Production
Intelligence
for the AI Era

Writing code is no longer the bottleneck. Understanding how systems actually behave in production is. CodeKarma maps production behavior directly to code.

Do you know?

More than half of your code is dead or dormant

01

Retired features

Old functionality still ships in production, never invoked and never removed.

02

Untaken paths

Branches your customers never exercise, still adding risk to every release.

03

Classes nobody calls anymore.

Dead classes stay in the codebase until production behavior proves they can go.

Do you know?

More than half of your code is dead or dormant Retired features Untaken paths Classes nobody calls anymore.

Classes nobody
calls anymore.

IntelliJ-style IDE with CodeKarma dead-code insights
57 @Override
Not invoked in production no usages
58 public List<Order> getAllOrders() { return getAllOrders(null, null); }
59
60 @Override
66.1 rpm | 10.72% err | Call Hierarchy 1 usage
61 public Order getOrderById(Long orderId) {
62 Order order = mockOrders.stream()
63 .filter(o -> o.getId().equals(orderId)).findFirst()
64 .orElse(createMockOrder(orderId));
65 switch (order.getType()) {
66 case "virtual":
67 processVirtualOrder(order);
68 break;
69 case "physical":
70 try { processPhysicalOrder(order); } catch (Exception e) { fallbackOrderHandler(order); }
71 break;
72 default: handleUnknownOrder(order);
73 }
74 OrderUtil.enrichOrder(order);
75 return order;
76 }
77
Not invoked in production 1 usage
78 private void processVirtualOrder(Order order) {
79 System.out.println("Processing virtual order: " + order.getId());
80 generateLicense(order);
81 sendUserEmail(order);
82 createAuditLog(order, "virtual_processed");
83 order.setStatus("COMPLETED");
84 }
85
30.5 rpm | 32.61% err | Call Hierarchy 1 usage
86 private void processPhysicalOrder(Order order) {
87 System.out.println("Processing physical order: " + order.getId());
88 validateInventory(order);
89 estimateShipping(order);
90 dispatchToVendor(order);
91 }
23 public class CollaborativeFilteringEngine implements RecommendationEngine {
24
25 @Override
Not invoked in production 1 usage
26 public List<String> recommend(String userId) {
27 // Find similar users and generate collaborative recommendations
28 List<String> similarUsers = findSimilarUsers(userId);
29 Map<String, Double> itemScores = calculateCollaborativeScores(userId, similarUsers);
30 return rankCollaborativeRecommendations(itemScores);
31 }
32
Not invoked in production 1 usage
33 private List<String> findSimilarUsers(String targetUserId) {
34 // Find users with similar preferences using collaborative filtering
35 simulateDelay(60);
36
37 Map<String, Double> targetUserProfile = getUserProfile(targetUserId);
38 Map<String, Double> userSimilarities = new HashMap<>();
39
40 for (String userId : userItemMatrix.keySet()) {
41 if (!userId.equals(targetUserId)) {
42 double similarity = calculateUserSimilarity(targetUserProfile, userItemMatrix.get(userId));
43 userSimilarities.put(userId, similarity);
44 }
45 }
46
47 // Return top similar users
48 return userSimilarities.entrySet().stream()
49 .sorted(Map.Entry.<String, Double>comparingByValue().reversed())
50 .limit(3)
51 .map(Map.Entry::getKey)
52 .collect(Collectors.toList());
53 }
54
Not invoked in production 3 usages
55 private Map<String, Double> getUserProfile(String userId) {
56 // Get or generate user profile
57 simulateDelay(30);
58
59 if (userItemMatrix.containsKey(userId)) {
60 return userItemMatrix.get(userId);
61 }
62
63 // Generate profile for new user based on userId characteristics
64 Map<String, Double> newProfile = new HashMap<>();
65 String[] allItems = {"laptop", "smartphone", "book1", "book2", "shirt", "shoes", "furniture", "sports_gear"};
66
67 for (String item : allItems) {
68 if (random.nextDouble() > 0.4) { // 60% chance of having a rating
69 newProfile.put(item, 1.0 + random.nextDouble() * 4.0);
70 }
71 }
72
73 return newProfile;
74 }
Your engineers can't confidently tell which is which,

CodeKarma shows what runs in your production

Schedule Demo

The Platform

One truth.
Four products.

From developers and managers to VPs and AI agents — production intelligence for every role.

Why CodeKarma

Measurable impact
from day one

Productivity increase chart

40–60% productivity increase

Production-aware development eliminates guesswork. Insights directly in the IDE.

Early detection incident reduction chart

5–10% fewer incidents

Behavior regressions caught before deployment. Hidden cascades flagged in planning.

Dead code detection

Up to 60% dead code found

Identified from real production traffic, with no estimate-based guesswork.

Alert noise reduction

80% less alert noise

Correlated root cause analysis. One signal, not a hundred alerts.

Real-time production mapping

Real-time production mapping

Every line of code linked to live production behavior. See exactly what runs, how often, and where it matters.

AI-native workflow integration

AI-native workflow integration

Production context flows directly into AI coding assistants. Smarter suggestions grounded in how your system actually behaves.

Integrations

Zero friction.
Any stack. Any cloud.

Languages

Java Java 8+
Go Go
Python Python
.NET .NET

Clouds

AWS AWS AWS
GCP GCP
Azure Azure Azure

Databases

MySQL MySQL
Postgres Postgres Postgres
MongoDB MongoDB
DynamoDB DynamoDB DynamoDB
Redis Redis Redis
Cosmos Cosmos Cosmos
MariaDB MariaDB
HBase HBase
Elastic Elastic

Queues

Kafka Kafka
Pulsar Pulsar
RabbitMQ RabbitMQ
SQS SQS
SNS SNS SNS

Infra & Protocols

Kubernetes Kubernetes
HTTP/S HTTP/S HTTP/S
gRPC gRPC gRPC

IDEs & AI Tools

JetBrains JetBrains
VS Code VS Code
Cursor Cursor
Windsurf Windsurf
Copilot Copilot
Claude Claude
Custom Agents

Security

Security first.
Always.

Learn more

BYOC

All data stays in your cloud. Your perimeter. Your control.

SaaS

Managed by CodeKarma. Enterprise-grade. No infra overhead.

SOC 2 Type 2

Independently audited, with controls maintained year-round.

No data captured

No PII. No payloads. No request bodies. Behavioral telemetry only.

Contact

Let's talk

Schedule a call:

Anantharam Vanchi Prakash

Anantharam Vanchi Prakash

Co-Founder & CEO

Schedule Call View Platform

Contact Us

codekarma.ai

curl https://preview.codekarma.ai/home.md

CodeKarma homepage for AI agents

# Production intelligence for humans and AI agents

> CodeKarma maps production behavior directly to code so teams and AI agents can understand what actually runs, what is dead, and what is risky to change.

links: [Schedule demo](https://cal.com/ananth-vanchi/30min)[View platform](https://demoapp.codekarma.ai/graph?pinned=true&itemId=Tix-Winterfell%3A%3AApp%3A%3AGET%3A%3A%2Fsearch%2FmoviesByName&domain=demo&metrics=throughput%2CerrorRate%2Clatency&restoreServices=ss%3ATix-Winterfell&version=demo--366--Tix-Winterfell%3A%3AApp%3A%3AGET%3A%3A%2Fsearch%2FmoviesByName)[Email](mailto:info@codekarma.ai)

## metadata

path
/
default_mode
human
agent_mode
structured site summary

## What CodeKarma does

  • Connects runtime production behavior to source code and APIs.
  • Identifies dead, dormant, hot, slow, and risky code paths from real traffic.
  • Feeds production context into humans in the IDE and into AI agents through KarmaIQ.

## Core products

  • KarmaLens: IDE plugin for production-aware development.
  • KarmaDomain: API-level dependency and system topology graph.
  • KarmaPulse: operational dashboard for issues, drift, and RCA.
  • KarmaIQ: MCP server that gives AI agents production context.
refs: [Products](/products?mode=agent)[KarmaIQ](/products?mode=agent#karmaiq)

## Why it matters

  • AI can write code faster, but needs production truth to change systems safely.
  • Engineering teams need deterministic signals instead of noisy observability dashboards.
  • Production behavior becomes the shared context for developers, managers, and agents.
Human Agent