AI agent evaluation for non‑profits
Existing AI evaluation tools are too complex or overly expensive for non‑profits to use. Calibrate is built by ML engineers with decades of experience to make AI evaluation accessible with best practices baked into every step
BUILT BY ARTPARK
@ IIScFUNDED BY GOVERNMENT OF KARNATAKA
Evaluate the quality
of your AI responses
Define edge cases and evaluate the agent's response against custom criteria
Add the conversation history as input to the agent
Define custom criteria to evaluate the agent's response given the conversation history
Run the test and see whether the agent response passed the evaluation criteria
Find the best LLM
for your agent
Compare different models on your tests to find the best one for your agent
Pick the models to compare and run benchmarking on all tests
Get a leaderboard across models and evaluators to pick the best LLM
Evaluate the quality
of your AI responses
Define edge cases and evaluate the agent's response against custom criteria
Add the conversation history as input to the agent
Define custom criteria to evaluate the agent's response given the conversation history
Run the test and see whether the agent response passed the evaluation criteria
Find the best LLM
for your agent
Compare different models on your tests to find the best one for your agent
Pick the models to compare and run benchmarking on all tests
Get a leaderboard across models and evaluators to pick the best LLM
Find the best speech‑to‑text model
for your users
Calibrate uses evaluators that compare the meaning of the predicted transcriptions with the references beyond simple rule-based metrics to rank different models
Upload your audios with reference texts
Select the language, models to compare and the evaluators for measuring transcription accuracy
See the leaderboard across models for each metric
For each model view row-by-row outputs along with evaluator scores and reasoning
Find the best speech‑to‑text model
for your users
Calibrate uses evaluators that compare the meaning of the predicted transcriptions with the references beyond simple rule-based metrics to rank different models
Upload your audios with reference texts
Select the language, models to compare and the evaluators for measuring transcription accuracy
See the leaderboard across models for each metric
For each model view row-by-row outputs along with evaluator scores and reasoning
Select the perfect voice
for your agent
Calibrate uses AI models which lets you evaluate the generated audios against the reference texts on pronunciation, clarity, naturalness and more
Add the reference texts to be spoken
Select the language, models to compare and the evaluators to measure the quality of the generated audios
See the leaderboard across models for each metric
For each model view the generated audios for each row along with evaluator scores and reasoning
Select the perfect voice
for your agent
Calibrate uses AI models which lets you evaluate the generated audios against the reference texts on pronunciation, clarity, naturalness and more
Add the reference texts to be spoken
Select the language, models to compare and the evaluators to measure the quality of the generated audios
See the leaderboard across models for each metric
For each model view the generated audios for each row along with evaluator scores and reasoning
Align your LLM judges
with human judgment
LLM evaluators are prone to mistakes as well. The only way to trust them is to compare them with human judgment. Collect human labels, measure alignment with LLM judges and iteratively improve them.
Create a human alignment task and attach the evaluators you want to align. Each evaluator becomes an annotation column for every item
Add the items to the task that humans will review and label
Create labelling jobs by assigning items to annotators and send unique links so that labels from one annotator never influence another's
Each annotator labels each item across all evaluators you want to align. Measure consistency in their labels by sending a few common items to all annotators
Run your evaluators on the same items and review disagreements between LLM judges and human labels
Iteratively improve LLM judge alignment by experimenting with different evaluator settings. Once aligned, the evaluator can be reliably used to automate monitoring.
Align your LLM judges
with human judgment
LLM evaluators are prone to mistakes as well. The only way to trust them is to compare them with human judgment. Collect human labels, measure alignment with LLM judges and iteratively improve them.
Create a human alignment task and attach the evaluators you want to align. Each evaluator becomes an annotation column for every item
Add the items to the task that humans will review and label
Create labelling jobs by assigning items to annotators and send unique links so that labels from one annotator never influence another's
Each annotator labels each item across all evaluators you want to align. Measure consistency in their labels by sending a few common items to all annotators
Run your evaluators on the same items and review disagreements between LLM judges and human labels
Iteratively improve LLM judge alignment by experimenting with different evaluator settings. Once aligned, the evaluator can be reliably used to automate monitoring.
Simulate conversations
with your agent
Catch bugs and regressions in your agent before deploying it to real users
Create user personas to define who your users are
Create scenarios to depict the purpose of the user's interaction with the agent
Run a simulation with personas and scenarios using custom evaluators and get performance metrics across all runs
Inspect each simulation run with full transcript and generated audios for the agent and the simulated user
Simulate conversations
with your agent
Catch bugs and regressions in your agent before deploying it to real users
Create user personas to define who your users are
Create scenarios to depict the purpose of the user's interaction with the agent
Run a simulation with personas and scenarios using custom evaluators and get performance metrics across all runs
Inspect each simulation run with full transcript and generated audios for the agent and the simulated user
Proudly open source
What we open-source is what we use ourselves. Nothing hidden behind a paywall.
Self-hosting
We can help you run Calibrate on your infrastructure to ensure sensitive data stays in environments you control
No per-seat pricing. Ever.
No per-user fees. Add staff, partners, and consultants as your team grows
Auditable, end to end
The full codebase is on GitHub for pre-deploy review and real diligence
No vendor lock-in
Fork, adapt, and make changes as you wish
Works with any AI agent stack
Supports all major models with more coming soon
Supports integrations including Deepgram, ElevenLabs, OpenAI, Google, Cartesia, Anthropic, Groq, DeepSeek, Smallest AI, Claude, Gemini, Qwen, Meta, Mistral, Cohere, Sarvam, AI21, Baidu, NVIDIA, Amazon.
Join the community
Talk to the team building Calibrate to get your questions answered and shape our roadmap
Team
Combined experience of 25+ years building AI systems
Start Calibrating today
Become a team that ships trustworthy AI agents beyond vibe checks