entelligence — ai code review that learns from production

Ellie helps your team close the loop on repeat incidents.

Every other reviewer flags what looks suspicious. Ellie flags what survives being disproved, then connects it back to your incidents, your team, and your actual AI spend.

Start for free Book a demo

payments-service · pr #4821

47.2% F1

#1 on the 2026 review benchmark

43%

of incidents resolved autonomously

191 PRs

merged with zero defects (Digibee)

SOC 2

Type II · zero code retention

how it works · the loop

Every failure feeds the system. Every review gets smarter than the last.

Ellie cites specific precedent from your own history, not generic warnings pulled from a lint rule.

Production incident

Sentry · PagerDuty · Datadog

Post-mortem captured

incident knowledge base

Semantic retrieval

precedent, not heuristics

Contextual review

cites the incident by name

why this exists

More code is shipping. Less of it is verified.

2.74×: more security vulns in AI-assisted code
1.7×: more logic errors reaching review

You wrote the post-mortem. The bug shipped again.

28%: of AI review comments are noise
91%: longer review times under comment load

You spent $1,200 on AI tools. What did it return?

80%: of teams can't measure AI ROI
20%: use any metric to track impact

for engineers · code review

Code review that catches what humans miss.

Most AI review tools flag whatever looks suspicious. Ellie generates findings, then tries to disprove each one against your code and your incident history.

Adversarial verification
Every finding is attacked before you see it. What survives is real; what doesn't is deleted, not posted.
Context engine
Mid-reasoning, Ellie queries your codebase the way a staff engineer would, instead of guessing from the diff.
Incident learning
Every production failure becomes a pattern the next review hunts for. The corpus is yours, not a generic ruleset.

also ships: IDE extension (VS Code · Cursor · Windsurf) · deep-wiki auto-docs · cross-repo impact

Try it on your PRs Explore code review

payments/process.ts · pr #4821

52async function processPayment(id) {
53+  const response = await fetch(url, opts);
54+  return response.json();
55}
▲ ellie · verified finding
Response body is read without checking response.ok. This pattern took payments down before: see INC-512.

verified findings
hypotheses killed first
min setup

incident memory

That finding above? It matched 3 past incidents.

Ellie doesn't just read your diff. She reads every post-mortem your team has ever written and checks the new code against all of it.

Three incidents, same root cause, never connected until now. The finding ships with the full history attached, so the reviewer knows exactly what is at stake.

See how incident intelligence works

INC-441

Connection pool exhaustion under auth load spike

payments-service · 4h to diagnose · 2 engineers

INC-512

Checkout timeout cascade during Black Friday

payments-service · 6h downtime · $42k lost

INC-601

Retry storm from unclosed connections in prod

api-gateway · 2h degraded · P1 escalation

→ same root cause · surfaced together for the first time

incident intelligence

You wrote the post-mortem. Ellie read it.

Most review tools read the diff. Ellie reads the diff and every post-mortem your team has written, turning past failures into prevention.

Post-mortems train the loop

Race conditions, auth bypasses, retry storms: each one becomes a pattern Ellie hunts on every future diff.

Root cause traced to the PR

Production alerts link back to the exact code change and its review log, automatically.

Committable fixes

Recurring failures surface with a fix you can apply in one click, not a paragraph of advice.

the platform

One system that keeps your code and your team improving.

AI Code Review

Adversarial verification kills false positives before they reach you. Fix every finding from the PR.

Incident Intelligence

Post-mortems sharpen future reviews automatically. The same class of bug does not ship twice.

Ellie in the CLI

Review before you push. Your reviewer lives in the terminal too.

Ellie everywhere

Ask anything about your team or code in GitHub, VS Code, and Slack.

Living documentation

Architecture docs regenerate on merge. Ellie reads them during review.

Security dashboard

GitHub, GitLab, Jira and your stack feed one security picture.

for engineering leaders · ai spend, measured

Full visibility into what AI is actually returning.

Every AI-assisted PR is a bet. Entelligence shows you which bets are safe, which come back as incidents, and exactly what your tools returned this month.

Your AI tools cost $1,340 this month. They returned $42,100.

per-tool breakdown · this month
tool	cost/mo	ai lines	accept	note
Claude	$820	162,110	94%	best ROI on the team
Cursor	$406	141,900	71%	onboarding gap flagged
Copilot	$114	48,300	63%	low acceptance, review

Claude costs twice as much and earns it: 32% higher acceptance. Cursor's gap is an onboarding problem, not a tooling one. Five engineers not using AI at all represent $1.2k per sprint unrealized.

34×

return on AI spend

up from 31× last month

95.1%

AI acceptance rate

suggestions kept, not reverted

4.1h

saved per dev per week

from acceptance × time

$6,820

sprint savings

net of tool cost

Explore AI analytics →

2026 ai code review benchmark · independent, open method

Tested on real bugs, in real repos.

8 tools, 67 seeded bugs across Cal.com, Sentry, Discourse, Keycloak, and Grafana. F1 penalizes both missed bugs and false alarms, so noise loses.

Read the full benchmark report

Entelligence

47.2%

Copilot PR

22.6%

CodeRabbit

21.4%

Greptile

19.8%

Cursor BugBot

17.5%

Graphite

14.9%

F1 score, higher is better · 2 of 8 tools shown scored below 14%

why teams switch · no marketing speak

For engineers

Better signal, less noise.

vs CodeRabbitsingle-digit false-positive rate vs 28% noise

vs Copilot PR47.2% vs 22.6% F1 on the open benchmark

vs Graphiteworks with any editor, not Cursor-dependent

For eng leaders

Answers, not more dashboards.

vs Jellyfishplain-English answers vs charts you interpret

vs LinearBquality + incidents + AI ROI, not cycle time alone

vs all of themsprint assessments written for you, automatically

See the full comparisons — Cursor BugBot, CodeRabbit, and Greptile, feature by feature.

security & trust

Built for teams that can't afford to get this wrong.

Your code is never stored

Processed in memory only. When the review completes, it's gone. SOC 2 Type II.

Never trains our models

Not now, not ever. This isn't a setting; it's the only mode we operate in.

A real team, not a queue

Direct Slack access. Enterprise gets a dedicated CSM and priority SLAs.

Value in the first sprint

Connect in 10 minutes. No professional services, no onboarding marathon.

Not surveillance. Designed to help, not to report on people.

Engineers see the exact same dashboard their manager sees. Transparency goes both ways.
No keylogging, no screen time. Every signal derives from git outputs alone.
Managers get plain-language signals on who to check in with, not scores to punish by.

production results · real teams, real numbers

"Tightened our review loop overnight. It surfaces mistakes we'd normally catch only after deploy: race conditions, unclosed resources, validation misses."

Soumyarkya Mondal · CTO, Sybill · 187 PRs, zero critical issues

"191 PRs merged in 2 months, zero production defects. It catches subtle AI-generated bugs that look correct in isolation but break under load."Engineering team · Digibee

Allocore72% faster review →Digibee191 PRs, zero defects →Sybill187 PRs, zero critical issues →Composio3× dev velocity →Vectorial AI10× faster dev cycle →AgentOpsbetter DX, eng strategy →

The same class of bug won't ship twice.

Ellie catches what AI generates wrong, learns from every incident, and shows your leaders what AI spend is actually returning.

$npm install -g @entelligence/cli