AI + Software Engineering · Enterprise AI R&D · Domain-tuned large & small models

aiXcoder Intelligent software development, built for the enterprise

Name: aiXverilog-4B
Brand: aiXcoder
Availability: InStock

Built on a multi-agent architecture — requirement understanding, task decomposition, code generation, test verification, delivery — aiXcoder closes the loop autonomously in complex R&D scenarios, lifting both development efficiency and delivery quality.

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CREDO

Scenario agent engineering

Enterprise-grade harness engineering
Engineered ultra-long context handling
End-to-end automation of R&D tasks

Large–small model collaboration

"Large × small" collaboration for the best efficiency in every scenario
Large models serve as the global cognition and decision hub, focused on complex tasks
Small models take the high-frequency, patterned, deterministic subtasks

Sovereign technology stack

Chip to model, self-controlled end to end
Deep optimization of enterprise compute resources
Training and inference in one software R&D appliance

Since 2013: Global leader in intelligent software R&D
100+ published: Top-venue papers, many field-defining
Level 4+: CAICT Trusted-AI triple certification
Critical domains: At scale in telecom · energy · aerospace · finance

New releaseMay 2026 · aiXapply-4B · Open source

aiXapply-4B

Frontier-model accuracy, on one consumer GPU.

aiXapply-4B is a specialized small model for the highest-frequency agent subtask: applying code changes. It merges AI-generated snippets precisely back into source files — a 4B model whose accuracy rivals hundred-billion-parameter models; deployable on a single consumer GPU, with task latency cut from minutes to seconds.

Read the details GitHub

Code-change accuracy: A 397B frontier model scores 94.8%
Inference speed: Single consumer-grade RTX 4090
Faster inference: Measured on a single consumer GPU
Lower task latency: From minutes down to seconds

PRODUCTS

Six agents across the full R&D lifecycle

CodeAgent Plugin

Team-level coding agent inside the IDE

Ships for VS Code and JetBrains. Agent / Plan / Ask — three modes, switched freely by task complexity — with cross-file edits, terminal and MCP tool calls, and project-level repo understanding and completion.

For the enterprise: it runs entirely inside your network, on your own models and knowledge bases. Team know-how is packaged into custom agents and distributed centrally, large–small model collaboration keeps compute cost down, and every AI-generated change can be reviewed and accepted or rejected one by one.

TestAgent

The software-testing agent

Comprehensive, efficient, intelligent test automation for your R&D team: unit, integration and system testing all covered.

CodeWiki

Codebase analysis & documentation agent

Parses project architecture and code logic to auto-generate project-grade documentation — updated as the code changes, shared on your intranet.

Requirement Agent

The requirements agent

Reshape software requirements and design with AI, turning R&D "translation loss" into competitive advantage. AI embedded across the requirement lifecycle — elicitation, structuring, generation, management — makes the loss between requirement and implementation controlled and traceable.

CodeAgent CLI

Coding agent in the terminal

Natural-language dialogue in the command line, bulk edits across files and repos, invokable from shell / Makefile / CI — same backend as the Plugin.

Math2Code

Formulas & algorithm specs → engineering code

A rule engine paired with large models for high-precision formula-document-to-code conversion: parses complex formulas in LaTeX, design docs and papers into high-reliability engineering code, with two-way code–formula tracing.

ENTERPRISE HUB · The enterprise AI control platform

One control platform for every agent.

Agents, models, knowledge bases, rules — scattered across teams and tools, AI spins out of control. Enterprise Hub is the control platform across all our products: end-to-end private, observable, controllable, auditable.

Agent governance — models, MCPs, Skills, knowledge bases and built-in rules configured once, then packaged by task, stack or role and distributed on demand, making agents configurable, packageable and shippable as a process
Program analysis — agents that truly understand your codebase, callable by agents themselves, across the whole software lifecycle
Knowledge reuse — team session knowledge and soft workflows pooled and shared, avoiding duplicated work and stabilizing delivery on complex tasks

Explore the platform

TRUSTED DEPLOYMENT

Deployed in critical domains

Chip to model, data governance to runtime — every number comes from a real development environment.

View solutions

Finance

35%+of code AI-generated

Production rollout at a major bank

8M lines written, 100k auto-corrections; 6,000+ users, 2,000+ daily actives — 90% of developers call it genuinely helpful.

Aerospace

20+mission software packages · 9 months

Industry-first control-algorithm software generator

Parses algorithm design documents into hardened, high-assurance C code: 200+ algorithm modules, serving China's lunar missions.

Energy

250k+lines of AI-generated code

China's first nuclear-domain code LLM

15 production projects, 29% of code AI-generated, 80%+ compilation pass rate — fully adapted on domestic chips.

Telecom

70%+test-case acceptance

Software-testing agent at production scale

Requirements → cases → scripts fully auto-generated: 60% execution pass rate, test-script development 50% faster.