Soon This program is currently in development.

AI That Debugs Like an Engineer

Most AI can read code. Few can diagnose why it broke at 3 AM. We train language models to troubleshoot real software failures—reading logs, tracing dependencies, and finding root causes the way a senior engineer would.

incident-resolver
ALERTpayment-service latency > 5000ms
─────────────────────────────────
analyzing logs from payment-service, db-pool, gateway
traced connection pool exhaustion in db-pool
cause unclosed connections from retry loop in checkout.go:142
─────────────────────────────────
fix applied added defer conn.Close() + pool limit
verified latency nominal, pool utilization at 34%
Resolved in 94 seconds — P2 downgraded to resolved

What Your AI Learns

Six core competencies that turn a code-reading LLM into a production-grade troubleshooter.

Root Cause Analysis

Agents learn to trace errors back to their origin—not just treating symptoms, but identifying the actual failure point in complex systems.

Dependency Mapping

Trained to understand how services connect, which downstream systems are affected, and where a single change can cascade into failures.

Log Interpretation

Reads through thousands of log lines and stack traces to surface the signal buried in noise—no more scrolling through walls of text.

Runbook Execution

Follows existing runbooks step-by-step, adapts when conditions differ, and knows when a runbook is outdated or insufficient.

Environment Awareness

Understands the difference between staging and production, respects permissions boundaries, and never runs destructive commands without confirmation.

Severity Classification

Accurately triages incidents by impact and urgency. Distinguishes a degraded endpoint from a full outage—and responds proportionally.

How It Works

A four-phase program that takes your AI from "reads stack traces" to "resolves incidents."

01

Codebase Ingestion

We map your architecture, dependencies, deployment pipelines, and historical incident data to build a contextual foundation for training.

02

Failure Simulation

We construct realistic failure scenarios—misconfigured deploys, memory leaks, race conditions, third-party outages—drawn from your actual incident history.

03

Diagnostic Training

Your AI troubleshoots each scenario end-to-end: reading logs, forming hypotheses, testing them, and arriving at verified root causes.

04

Supervised Rollout

The agent shadows your on-call team first, then handles L1 incidents autonomously, with human review on every resolution until trust is established.

Target Outcomes

What We're Building Toward

< 2m

Time to Triage

From alert to categorized incident with initial hypothesis

78%

Auto-Resolved

L1 incidents handled without human intervention

-45%

MTTR Reduction

Faster mean-time-to-resolution across all severities

0

Rogue Commands

Zero unauthorized actions in production environments

Before & After Training

The gap between a generic code assistant and an EleveryAI-trained troubleshooter.

Untrained LLM
  • × Suggests "restart the service" as a first response to every issue
  • × Can't distinguish a network timeout from a deadlock
  • × Halluccinates config flags and CLI options that don't exist
  • × Runs dangerous commands without checking the environment
  • × Treats every incident as isolated—no pattern recognition
EleveryAI Trained
  • Forms hypotheses from logs and tests them systematically
  • Maps failure to specific code paths and recent deploys
  • Only suggests commands it's verified against your environment
  • Respects production safeguards—never acts without confirmation
  • Correlates incidents to detect recurring failure patterns
Coming Soon

Be First in Line

We're building the training program that turns AI into reliable on-call engineers. Join the waitlist to get early access.

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