The LLM Reliability Engineering Platform
LLM 可靠性工程平台

Your LLM works in staging.
It fails in production. We fix that.

你的 LLM 在 staging 表现完美。
一上线就故障。 我们来修。

Correctover is the first LLM Reliability Engineering platform—a complete engine for self-healing, semantic validation, and drift detection. Not another gateway. The reliability layer your AI stack is missing.

Correctover 是第一个 LLM 可靠性工程平台——集自愈、语义验证、漂移检测于一体的完整引擎。不是又一个网关。是你的 AI 栈缺失的那层可靠性。

✗ Without Correctover
✗ 无 Correctover
[03:42:17] 503 openai down
[03:42:18] Gateway → anthropic (HTTP 200)
[03:42:18] ✗ Semantic drift — wrong answer delivered
[03:42:19] ✗ 10× cost overrun
correct
over
✓ With Correctover
✓ 有 Correctover
[03:42:17] 503 openai down
[03:42:17] ✓ Level 1 retry → failed
[03:42:18] ✓ Level 2 — degrade to anthropic
[03:42:18] ✓ 6-dim validation → deepseek
[03:42:19] ✓ Verified response delivered
🛡️ Self-Healing Semantic Validation 📡 Drift Detection 22µs validation 🔓 Apache 2.0
correctover-self-healing-demo

✗ Without Correctover

✗ 无 Correctover

Waiting for trigger...

✓ With Correctover

✓ 有 Correctover

Waiting for trigger...

LLMs Are Unreliable by Default.
That's Not a Bug—It's Physics.

LLM 天生不可靠。
这不是 Bug—这是物理规律。

  • 🎲

    Non-deterministic by nature

    本质上的非确定性

    LLM outputs are probabilistic. Traditional SRE tools can't fix what they can't predict.

    LLM 输出是概率性的。传统 SRE 工具无法修复不可预测的问题。

  • 👁️

    Monitoring sees — it can't heal

    监控"看得见"但"修不了"

    Existing tools (gateways, observability) only alert you. You still fix everything manually.

    现有方案(网关、监控)只能告警。修复还得人来做。

  • 🌊

    Drift, hallucination, provider instability

    漂移、幻觉、Provider 不稳定

    These are everyday production realities—not edge cases. Your current stack has no answer.

    这些是生产环境的日常,不是边缘 case。你现有方案无解。

You don't need a better gateway. You need a new engineering paradigm—an LLM Reliability layer that detects, validates, heals, and learns automatically.

你不需要更好的网关。你需要一个全新的工程范式——能自动检测、验证、修复和学习的 LLM 可靠性层。

A Complete Reliability Loop.
No Gaps.

完整的可靠性闭环。
没有缺口。

🔄

Self-Healing Engine

自愈引擎

Catches failures before your users do.
在你的用户发现之前修好故障。

MAPE-K adaptive loop with 4-level recovery: Retry → Degrade → Switch → Flywheel. Circuit breakers, rate limiting, and bulkheads prevent cascading failures. Semantic boundaries keep healing safe.

MAPE-K 自适应循环,4 级恢复阶梯:重试 → 降级 → 切换 → 飞轮学习。断路器、限流、隔离舱防止级联故障。语义边界确保自愈不越界。

  • 4-level recovery (Retry → Degrade → Switch → Learn)
  • 4 级恢复(重试 → 降级 → 切换 → 学习)
  • Circuit breaker + rate limiter + bulkhead
  • 断路器 + 限流 + 隔离舱
  • Semantic boundary enforcement
  • 语义边界控制

Semantic Validation

语义验证

Knows when an answer is wrong—not just when it's slow.
知道答案对不对,不只是响应快不快。

5 contract strategies: Schema, Deterministic Hash, Similarity, Entity, Prohibited Patterns. 6-dimension protocol validation at the MCP layer. Catches "looks correct but is wrong" responses.

5 种 Contract 策略:Schema、确定性哈希、相似度、实体、禁止模式。MCP 层 6 维协议校验。拦截"看起来正常但实际错误"的响应。

  • 5 contract strategies for any use case
  • 5 种 Contract 策略覆盖所有场景
  • 6-dimension MCP protocol validation
  • MCP 层 6 维协议校验
  • 22µs per validation (P50)
  • 每次验证仅 22µs (P50)
📡

Drift Detection

漂移检测

Watches your models so you don't have to.
替你盯着模型,不用你操心。

4 drift categories: Semantic, Model Behavior, Routing Strategy, Provider Performance. Sliding window + EMA trend tracking. INFO/WARN/CRITICAL alerts, proactive not reactive.

4 类漂移检测:语义、模型行为、路由策略、Provider 性能。滑动窗口 + EMA 趋势追踪。INFO/WARN/CRITICAL 三级告警,提前预警而非事后补救。

  • 4 drift categories monitored continuously
  • 4 类漂移持续监控
  • Sliding window + EMA trend analysis
  • 滑动窗口 + EMA 趋势分析
  • Proactive 3-tier alerting (INFO / WARN / CRITICAL)
  • 三级主动告警(INFO / WARN / CRITICAL)

🔄 Drift Detection triggers Self-Healing → Semantic Validation validates the fix → results feed back into Drift baselines. A continuous reliability loop. 🔄 漂移检测触发自愈 → 语义验证校验修复 → 结果反馈回漂移基线。 持续的可靠性闭环。

Everything Else You Need.
Built In.

你需要的一切。
开箱即用。

Not a single-point tool. A complete platform that covers the full LLM reliability lifecycle.

不是单点工具。是一个覆盖完整 LLM 可靠性生命周期的平台。

🧠

Intelligent Routing

智能路由

Cost / Latency / Quality strategies with complexity classifier. 9 providers unified behind one API.

成本/延迟/质量三种策略,复杂度分类器自动判断。9 家 Provider 统一接入。

Cost-optimizedLatency-aware9 providers
💾

Agent Crash Recovery

Agent 断点续跑

Checkpoint-based resume. Never start an agent conversation from scratch after a crash.

基于断点的续跑机制。Agent 崩溃后再也不用从头开始。

Auto-resumeCost savingsLong-running agents
📊

Observability

可观测性

Health scoring, cost tracking, carbon tracking, MAPE-K trace visualization, real-time web dashboard.

健康评分、费用追踪、碳排放追踪、MAPE-K 链路可视化、实时 Web 仪表盘。

Real-timeCarbon-awareMAPE-K traces
🧪

Benchmark Suite

基准测试套件

6 standard scenarios: presence validation, effectiveness validation, boundary testing, fault injection.

6 个标准场景:存在性验证、有效性验证、边界测试、故障注入。

6 scenariosFault injectionCI/CD ready

See the Problem. Fix It. Learn from It.

看见问题。修复它。从中学习。

Traditional Gateways 传统网关 Observability Tools 可观测性工具 Correctover
See the problem 看见问题
Understand semantics 理解语义
Fix it automatically 自动修复
Learn from failures 从故障中学习
Full reliability loop 完整可靠性闭环
LiteLLM routes. · Helicone watches. · Portkey configures. ·
Correctover ensures.
LiteLLM 做路由。 · Helicone 做监控。 · Portkey 做配置。 ·
Correctover 来保障。

Validated at Every Level.

每一层都经过验证。

Performance benchmarks, fault injection validation, and architectural transparency. Not marketing numbers—production data.

性能基准、故障注入验证、架构透明。不是营销数字——是生产数据。

22µs
Diagnosis P50
故障诊断 P50
1M samples
47µs
Diagnosis P99
故障诊断 P99
1M samples
🛡️
70,000+
Fault Injections
故障注入验证
7 fault types
7 种故障类型
📊
177,582/s
Telemetry Throughput
遥测吞吐量
Single thread
单线程
🧠
84
Self-Healing Rules
自愈规则
MAPE-K knowledge base
MAPE-K 知识库
🔗
9
Supported Providers
支持的 Provider
OpenAI · Anthropic · DeepSeek · Google · more
OpenAI · Anthropic · DeepSeek · Google · 更多

One Platform.
Three Paths In.

一个平台。
三种接入方式。

For AI Teams Hit by Production Failures
已被生产事故坑过的团队

Stop paging SRE at 3 AM

别再半夜叫醒 SRE

You have monitoring. You're still manually fixing every LLM outage. Correctover's self-healing engine resolves failures before you wake up—4-level recovery from auto-retry to provider switching to flywheel learning. MTTR from hours to seconds.

你已经有监控了。但每次 LLM 故障还得人工修。Correctover 的自愈引擎在你醒来前就修好了——4 级恢复,从自动重试到 Provider 切换到飞轮学习。MTTR 从小时级降到秒级。

🎯 Self-healing · MTTR · No more pager 🎯 自愈 · MTTR · 不再半夜接报警
For AI Product Teams Going to Production
准备上线的 AI 产品团队

Your last line of defense before launch

上线前的最后一道防线

PoC works great. But your boss asks "how do we guarantee production reliability?" You need semantic validation for every output, drift detection before users complain, and a clear answer to "what happens when this fails?"

PoC 做得很漂亮。但老板问"上线后怎么保证不出事"——你需要语义验证确保每个输出符合预期,漂移检测在用户投诉前发现问题。

🎯 Production readiness · Semantic validation · Drift alerts 🎯 上线信心 · 语义验证 · 漂移预警
For Platform / Infrastructure Teams
平台 / 基础设施团队

The LLM reliability layer for your platform

你平台上的 LLM 可靠性层

Multiple teams using LLMs across your org. You need a unified reliability layer—single provider interface, global observability, cost control, and compliance. Correctover sits in your infrastructure stack. Every upstream app gets self-healing automatically.

多个团队在用 LLM。你需要的是一层统一可靠性层——统一 Provider 接入、全局可观测性、成本控制、合规。Correctover 嵌入你的基础设施栈,所有上游应用自动获得自愈能力。

🎯 Platform layer · Unified · Multi-tenant · Multi-provider 🎯 平台层 · 统一接入 · 多租户 · 基础设施

Stop Hoping Your LLM Works.
Start Knowing It Does.

别再祈祷你的 LLM 正常工作。
开始确认它真的可靠。

From zero to protected in under 5 minutes. No infrastructure changes, no data leaving your process.

5 分钟内从零到受保护。不需要改变基础设施,数据不离开你的进程。

pip install correctover 📋