can inspect, repeat, and improve.

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