Salus

Visibility14

Vibe71

AI Visibility & Sentiment

Salus

Salus is a Y Combinator-backed AI safety company that provides runtime guardrails for AI agents. Their platform validates and blocks incorrect agent actions before they execute, providing immediate feedback for self-correction and protecting businesses from costly AI mistakes.

Active Monitoring

usesalus.ai

Software YC25-26

AI Visibility Score

14/100

Invisible

Sentiment Score

71/100

AI Perception

Summary

Salus is currently ghosting the AI agent security conversation, holding a negligible 5/100 visibility score while competitors like LangChain and Guardrails AI dominate the narrative. Despite a rare high-ranking appearance in Claude for adversarial attacks, the brand is completely missing from production reliability and runtime guardrail queries that drive architect-level decision-making.

Value Proposition

Validate and block incorrect AI agent actions at runtime before they execute, with structured feedback that enables agents to self-correct and retry, protecting businesses from costly mistakes.

Overview

Mission

Protect businesses from costly AI agent mistakes by validating actions before they execute.

Products & Services

Runtime Guardrails for AI agentsSelf-Repair feedback systemReal-time agent monitoring and visibilityAdversarial evaluation testingPython SDK integration

Agent Breakdown

AI Platforms

How often do different AI platforms reference Salus?

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Conversation Analysis

Topics

What conversations is Salus included in — or excluded from?

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Buyer Personas

Personas

Who does each AI platform recommend Salus 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

Ensuring Production Reliability For AI Agents(3 queries)

“how do i stop my langchain agent from making weird database calls it shouldn't be doing?”

0/4 platforms mentioned

ChatGPT

1.sqlparse

2.pglast

3.LangChain

4.SQLAlchemy

Claude

1.LangChain

2.Python

3.Pydantic

Gemini

1.LangChain

2.PostgreSQL

3.Supabase

4.Pydantic

5.sqlparse

+3 more

AI Overviews

1.LangChain

2.GitHub

3.SQLDatabaseToolkit

4.SQLDatabase

5.ModelCallLimitMiddleware

+2 more

“help me build a system where my ai agent can fix its own errors before the user sees them”

0/3 platforms mentioned

Claude

1.LangGraph

2.Python

3.TypeScript

4.gpt-4-turbo

5.gpt-3.5

+2 more

Gemini

1.LangGraph

2.Pydantic

3.Claude 3.5 Sonnet

4.GPT-4o

5.Lakera

+8 more

AI Overviews

1.GPT-5

2.Claude 3.5

3.LangSmith

4.Langfuse

5.LangGraph

+3 more

“what tools can i use for real-time monitoring and visibility of autonomous agents in production?”

0/3 platforms mentioned

Claude

1.LangSmith

2.LangChain

3.LangGraph

4.Langfuse

5.Jaeger

+4 more

Gemini

1.LangGraph

2.LangChain

3.OpenTelemetry

4.LangSmith

5.Helicone

+10 more

AI Overviews

1.Galileo AI

2.Maxim AI

3.Braintrust

4.LangSmith

5.LangChain

+11 more

Securing Autonomous Systems Against Adversarial Attacks(1 query)

“best way to protect my ai agents from prompt injection and adversarial attacks, what products should i look at?”

0/4 platforms mentioned

ChatGPT

1.Microsoft Azure OpenAI

2.Azure AI Content Safety

3.Google Cloud Content Safety

4.LangChain

5.LlamaIndex

+16 more

Claude

1.Rebuff.ai

2.Lakera Guard

3.Zeno

4.LLMGuard

5.NeMo Guardrails

+6 more

Gemini

1.Lakera Guard

2.Gandalf

3.Prompt Security

4.NeMo Guardrails

5.NVIDIA

+11 more

AI Overviews

1.Lakera Guard

2.Wiz AI-SPM

3.Lasso Security

4.Aim Security

5.CrowdStrike Falcon

+4 more

Evaluating AI Safety And Guardrail Frameworks(1 query)

“compare the best runtime guardrail tools for ai agents right now”

0/3 platforms mentioned

Claude

1.Humanloop

2.Responsible Scaling Framework

3.Claude API

4.Promptfoo

5.Langfuse

+4 more

Gemini

1.Guardrails AI

2.Guardrails Hub

3.Pydantic

4.NVIDIA NeMo Guardrails

5.NVIDIA

+6 more

AI Overviews

1.Lakera

2.Cloudflare AI Gateway

3.NeMo Guardrails

4.Guardrails AI

5.Galileo

+5 more

Analysis

Key Insights

What AI visibility analysis reveals about this brand

Strength

Secured a high-ranking position (#2) on Claude specifically for queries regarding protection against adversarial attacks and prompt injection.

Strength

Shows a relative strength with the Enterprise CISO persona, achieving a 30% mention rate compared to zero visibility with other technical personas.

Strength

Maintains positive sentiment across all LLM mentions, indicating that when the brand is known, it is viewed as a favorable security solution.

Gap

Complete invisibility in Google AI Overviews (0% mention rate), missing out on the primary discovery layer for modern search.

Gap

Fails to appear in critical 'production reliability' queries, allowing LangChain and Pydantic to own the troubleshooting narrative.

Gap

Total lack of presence for the Lead AI Architect and High-Growth Startup CTO personas, who are the primary evaluators of runtime guardrail tools.

Opportunity

Capture market share in the 'runtime guardrails' category by creating structured technical content that mimics the successful data patterns of Guardrails AI.

Opportunity

Leverage the existing 30% CISO visibility to build a 'Security-First' authority narrative that targets the enterprise transition from pilot to production.

Opportunity

Improve ChatGPT positioning from #31 to the top 10 by optimizing API documentation for LLM indexing and retrieval.

Technical Health

Site Health for AI Visibility

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

87/100

15 passed 5 warnings

Audited 2/27/2026

Crawlability83

Can AI bots find your pages?

Technical96

SSL, mobile, doctype basics

On-Page SEO93

Titles, descriptions, headings

Content Quality60

Word count, depth, freshness

Schema Markup85

Structured data for AI comprehension

Social & OG100

Open Graph, Twitter cards

AI Readability60

How well AI can parse your content

Warnings

No robots.txt file found

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

3 render-blocking resource(s) detected

Consider deferring or async-loading non-critical scripts and stylesheets.

Meta description is too short (47 characters)

Expand the description to 150-160 characters with a clear value proposition.

Content may be too short

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

Few internal links on this page

Add more internal links to related pages on your site.

Want a full technical audit with AI-specific recommendations?

Run a free visibility scan

Brand Identity

Brand Voice & Style

How AI perceives Salus's communication style and personality

Salus communicates with a technically sophisticated yet accessible voice that speaks directly to developers and engineering teams. The tone is confident and security-focused, emphasizing protection and reliability without being alarmist. They use clear, concise language with technical precision, demonstrating deep understanding of AI agent architecture while keeping messaging approachable. The brand conveys urgency around AI safety while maintaining a calm, professional demeanor that instills trust.

Core Tone Traits

Technically Precise

Uses accurate developer terminology and code examples to demonstrate credibility

Security-Focused

Emphasizes protection, validation, and preventing costly mistakes

Developer-Friendly

Speaks directly to engineers with practical, implementation-focused messaging

Confident & Trustworthy

Projects authority in AI safety without being alarmist or fear-mongering

Competitive Landscape

Related Ecosystem

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

LangChain30 mentions

LangGraph18 mentions

Pydantic15 mentions

LangSmith15 mentions

Guardrails AI13 mentions

CrewAI11 mentions

Lakera Guard8 mentions

sqlparse7 mentions

Arize Phoenix7 mentions

Datadog7 mentions

Salus3 mentions

Source Intelligence

Citations

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

Limit Data and Enforcing Security for SQLDatabase & Agents

https://github.com/langchain-ai/langchain/issues/11243

Referenced in 1 query

Pitch Story

Build a custom SQL agent - Docs by LangChain

https://docs.langchain.com/oss/python/langgraph/sql-agent

Referenced in 1 query

Review

My SQL langchain agent seems to be limiting its answers to ...

https://stackoverflow.com/questions/77204383/my-sql-langchain-agent-seems-to-be-limiting-its-answers-to-10-rows

Referenced in 1 query

Join Discussion

langchain-ai/sql-agent-system-prompt

https://smith.langchain.com/hub/langchain-ai/sql-agent-system-prompt/31156d5f

Referenced in 1 query

Review

'Talk' to Your SQL Database Using LangChain and Azure ...

https://towardsdatascience.com/talk-to-your-sql-database-using-langchain-and-azure-openai-bb79ad22c5e2/

Referenced in 1 query

Review

Using LangChain and Azure OpenAI to Talk’ to Your SQL Database

https://medium.com/data-science/talk-to-your-sql-database-using-langchain-and-azure-openai-bb79ad22c5e2

Referenced in 1 query

Review

Stop Endless Back-and-Forth — Add Model Call Limits in ...

https://www.youtube.com/watch?v=x5jLQTFXR0Y

Referenced in 1 query

Pitch Story

Building a Robust SQL Agent: The Art of Prompt Engineering

https://www.linkedin.com/pulse/building-robust-sql-agent-art-prompt-engineering-campos-vilas-boas-wcmae

Referenced in 1 query

Pitch Story

Stop LLM SQL Mistakes: 5 LangChain Tool Policies - Medium

https://medium.com/@Quaxel/stop-llm-sql-mistakes-5-langchain-tool-policies-fb27be5df383

Referenced in 1 query

Review

Security policy - Docs by LangChain

https://docs.langchain.com/oss/python/security-policy

Referenced in 1 query

Review

LangChain Middleware: Taking Control of Your Agent's ...

https://python.plainenglish.io/langchain-middleware-taking-control-of-your-agents-behavior-a667f6b04c54

Referenced in 1 query

Review

Security - langchain-ai/deep-agents-ui - GitHub

https://github.com/langchain-ai/deep-agents-ui/security

Referenced in 1 query

Pitch Story

Content Engineering

Goals & Content Ideas

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

Dominate AI Agent Troubleshooting Search Results

Address the critical visibility gap in high-volume troubleshooting queries where Salus is currently invisible. By publishing comprehensive technical how-to guides for fixing AI agent database errors and production reliability issues, we'll create the authoritative content that LLMs reference when developers search for solutions—directly challenging LangChain's current dominance in this space.

The Complete Guide to Debugging AI Agent Database Connection Failures in Production

5 Runtime Errors That Crash AI Agents and How to Prevent Them

Why Your AI Agent Keeps Failing Silently and What to Do About It

Production Reliability Checklist: 10 Pre-Deployment Tests for AI Agents

Optimize Technical SEO for AI Overviews

With 0% visibility in AI Overviews, implement structured data and FAQ schemas specifically targeting 'AI Runtime Guardrails' and 'Adversarial Defense' queries. This technical SEO foundation will provide the signals LLMs need to include Salus in generated summary responses, dramatically improving discoverability when users ask AI assistants about AI safety solutions.

What Are AI Runtime Guardrails? A Technical Deep Dive for Engineering Teams

Adversarial Defense 101: Protecting Your AI Agents from Malicious Inputs

FAQ: Everything Developers Ask About Validating AI Agent Actions

The Definitive Glossary of AI Agent Safety Terminology

Build the Definitive CISO Resource Hub

Capitalize on Salus's existing foothold with Enterprise CISOs by developing a dedicated resource center that deepens engagement with this critical persona. This hub will solidify our primary enterprise entry point while generating the authoritative, persona-specific content that LLMs surface when security leaders research AI governance and risk management solutions.

AI Agent Risk Assessment Framework for Enterprise Security Teams

Board-Ready: How to Present AI Agent Safety to Non-Technical Stakeholders

The CISO's Guide to Evaluating AI Guardrail Solutions

Compliance Mapping: AI Agent Controls for SOC 2 and ISO 27001

Real Cost Analysis: What AI Agent Failures Actually Cost Enterprises

Create Competitive Comparison Content for AI Discovery

LLMs frequently categorize vendors through comparison queries, so creating transparent documentation comparing Salus against Lakera Guard and Guardrails AI will improve brand association and discovery. This positions Salus in the competitive conversation that AI assistants reference when users ask for AI safety solution recommendations.

Salus vs Lakera Guard: Which Runtime Guardrail Fits Your Architecture?

Comparing AI Agent Safety Approaches: Validation vs Detection vs Prevention

Feature Breakdown: How Top AI Guardrail Platforms Handle Edge Cases

Migration Guide: Switching from Guardrails AI to Salus in 30 Minutes

When to Choose Salus Over Open-Source AI Safety Tools

Content Engineering

Recommended Actions

Publish a series of technical 'How-To' guides specifically for fixing AI agent database errors and production reliability.

This directly addresses the high-volume queries where Salus is currently invisible, allowing it to challenge LangChain's dominance in troubleshooting.

Impact: High

Implement structured data and FAQ schemas focused on 'AI Runtime Guardrails' and 'Adversarial Defense'.

With 0% visibility in AI Overviews, the brand needs technical SEO signals that LLMs use to generate summary responses.

Impact: High

Develop a dedicated 'Salus for CISOs' resource hub.

The data shows the Enterprise CISO is the only persona where Salus has a foothold; deepening this content will solidify its primary entry point into the enterprise.

Impact: Medium

Create comparison documentation against Lakera Guard and Guardrails AI.

LLMs frequently use comparison queries to categorize vendors; being cited alongside established competitors will improve brand association and discovery.

Impact: Medium

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