In-depth combat: Build a self-developed and privatized AI communication center platform based on Go + Gemma 4

##Preface

** In 2026, the competition for large model (LLM) has shifted from "parameter quantities" to "landing scenarios". **

For developer studios, relying solely on public cloud APIs such as OpenAI for shell applications is facing a triple blow of runaway costs, privacy compliance and technology homogenization. This article will deeply dismantle the high-performance communication project axiom that our studio uses for personal use. See how we build an industrial-grade communication base through Go, and combine it with the locally hosted Google Gemma 4 model to build a high-walled private AI business closed loop.

##1. Strategic choice: Why is the "communication base" the golden moat for AI to realize?

Under the current wave of AI, **AI is essentially the "brain", while communication (IM + RTC) is its "senses" and "nerves."

** Extreme privacy (privacy compliance)**: B-side enterprises (financial, medical, legal) are extremely sensitive to data outbound. A privatized and deployed IM system combined with a locally hosted open source model is the only compliance solution currently available.
** Low-cost reasoning **: After hosting excellent open source models such as Gemma 4 locally, after the initial hardware (GPU server) investment, the subsequent marginal Token cost is almost zero.
** High-frequency interaction **: IM is the entrance with the highest user stickiness and the most intensive data generation. It is also the most natural interaction carrier for AI Agents.

##2. Deep disassembly of core architecture: Go's high concurrency approach

The core of axiom's design lies in: ** extreme performance, multi-terminal synchronization, and AI native support **.

2.1 Multi-Device Management

In order to support AI's seamless switching between different terminals (Web, iOS, Android), synchronous routing of messages is crucial. We designed a three-level nested mapping structure in manager:

go Copy

//Core logic reference: service/ws_ser/manager.go
type Manager struct {
    // UserID -> DeviceID -> Client instance
    Clients      map[uint]map[string]*Client
    Lock         sync.RWMutex

    //Group mapping: GroupID -> UserID -> DeviceID -> Client
    GroupClients map[uint]map[uint]map[string]*Client
}

When the AI generates a new reply message, the system must ensure that it can be fully distributed to all online terminals under the username:

go Copy

// BroadcastToUser ensures that responses generated by AI are fully distributed to all users 'online devices
func (m *Manager) BroadcastToUser(userID uint, data []byte) {
    m.Lock.RLock()
    defer m.Lock.RUnlock()

    devices, ok := m.Clients[userID]
    if ! ok { 
        return 
    }

    for _, client := range devices {
        //Asynchronous write channels to prevent single terminal network congestion from blocking the global main cycle
        select {
        case client.Send <- data:
        default:
            //Strategy: Abandon overloaded connections, avoid the spread of congestion, and protect system stability
            close(client.Send)
            delete(devices, client.DeviceID)
        }
    }
}

2.2 Performance optimization "black magic"

** Object reuse (sync.Pool)**: In high concurrency scenarios, frequent creation of message structures will cause a sharp increase in Golang memory GC pressure. We reuse the Message object through sync.Pool, which reduces memory consumption by approximately 30%.
** Globally unique ID (Snowflake)**: The snowflake algorithm is used to generate a 64-bit incremental ID to ensure that when the AI generates a large number of messages at the millisecond level, the system can still maintain strict timing and that messages are not repeated or leaked.

##3. Hosting Google Gemma 4: The business advantages of localized AI reasoning

We host Google Gemma 4 on the studio's own private GPU server and communicate with the Go backend through a high-performance RPC (such as gRPC) interface.

3.1 AI Message Interceptor Design

In axiom, AI is not a simple external plug-in, but is modeled as a *"shadow user" within the system.

sequenceDiagram actor User as real user participant Interceptor as AI Interceptor (Casbin) participant LLM as Gemma 4 Inference Engine participant WS as WebSocket Services User->>Interceptor: Sending messages Note over Interceptor: Authentication decision: Does an AI response need? alt meets trigger policy Interceptor->>LLM: Forwarding messages (Streaming) loop Token real-time streaming LLM-->>WS: Spit out Token fragment WS-->>User: WordPress Push end Else Ordinary Conversation Interceptor-->>WS: Regular Group chats/private chat delivery end

** Interception Logic **: When a message flows into the system,Interceptor will intelligently determine whether to forward the content to the Gemma4 inference engine based on the permission/subscription policy defined by Casbin.
** Streaming response **: AI replies do not need to wait for them to be fully generated. We use WebSocket's Framing to push the Tokens generated by Gemma 4 to the front end in real time, achieving a smooth experience of "word-for-word jumping" at the millisecond level.

3.2 LiveKit Real-Time Communication AI enhancements

Using the utils/livekit module, we can mount the AI bypass processing line during audio and video calls:

** Application scenarios **: Real-time translation of foreign trade meetings, game voice AI changes, and real-time monitoring of customer service emotions.
** Technical path **: Capture WebRTC audio streams-> Bypass push ASR -> Gemma4 semantic analysis-> text or synthetic speech feedback.

##4. Liquidation closed-loop: How to transform technology into flowing water?

4.1 Industry Vertical Agent Container

Through axiom's internally integrated Casbin (RBAC/ABAC) permission system, the fine-tuned models of knowledge bases in different industries are encapsulated as "digital employees":

** Cash method **: Charge based on account authorization or subscription based on model role. For example: legal consulting AI role, cross-border logistics real-time tracking AI.

4.2 "Private Domain + AI" Automatic Operation

Use the cron_ser(scheduled task service) in the system and the message_model to achieve 7 x 24-hour fully automatic private domain customer agent operation:

** Business logic **: AI regularly analyzes user conversation intentions-> automatically generates follow-up strategies-> writes offline_message(offline message table) waiting for sending, greatly improving conversion efficiency.

##Conclusion

Technology is always just a means. Solving problems and creating value is the core of business.

axiom provides a solid engineering base for privatizing instant messaging, while Gemma 4 gives it intelligent wings. In this era of technological explosion, don't be the waves that drift with the flow, but be the river that carries the waves.

[! NOTE]
This article was originally created by the tapcode development team. If your studio is also exploring the path of privatizing AI, you are welcome to share your experience at any time!