For AI Engineering Teams

Stop Hoping For LLM Reliability, Start Building It


Lam∞m transforms how AI teams build reliable products by replacing good intentions with proven mechanisms.

Build LLM applications that deliver consistent, reliable results — every time.

87% Reduced LLM errors
15+ Hours saved weekly
5 min Setup time
CI/CD Prompt Pipeline Build Your First CI/CD Pipeline Watch Demo Play Icon Book a Demo Chat with the Team
Lamoom Mechanisms

Trusted by innovative AI teams

The Lamoom Philosophy

From good intentions to reliable mechanisms

"Good intentions don't work, mechanisms do."

When building AI applications, relying on an LLM's "good intentions" to produce correct outputs is a recipe for disappointment. That's why Lamoom comes from Lambda on MechanisMs (λ on ∞). Lamoom focuses on building mechanisms that iteratively guide language models to perform correctly, every time.

Through our unique approach to automated testing, prompt validation, and multi-model optimization, Lamoom ensures your AI applications deliver reliable, consistent results—whether in development or production.

See How It Works
Lamoom Mechanisms

Features That Ensure LLM Reliability

Comprehensive tools to build, test, and optimize your AI applications

CI/CD Testing Icon

CI/CD Testing

Automatically generate tests based on context and ideal answers. Lamoom verifies LLM outputs against expected results and catches regressions before they reach users.

🔍 87% reduction in LLM-related issues
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Multi-Model Icon

Multi-Model Support

Seamlessly integrate with OpenAI, Anthropic, Gemini, and more. Lamoom distributes load across models based on performance needs and optimizes for cost-efficiency.

💰 Up to 40% reduction in API costs
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Dynamic Prompt Management

Dynamic Prompt Management

Update prompts without code deployments. Lamoom automatically handles prompt caching with 5-minute TTL for reduced latency while ensuring up-to-date content.

⚡ 95% faster prompt updates
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Real-Time Insights Icon

Real-Time Insights

Monitor interactions, request/response metrics, latency, and costs in production. Lamoom helps identify bottlenecks and opportunities for optimization.

📊 Real-time visibility into AI performance
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Asynchronous Logging Icon

Asynchronous Logging

Lamoom records all LLM interactions without blocking the main execution flow. Analyze performance and improve your prompts over time.

📝 Zero performance impact on your application
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Feedback Collection Icon

Feedback Collection

Use customer feedback to improve prompt quality. Lamoom associates ideal answers with previous responses for continuous improvement.

🔄 Continuous improvement system
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Easy to Implement

Just a few lines of code to get started

Python Logo 
# Install Lamoom
! pip install lamoom

# Import and initialize
from lamoom import Lamoom, Prompt

# Create a prompt template
summary_prompt = Prompt("article_summarizer")
summary_prompt.add(
    "Summarize the following article in 3 bullet points:\n{article}",
    role="system"
)
client = Lamoom(api_token="lamoom_api_key", opean_key="openai_api_key")

# Use it in your application
response = client.call(
    summary_prompt.id,
    context={"article": "article_text"},
    test_data={  # Optional: Creates test with context in CI/CD pipeline
        "ideal_answer": "A concise summary with 3 bullet points"
    }
)
print(response.content)
View More Examples

As Head of AI, I saw domain experts — the geniuses behind AI's logic — shrink from updating prompts, terrified of breaking systems. It's a reason why we built a Cloud.
The Cloud where they experiment fearlessly: edit prompts, update knowledge base, validate logic, watch AI improve.

Amazon taught me customer obsession; Our mission? Let experts own the "why" behind responses. Because when experts thrive, AI finally speaks human.

— Kate Yanchenko, Founder | Lamoom

How Lamoom Works

A mechanism-driven approach to building reliable AI applications

1

Prompt Management and Caching

Lamoom implements an efficient prompt caching system with a 5-minute TTL (Time-To-Live):

  • • Automatic Updates: When you call a prompt, Lamoom checks if a newer version exists on the server.
  • • Cache Invalidation: Prompts are automatically refreshed after 5 minutes to ensure your AI always has the latest instructions.
  • • Local Fallback: If the Lamoom service is unavailable, we intelligently fall back to locally defined prompts.
sequenceDiagram Note over Lamoom,LLM: call(prompt_id, context, model) Lamoom->>Lamoom: get_cached_prompt(prompt_id) alt Cache miss Lamoom->>LamoomService: get the last published prompt, Updates cache for 5 mins end Lamoom->>LLM: Call LLM with prompt and context
2

CI/CD Testing with Automatic Test Generation

What makes Lamoom truly unique is our approach to testing LLM outputs against ideal answers:

  • • Test Generation from Ideal Answers: Simply provide what a "correct" response should look like, and Lamoom automatically generates test scenarios.
  • • Two Testing Methods:
    • 1. Inline Testing: Add test_data with an ideal answer during normal LLM calls
    • 2. Direct Test Creation: Explicitly create tests for specific prompts
  • • Automatic Validation: Tests compare LLM responses to ideal answers, helping maintain prompt quality as models evolve.
  • • CI/CD Pipeline Integration: Automated testing on every prompt change ensures quality never degrades.
sequenceDiagram Lamoom->>LamoomService: call → creates asynchronous job to create test with an ideal answer
3

Asynchronous Logging for Performance Analysis

Lamoom's non-blocking logging system captures everything without sacrificing performance:

  • • Performance Metrics: Track latency, token usage, and cost automatically.
  • • Complete Context Storage: Retain all prompts, contexts, and responses for comprehensive analysis.
  • • Non-Blocking Architecture: Background processing ensures logging never impacts your application speed.
sequenceDiagram Lamoom->>LLM: call(prompt_id, context, model) Lamoom->>LamoomService: create asynchronous job to save logs
4

Feedback Collection for Continuous Improvement

Improve prompt quality through our built-in feedback loop:

  • • Ideal Answer Addition: Associate ideal answers with previous responses.
  • • Test Generation: Automatically create tests from user feedback.
  • • Prompt Refinement: Use real-world examples to constantly improve your AI.
sequenceDiagram Lamoom->>LamoomService: add_ideal_answer(response_id, ideal_answer)

Simple, Transparent Pricing

Choose the plan that fits your AI development needs

Free

Getting started

$0 forever
  • ✓ First seat in Organization for free
  • ✓ Observability Logs
  • ✓ CI/CD Pipeline of Prompts
  • ✓ Prompt Management
Get Started Free
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Pro

Collaboration on GenAI Product

$30 per seat / month
  • ✓ All Free tier features
  • ✓ Premium Support
  • ✓ Faster running tests
  • ✓ Team collaboration tools
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Enterprise

For teams with complex work needs

Custom contact for pricing
  • ✓ Premium Enterprise Support
  • ✓ We will help you to improve prompts
  • ✓ Adding tests
  • ✓ Finding best model for your use case
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Have questions about our pricing? Contact our team