Katanemo Labs

Visibility4

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Businesses/Artificial Intelligence/Katanemo Labs

AI Visibility & Sentiment

Katanemo Labs

Katanemo Labs provides forward-deployed AI infrastructure engineers who help enterprises accelerate the development and deployment of AI agents. They combine industry-leading research with open-source technologies to deliver production-ready AI agent solutions for major companies.

Active Monitoring

katanemo.com

Artificial Intelligence Startups

AI Visibility Score

4/100

Invisible

Sentiment Score

86/100

AI Perception

Summary

Katanemo Labs is currently a ghost in the production-grade AI infrastructure conversation, ceding nearly the entire market narrative to established ecosystem players like LangChain and LlamaIndex. While technical founders show a glimmer of awareness, the brand is completely missing from the critical enterprise architect decision-making path where reliability and deployment strategies are defined.

Value Proposition

Accelerate AI agent development with forward-deployed infrastructure engineers who bring industry-leading research and open-source technologies directly to your team.

Overview

Mission

Bringing industry-leading research and open-source technologies to accelerate the development of AI agents.

Products & Services

Forward-deployed AI infrastructure engineering servicesPlano - Open Source Agent InfrastructureAI agent development consultingModels research and implementationProduction AI deployment support

Agent Breakdown

AI Platforms

How often do different AI platforms reference Katanemo Labs?

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Conversation Analysis

Topics

What conversations is Katanemo Labs included in — or excluded from?

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Buyer Personas

Personas

Who does each AI platform recommend Katanemo Labs to, and when?

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Programmatic Testing

Sample Conversations

We programmatically analyze questions that real customers are asking to AI agents and chatbots, extract brand mentions and sentiment, analyze every response, and synthesize the data into an action plan to increase AI visibility.

ChatGPT

Claude

Gemini

AI Overviews

Scaling AI Agents To Production(2 queries)

“our ai agent prototype is too buggy for customers, what steps do we need to take to make it production-ready”

0/4 platforms mentioned

ChatGPT

1.Sentry

2.Datadog

3.AWS CloudWatch

4.Kong

5.Envoy

+74 more

Claude

1.AWS

2.GCP

3.Azure

4.Modal

5.RunwayML

+2 more

Gemini

1.DVC

2.LIME

3.SHAP

4.Jenkins

5.GitLab CI

+8 more

AI Overviews

1.Galileo AI

2.Maxim AI

3.Google Cloud

4.Arize Phoenix

5.Beam AI

+2 more

“help me figure out a production deployment strategy for a fleet of ai agents in a large enterprise”

0/3 platforms mentioned

Claude

1.Kubernetes

2.EKS

3.AKS

4.Istio

5.Redis Cluster

+13 more

Gemini

1.Docker

2.Kubernetes

3.kubeadm

4.Azure Kubernetes Service

5.AKS

+48 more

AI Overviews

1.Inbenta

2.Clarifai

3.Grid Dynamics

4.Datagrid

Agent Infrastructure & Tooling Selection(1 query)

“best open source agent infrastructure for building complex workflows, give me specific project names”

0/4 platforms mentioned

ChatGPT

1.LangChain

2.Microsoft Autogen

3.LlamaIndex

4.Haystack

5.Temporal

+15 more

Claude

1.Apache Airflow

2.Prefect

3.Temporal

4.LangChain

5.AutoGen

+6 more

Gemini

1.LangChain

2.Auto-GPT

3.GPT-4

4.BabyAGI

5.CrewAI

+4 more

AI Overviews

1.LangGraph

2.LangChain

3.CrewAI

4.Python

5.AutoGen

+7 more

Expert Engineering & Implementation Support(1 query)

“need to find expert engineers who specialize in forward-deployed ai infrastructure to help us scale”

0/3 platforms mentioned

Claude

1.MLOps.community

2.Weights & Biases

3.Papers with Code

4.Hugging Face

5.Triton Inference Server

+20 more

Gemini

1.Blackwood Group

2.Talener

3.CyberCoders

4.Hacker News

5.Stack Overflow Jobs

+20 more

AI Overviews

1.Scale AI

2.Human Agency

3.Lumay

4.Baseten

5.10Clouds

+8 more

Enterprise AI Reliability & Vendor Trust(1 query)

“who are the most trusted partners for implementing production ai agents right now”

0/4 platforms mentioned

ChatGPT

1.Microsoft Azure

2.Azure OpenAI

3.Amazon Web Services

4.AWS

5.Bedrock

+58 more

Claude

1.GPT-4

2.Google Cloud AI

3.Vertex AI

4.Anduril

5.Replit Agent

+6 more

Gemini

1.Amazon Web Services (AWS)

2.Amazon SageMaker

3.Amazon Lex

4.Amazon Kendra

5.Accenture

+19 more

AI Overviews

1.Accenture

2.IBM Consulting

3.watsonx

4.Cognizant

5.Infosys

+19 more

Analysis

Key Insights

What AI visibility analysis reveals about this brand

Strength

Maintains a 100% success rate on direct brand inquiries across all platforms, indicating a well-defined identity that LLMs can recall when prompted specifically.

Strength

Achieved a 33% mention rate with the Technical Co-Founder persona, suggesting early traction and recognition within the startup ecosystem.

Strength

Shows higher organic receptivity on Claude (13%) compared to other models, likely due to the platform's preference for detailed technical documentation.

Gap

Total absence from ChatGPT and Google AI Overviews, which serve as the primary discovery engines for high-intent enterprise buyers.

Gap

Complete lack of visibility for the AI Product Innovation Manager and Enterprise Architect personas, stalling the sales cycle at the top of the funnel.

Gap

Failed to capture any share of voice in 'Scaling AI Agents' queries, even when users explicitly expressed frustration with 'buggy' prototypes.

Opportunity

Capitalize on the 'buggy prototype' pain point by positioning Katanemo as the enterprise-grade alternative to the often-criticized reliability of early-stage tools like LangChain.

Opportunity

Bridge the 'Expert Engineering' gap by publishing specific implementation guides that target the 'forward-deployed engineer' search intent where no competitors are currently dominant.

Opportunity

Leverage the existing 13% visibility on Claude by doubling down on technical documentation that highlights Katanemo's architectural superiority over LlamaIndex.

Technical Health

Site Health for AI Visibility

How well Katanemo Labs's website is optimized for AI agent discovery and comprehension.

73/100

6 passed 3 warnings 5 issues

Audited 2/27/2026

Crawlability79

Can AI bots find your pages?

Technical80

SSL, mobile, doctype basics

On-Page SEO58

Titles, descriptions, headings

Content Quality55

Word count, depth, freshness

Schema Markup85

Structured data for AI comprehension

Social & OG77

Open Graph, Twitter cards

AI Readability85

How well AI can parse your content

Critical Issues

Page returns a 404 error

Fix the underlying issue causing the error. 404s should be redirected or the link removed.

Page has no title tag

Add a <title> tag describing the page content (50-60 characters).

Page has no meta description

Add a <meta name="description"> tag summarizing the page (150-160 characters).

Page has no H1 heading

Add a single H1 tag as the main page heading.

Content is too thin

Expand your content to at least 300-500 words with valuable information.

Warnings

No robots.txt file found

Create a robots.txt file at your domain root. Optional but recommended.

Few headings on page

Add more H2 and H3 headings to organize content into sections.

Few internal links on this page

Add more internal links to related pages on your site.

Missing Open Graph tags for social sharing

Add og:title, og:description, and og:image meta tags.

LCP data not available

Ensure the page loads with browser rendering enabled.

+ 1 more warnings

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Brand Identity

Brand Voice & Style

How AI perceives Katanemo Labs's communication style and personality

Katanemo Labs communicates with a technically sophisticated yet accessible voice that balances deep AI expertise with practical, results-oriented messaging. The brand projects confidence and authority in the AI infrastructure space while maintaining approachability through clear, jargon-free explanations. Their tone emphasizes reliability, speed, and the tangible business value of their forward-deployed engineering model, appealing to enterprise decision-makers who need both technical depth and business outcomes.

Core Tone Traits

Technically Authoritative

Demonstrates deep expertise in AI infrastructure and agent development without being condescending

Results-Oriented

Focuses on practical outcomes like speed, reliability, and production readiness

Confidently Understated

Projects expertise through substance rather than hype, letting client logos and capabilities speak

Enterprise-Ready

Professional and trustworthy tone that resonates with Fortune 500 decision-makers

Competitive Landscape

Related Ecosystem

Related products and services that AI mentions in conversations alongside or instead of Katanemo Labs

LangChain29 mentions

LlamaIndex16 mentions

Kubernetes15 mentions

LangSmith14 mentions

Haystack12 mentions

Prometheus11 mentions

Docker11 mentions

Weaviate11 mentions

CrewAI11 mentions

LangGraph11 mentions

Katanemo Labs3 mentions

Source Intelligence

Citations

Sources that AI assistants cite. Getting featured here improves visibility.

How to Build AI Agents That Actually Think : r/AI_Agents - Reddit

https://www.reddit.com/r/AI_Agents/comments/1r17hbx/from_prototype_to_production_how_to_build_ai/

Referenced in 1 query

Join Discussion

Why AI Agents Fail in Production: Six Architecture ... - Softcery

https://softcery.com/lab/why-ai-agent-prototypes-fail-in-production-and-how-to-fix-it

Referenced in 1 query

Review

10 Essential Steps for Evaluating the Reliability of AI Agents

https://www.getmaxim.ai/articles/10-essential-steps-for-evaluating-the-reliability-of-ai-agents/

Referenced in 1 query

Review

6 Principles for Building Production-Ready AI Agents - Beam AI

https://beam.ai/agentic-insights/production-ready-ai-agents-the-design-principles-that-actually-work

Referenced in 1 query

Review

A dev's guide to production-ready AI agents | Google Cloud Blog

https://cloud.google.com/blog/products/ai-machine-learning/a-devs-guide-to-production-ready-ai-agents

Referenced in 1 query

Review

AI Agent Reliability: The Playbook for Production-Ready ...

https://www.getmaxim.ai/articles/ai-agent-reliability-the-long-term-playbook-for-production-ready-systems/

Referenced in 1 query

Review

How to Debug AI Agents: 10 Failure Modes + Fixes | Galileo

https://galileo.ai/blog/debug-ai-agents

Referenced in 1 query

Review

How to Build Reliable AI Agents (without the hype)

https://www.youtube.com/watch?v=T1Lowy1mnEg&t=1514

Referenced in 1 query

Pitch Story

8 AI Agent Metrics That Go Beyond Accuracy | Galileo

https://galileo.ai/blog/ai-agent-reliability-metrics

Referenced in 1 query

Review

How to build reliable AI Agents?

https://www.youtube.com/watch?v=fBshJ_ps6WI

Referenced in 1 query

Pitch Story

Looking for production-ready AI agents? Here’s where to start.

https://cloud.google.com/blog/topics/startups/startup-guide-ai-agents-production-ready-ai-how-to

Referenced in 1 query

Review

Evaluating AI agents: Real-world lessons from building agentic systems at Amazon | Artificial Intelligence

https://aws.amazon.com/blogs/machine-learning/evaluating-ai-agents-real-world-lessons-from-building-agentic-systems-at-amazon/

Referenced in 1 query

Partner

Content Engineering

Goals & Content Ideas

Ideas to help AI agents better understand the business and be more likely to use Katanemo Labs's resources to help users.

Dominate Production-Ready Agent Infrastructure Narrative

Address the critical gap where LangChain and LlamaIndex currently own the infrastructure conversation despite users actively seeking alternatives to buggy prototypes. Launch a technical content blitz positioning Katanemo as the production-ready solution, optimized for AI assistant indexing. Amplify through social media with technical deep-dives and direct comparisons that AI models will reference when users ask about reliable agent infrastructure.

Why Your LangChain Prototype Keeps Breaking in Production (And How to Fix It)

The Hidden Costs of Agent Framework Lock-In: A Technical Analysis

Production-Ready vs. Prototype-Ready: What Enterprise Teams Actually Need from Agent Infrastructure

5 Signs Your AI Agent Infrastructure Isn't Ready for Production Scale

How We Deployed 10,000 Concurrent Agents Without the Typical Infrastructure Nightmares

Achieve ChatGPT Visibility Through Documentation Optimization

Tackle the critical zero percent mention rate on ChatGPT by restructuring technical documentation and whitepapers specifically for OpenAI's GPT model indexing. Create authoritative, well-structured content that answers common AI infrastructure queries directly. Social media will drive traffic and backlinks to these optimized resources, increasing their authority signals for AI crawlers.

The Complete Guide to Enterprise AI Agent Deployment: Architecture to Production

AI Agent Infrastructure Glossary: 50 Terms Every Platform Architect Should Know

Whitepaper: Benchmarking Agent Reliability Across Production Environments

Technical Deep-Dive: How Forward-Deployed Engineers Accelerate Agent Development

The Enterprise Checklist for Evaluating AI Agent Infrastructure Vendors

Capture Enterprise Architect Mindshare with Reliability Guides

Bridge the gap in corporate procurement by creating authoritative Enterprise Reliability guides targeting AI Platform Architects who control production-scale deployment decisions. Position Katanemo in formal vendor evaluation processes through technical credibility content. Distribute through LinkedIn and technical communities where enterprise architects research infrastructure solutions.

What AI Platform Architects Get Wrong About Agent Reliability at Scale

Enterprise Reliability Scorecard: How to Evaluate AI Agent Infrastructure

The Platform Architect's Guide to Production-Grade Agent Deployment

Security, Compliance, and Reliability: The Enterprise AI Infrastructure Trifecta

How Fortune 500 Companies Are Rethinking AI Agent Infrastructure Requirements

Own Expert Implementation Support Through Success Stories

Claim the wide-open 'Expert Engineering & Implementation Support' query space by publishing detailed case studies showcasing forward-deployed engineering successes. Position Katanemo as a hands-on partner rather than just another infrastructure vendor. Leverage social proof through client success narratives that AI assistants will cite when users seek implementation support recommendations.

Case Study: How Forward-Deployed Engineers Cut Agent Development Time by 60%

Behind the Deployment: Lessons from Scaling AI Agents for a Fortune 100 Client

What 'Forward-Deployed' Actually Means: Inside Our Engineering Partnership Model

The ROI of Embedded AI Infrastructure Engineers: Real Numbers from Real Deployments

From Prototype to Production in 30 Days: An Implementation Success Story

Content Engineering

Recommended Actions

Execute a technical content blitz focused on 'production-ready agent infrastructure' to disrupt the LangChain/LlamaIndex narrative dominance.

Competitors currently own the infrastructure conversation, but data shows users are searching for alternatives to buggy prototypes—a gap Katanemo is perfectly positioned to fill.

Impact: High

Optimize core technical documentation and whitepapers for indexing by OpenAI's GPT models to move from 0% to a double-digit mention rate.

Zero visibility on ChatGPT, the world's most-used AI platform, represents a critical barrier to mainstream market awareness and enterprise adoption.

Impact: High

Develop specific 'Enterprise Reliability' guides targeting the AI Platform Architect persona to bridge the gap in corporate procurement.

Katanemo is currently invisible to the very architects responsible for signing off on production-scale AI deployments, preventing the brand from entering formal vendor evaluations.

Impact: Medium

Establish a presence in the 'Expert Engineering & Implementation Support' query space through case studies detailing forward-deployed engineering successes.

This query category is currently wide open; claiming it would position Katanemo as a hands-on partner rather than just another infrastructure provider.

Impact: Medium

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