001 / Pontex Native IntegrationsUnity 6●IL2CPP & Burst Ready

On-device AI for
real-time games and
training simulation.

Pontex runs language models, speech recognition and voice inside Unity, on your user's own hardware. Build talking characters for games, or AI role-players for training that has to stay offline. No API keys. No cloud costs. 100% offline.

See Pontex in Action
// 002 — Simulation

AI role-players for training that has to be right.

Pontex Simulation turns a procedure into a scenario an AI can play. The trainee speaks, an AI counterpart answers by voice and stays within procedure, and every response is graded by plain, rule-based code. Scenarios are data, so a new procedure is a new file, not a new program. Everything runs on one computer, with no internet connection.

First scenario: MEDEVAC 9-line

A trainee calls in a medical evacuation request by voice. KILO 6, an AI dispatcher, answers on the radio, reads the request back with one deliberate mistake, and the trainee has to catch it and correct it. The debrief grades each of the nine lines, shows what was sent against what was correct, and records whether the readback error was caught and how fast.

MEDEVAC 9-line demo, running offline on one PC. The trainee sends all nine lines, KILO 6 reads line 3 back wrong, the trainee corrects it 8 seconds later, and the debrief shows GO on every line, 86 seconds into a 3-minute limit.

How it works

  1. 1. Speak. The trainee holds push-to-talk (the on-screen button or the space bar) and sends the request the way they would on a real radio.
  2. 2. Respond. The AI dispatcher answers by voice, locked to the scenario's radio phrases, and reads the request back with a planted error for the trainee to correct.
  3. 3. Grade. Code compares each line with the scenario's answer. The debrief shows GO or NO-GO per line, next to what was sent and what was correct.

Why it matters

  • Runs fully offline. Speech recognition, the AI and the voice run on the trainee's computer. No cloud, and no training data leaves the building.
  • Grading you can defend. Pass and fail come from fixed rules, not an AI's opinion. The same words always get the same score, and results are recorded in xAPI, the standard training-record format.
  • An AI that stays on script. The dispatcher is locked to the scenario's phrases. In 6 deliberate attempts to pull it off procedure, it held all 6 times.
  • Scenarios are data. The procedure, answers, dispatcher phrases and grading rules live in one scenario file.

Measured results

  • On a speaker the system was never tuned on, the grading agreed with the script on 173 of 180 lines as recorded.
  • In every test condition, clean and with added noise and radio distortion, it never passed a wrong line or a critical error.
  • More than 300 automated tests run on every change.

Measured on one desktop (NVIDIA RTX 5070 Ti, Intel i9-14900K) with Phi-3.5-mini and a Whisper speech model adapted for radio phrasing. The 9-line format follows publicly released U.S. Army doctrine (ATP 4-02.2). Call signs, grid references and casualties are fictional.

One engine, many scenarios

  • Live demoMEDEVAC 9-lineMedical evacuation request by radio, with readback and graded debrief.
  • NextMilitary radio reportsOther line-by-line reports, such as SPOT and SALUTE reports.
  • NextEmergency dispatchCall-taking for police, fire and ambulance, with a caller played by AI.
  • NextClinical handoffStructured patient handoffs between shifts and teams.
  • NextMaritime radioDistress and safety calls on VHF, with the coast station played by AI.
  • NextRegulated conversationsScripted, compliance-graded conversations in finance and customer service.

Any procedure built from structured spoken exchanges can become a Pontex scenario. Tell us yours.

Request a Simulation Demo
// 003 — Performance

Engineered for the main thread.

0ms
Cloud Round-Trip
100%
IL2CPP & Burst Ready
3
Sub-Systems (LLM/STT/TTS)
0
API Keys Required
// 004 — Local. Always.

Built for the Unity main thread.

Cloud AI ruins immersion with network latency and ruins budgets with API costs. Pontex is engineered specifically for high-frequency game loops.

Pontex runs inference in a native C++ backend, outside the managed C# heap, and keeps it off the main thread. Inference is dispatched across worker threads via Unity’s Job System and Burst Compiler, automatically utilizing the player’s CPU, CUDA, or Vulkan hardware.

// 005 — Infrastructure

Production-grade infrastructure, in the box.

01 / Pipeline

Asynchronous ECS pipeline.

Forget coroutines. Our LifecycleSystem drives inference natively using Unity's ECS. Token generation is monitored via JobHandle and sampled through Burst-compiled jobs, off the main thread.

ECSBurstJobHandle
02 / Tool Calling

Zero-reflection tool calling.

Give your AI the ability to trigger in-game events securely. Roslyn Source Generators detect [AITool] attributes at compile-time, emitting direct C# execution code — bypassing System.Reflection entirely for speed and strict IL2CPP compatibility.

RoslynIL2CPP[AITool]
03 / RAG

Native vector database.

Equip NPCs with persistent memory and world lore. The offline Knowledge Baker converts .txt lore into searchable .json vectors. At runtime, the Burst-compiled CalculateCosineSimilarityJob searches thousands of entities in O(n) time.

RAGBurstO(n) search
04 / Voice

Complete voice pipeline.

True multimodal immersion. Convert player microphone input to text instantly via the local Whisper STT engine. Stream LLM responses into the local neural voice engine for dynamic voice acting — synchronized with Unity's AudioSource.

Whisper STTNeural TTSAudioSource
// 006 — Developer Experience

From prototype to production.

Whether you prefer dragging components or writing unmanaged C#, Pontex adapts to your workflow.

For designers: the 5-minute NPC.

No engineering required. Use the Engine Dashboard to load models. Drop an AgentClient onto any GameObject, assign a Persona, and wire up standard UnityEvents.

Inspector
Agent Client (Script)
Model File
mistral-7b-q4.gguf
◎
System Prompt
RAG Settings
Knowledge Base
MerchantLoreData
◎
Max Context Docs
Voice (TTS)
Voice Model

For engineers: bare-metal API access.

Need absolute control? Interface directly with RuntimeNative. Allocate unmanaged pointers, schedule batch inference jobs, and build fully memory-managed pipelines.

AILogicSystem.cs
using Unity.Entities;
using Unity.Jobs;
using Unity.Collections;
using Pontex.Native;

public partial struct InferenceSystem : ISystem
{
    public void OnUpdate(ref SystemState state)
    {
        // 1. Allocate unmanaged request buffer
        var requests = new NativeArray<TokenRequest>(
            count, Allocator.TempJob);

        // 2. Schedule Native C++ Inference Job
        var inferenceJob = new EvaluateLLMJob
        {
            RuntimePtr = PontexRuntime.GetSharedInstance(),
            Requests   = requests,
            MaxTokens  = 128
        };

        // 3. Dispatch on worker threads
        state.Dependency = inferenceJob.ScheduleBatch(
            requests.Length, 32, state.Dependency);
    }
}
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