One platform.
Close the loop between AI development and production.
Integrate development and production to enable a data-driven iteration cycle—real production data powers better development, and production observability aligns with trusted evaluations.
Comprehensive Observability & Evaluation Features
terr.io offers a suite of powerful tools designed to give you unparalleled visibility and control over your LLM deployments, from development to production.
Agent Monitoring
Monitor and evaluate AI agents in production with comprehensive observability tools.
Tracing Capabilities
Track and analyze the complete execution path of your AI models for better debugging.
Custom Evaluators
Assess model performance with customizable evaluation metrics and benchmarks.
AI Co-Pilot Assistance
Get intelligent assistance for optimizing your AI systems and workflows.
Experiment Management
Run controlled experiments to compare model versions and configurations.
Prompt Optimization
Manage and optimize prompts with version control and performance tracking.
Solve Real-World AI Challenges
terr.io is versatile, addressing critical observability and evaluation needs across various AI applications.
Generative AI
Monitor and evaluate generative AI models in production environments for quality, safety, and performance drift.
ML & Computer Vision
Track performance and detect drift in machine learning and computer vision models.
Trusted by Industry Leaders
"Reckon observability is pretty awesome!"
Andrei Fajardo
Founding Engineer, LlamaIndex
"From Day 1 you want to integrate some kind of observability. In terms of prompt engineering, we use Reckon to look at the traces to see the execution flow to determine the changes needed there."
Kyle Weston
Lead Data Scientist, GenAI, Geotab
"As we continue to scale GenAI across our digital platforms, Reckon gives us the visibility, control, and insights essential for building trustworthy, high-performing systems."
Charles Holive
SVP, AI Solutions and Platforms, PepsiCo
Dashboard: LLM Performance Overview
This dashboard provides a simulated overview of LLM performance based on hypothetical evaluations. It allows you to quickly gauge model quality and efficiency metrics over time.
Avg. Response Quality
4.2 / 5
Avg. Latency (ms)
580
Total Evaluations
1,245
Avg. Cost per Request (Simulated)
$0.003
Toxicity Score (Avg.)
0.05
Bias Index (Avg.)
0.12
Model Performance Trends (Simulated)
Illustrative data: Trend of hypothetical accuracy for different LLM models over time.
Evaluate Prompts & Responses
Simulate submitting a prompt to an LLM and then evaluate its response based on key criteria. This helps in understanding response quality and identifying areas for improvement in prompt engineering and model fine-tuning.
"Fīde, sed verifica." — Trust, but verify.
Recent Evaluation History
Review a simulated history of past prompt submissions and their evaluations. This helps track individual performance and identify patterns across different LLM models and prompt variations.
| Date | Prompt (Excerpt) | Model | Rating | Criteria | Latency (ms) | Tokens |
|---|
Note: This history is simulated and clears on page refresh.
Prompt Engineering & Evaluation Best Practices
Effective prompt engineering is key to getting optimal LLM responses. These guidelines, adapted from Generative Search Optimization (GSO) principles, also serve as criteria for robust evaluation, ensuring your AI systems perform at their best.
Clarity & Conciseness
Factual Accuracy & Verifiability
Topical Authority & Context
User Intent Alignment
Trust Signals (E-E-A-T)
Structured Prompts & Output
Learn & Community
Expand your knowledge and connect with the LLM community through our curated resources and events.
Docs & Phoenix Open Source
Access comprehensive documentation for platform features and contribute to our open-source initiatives.
- Platform Documentation
- Phoenix Open Source
Blog
Stay up-to-date with the latest insights, research, and best practices in LLM observability and evaluation.
Agents Hub
Explore resources and case studies specifically for monitoring and evaluating AI agents.
LLM Evals Hub
A comprehensive collection of tools, methodologies, and benchmarks for LLM evaluation.
Courses & Readings
Access structured courses like "Evaluating AI Agents" and our "AI Paper Readings" series.
- Evaluating AI Agents course
- AI Paper Readings
Community & Events
Join our community, participate in discussions, and attend upcoming workshops and events.
Topics
Dive deep into specific subjects related to LLM evaluation and AI agent development.
- LLM Evaluation
- Agent Evaluation
- AI Agent: Useful Case Study
- AI Product Manager
- LLM as a Judge
- LLM Tracing
- Prompt Optimization Techniques
Company
Learn more about terr.io's mission, careers, and commitment to building trustworthy AI systems.
About Us
Our mission is to close the loop between AI development and production, enabling data-driven iteration cycles.
Careers
Join our team and help us build the future of AI observability and evaluation.
Partners
Collaborate with us to expand the ecosystem of trustworthy AI solutions.
Press & Security
Access our press kit and learn about our robust security practices.
- Press Resources
- Security Statement