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Trustworthy AI
Trustworthy AI
Trustworthy AI
Trustworthy AI
World’s first comprehensive evaluation and optimization platform to help enterprises achieve 99% accuracy in AI applications across software and hardware.
World’s first comprehensive evaluation and optimization platform to help enterprises achieve 99% accuracy in AI applications across software and hardware.
World’s first comprehensive evaluation and optimization platform to help enterprises achieve 99% accuracy in AI applications across software and hardware.

Create
Trustworthy AI
World’s first comprehensive evaluation and optimization platform to help enterprises achieve 99% accuracy in AI applications across software and hardware.

Integrated with
Integrated with
Faster AI Evaluation
Faster Agent Optimization
Model and Agent Accuracy in Production
Faster AI Evaluation
Faster Agent Optimization
Model and Agent Accuracy in Production

Integrated with
Faster AI Evaluation
Faster Agent Optimization
Model and Agent Accuracy in Production





LLMs are probabilistic.
Build, Evaluate and Improve AI reliably with Future AGI.
Build, Evaluate and Improve AI reliably with Future AGI.
Build, Evaluate and Improve AI reliably with Future AGI.

Generate and manage diverse synthetic datasets to effectively train and test AI models, including edge cases.

Generate and manage diverse synthetic datasets to effectively train and test AI models, including edge cases.

Test, compare and analyse multiple agentic workflow configurations to identify the ‘Winner’ based on built-in or custom evaluation metrics- literally no code!

Test, compare and analyse multiple agentic workflow configurations to identify the ‘Winner’ based on built-in or custom evaluation metrics- literally no code!

Assess and measure agent performance, pin-point root cause and close loop with actionable feedback using our proprietary eval metrics.

Assess and measure agent performance, pin-point root cause and close loop with actionable feedback using our proprietary eval metrics.

Enhance your LLM application's performance by incorporating feedback from evaluations or custom input, and let system automatically refine your prompt based.

Enhance your LLM application's performance by incorporating feedback from evaluations or custom input, and let system automatically refine your prompt based.

Track applications in production with real-time insights, diagnose issues, and improve robustness, while gaining priority access to Future AGI's safety metrics to block unsafe content with minimal latency.

Track applications in production with real-time insights, diagnose issues, and improve robustness, while gaining priority access to Future AGI's safety metrics to block unsafe content with minimal latency.

Evaluate your AI across different modalities- text, image, audio, and video. Pinpoint errors and automatically get the feedback to improve it.

Evaluate your AI across different modalities- text, image, audio, and video. Pinpoint errors and automatically get the feedback to improve it.

Generate and manage diverse synthetic datasets to effectively train and test AI models, including edge cases.

Test, compare and analyse multiple agentic workflow configurations to identify the ‘Winner’ based on built-in or custom evaluation metrics- literally no code!

Assess and measure agent performance, pin-point root cause and close loop with actionable feedback using our proprietary eval metrics.

Enhance your LLM application's performance by incorporating feedback from evaluations or custom input, and let system automatically refine your prompt based.

Track applications in production with real-time insights, diagnose issues, and improve robustness, while gaining priority access to Future AGI's safety metrics to block unsafe content with minimal latency.

Evaluate your AI across different modalities- text, image, audio, and video. Pinpoint errors and automatically get the feedback to improve it.

Generate and manage diverse synthetic datasets to effectively train and test AI models, including edge cases.

Test, compare and analyse multiple agentic workflow configurations to identify the ‘Winner’ based on built-in or custom evaluation metrics- literally no code!

Assess and measure agent performance, pin-point root cause and close the loop with actionable feedback using our proprietary eval metrics.

Enhance your LLM application's performance by incorporating feedback from evaluations or custom input, and let our system automatically refine your prompt.

Track applications in production with real-time insights, diagnose issues, and improve robustness, while gaining priority access to Future AGI's safety metrics to block unsafe content with minimal latency.

Evaluate your AI across different modalities- text, image, audio, and video. Pinpoint errors and automatically get the feedback to improve it.

Generate and manage diverse synthetic datasets to effectively train and test AI models, including edge cases.

Test, compare and analyse multiple agentic workflow configurations to identify the ‘Winner’ based on built-in or custom evaluation metrics- literally no code!

Assess and measure agent performance, pin-point root cause and close loop with actionable feedback using our proprietary eval metrics.

Enhance your LLM application's performance by incorporating feedback from evaluations or custom input, and let system automatically refine your prompt based.

Track applications in production with real-time insights, diagnose issues, and improve robustness, while gaining priority access to Future AGI's safety metrics to block unsafe content with minimal latency.

Evaluate your AI across different modalities- text, image, audio, and video. Pinpoint errors and automatically get the feedback to improve it.
Integrate into your Existing Workflow
Integrate into your Existing Workflow
Integrate into your Existing Workflow
Integrate into your Existing Workflow
Future AGI is developer-first and integrates seamlessly with industry-standard tools, so your team can keep their workflow unchanged.
Future AGI is developer-first and integrates seamlessly with industry-standard tools, so your team can keep their workflow unchanged.


from fi.integrations.otel import OpenAIInstrumentor, register from fi.integrations.otel.types import ( EvalName, EvalSpanKind, EvalTag, EvalTagType, prepare_eval_tags, ) from openai import OpenAI # Configure trace provider with custom evaluation tags eval_tags = [ EvalTag( eval_name=EvalName.DETERMINISTIC_EVALS, value=EvalSpanKind.LLM, type=EvalTagType.OBSERVATION_SPAN, config={ "multi_choice": False, "choices": ["Yes", "No"], "rule_prompt": "Evaluate if the response is correct", }, ) ] # Configure trace provider with custom evaluation tags trace_provider = register( endpoint="https://app.futureagi.com//tracer/observation-span/create_otel_span/", eval_tags=prepare_eval_tags(eval_tags), project_name="ANTHROPIC_TEST", project_version_name="v1", ) # Initialize the Anthropic instrumentation OpenAIInstrumentor().instrument(tracer_provider=trace_provider) client = OpenAI() completion = client.chat.completions.create( model="gpt-4o", messages=[ {"role": "developer", "content": "You are a helpful assistant."}, {"role": "user", "content": "Write a haiku about recursion in programming."}, ], ) print(completion.choices[0].message)


from fi.integrations.otel import OpenAIInstrumentor, register from fi.integrations.otel.types import ( EvalName, EvalSpanKind, EvalTag, EvalTagType, prepare_eval_tags, ) from openai import OpenAI # Configure trace provider with custom evaluation tags eval_tags = [ EvalTag( eval_name=EvalName.DETERMINISTIC_EVALS, value=EvalSpanKind.LLM, type=EvalTagType.OBSERVATION_SPAN, config={ "multi_choice": False, "choices": ["Yes", "No"], "rule_prompt": "Evaluate if the response is correct", }, ) ] # Configure trace provider with custom evaluation tags trace_provider = register( endpoint="https://app.futureagi.com//tracer/observation-span/create_otel_span/", eval_tags=prepare_eval_tags(eval_tags), project_name="ANTHROPIC_TEST", project_version_name="v1", ) # Initialize the Anthropic instrumentation OpenAIInstrumentor().instrument(tracer_provider=trace_provider) client = OpenAI() completion = client.chat.completions.create( model="gpt-4o", messages=[ {"role": "developer", "content": "You are a helpful assistant."}, {"role": "user", "content": "Write a haiku about recursion in programming."}, ], ) print(completion.choices[0].message)


from fi.integrations.otel import OpenAIInstrumentor, register from fi.integrations.otel.types import ( EvalName, EvalSpanKind, EvalTag, EvalTagType, prepare_eval_tags, ) from openai import OpenAI # Configure trace provider with custom evaluation tags eval_tags = [ EvalTag( eval_name=EvalName.DETERMINISTIC_EVALS, value=EvalSpanKind.LLM, type=EvalTagType.OBSERVATION_SPAN, config={ "multi_choice": False, "choices": ["Yes", "No"], "rule_prompt": "Evaluate if the response is correct", }, ) ] # Configure trace provider with custom evaluation tags trace_provider = register( endpoint="https://app.futureagi.com//tracer/observation-span/create_otel_span/", eval_tags=prepare_eval_tags(eval_tags), project_name="ANTHROPIC_TEST", project_version_name="v1", ) # Initialize the Anthropic instrumentation OpenAIInstrumentor().instrument(tracer_provider=trace_provider) client = OpenAI() completion = client.chat.completions.create( model="gpt-4o", messages=[ {"role": "developer", "content": "You are a helpful assistant."}, {"role": "user", "content": "Write a haiku about recursion in programming."}, ], ) print(completion.choices[0].message)


from fi.integrations.otel import OpenAIInstrumentor, register from fi.integrations.otel.types import ( EvalName, EvalSpanKind, EvalTag, EvalTagType, prepare_eval_tags, ) from openai import OpenAI # Configure trace provider with custom evaluation tags eval_tags = [ EvalTag( eval_name=EvalName.DETERMINISTIC_EVALS, value=EvalSpanKind.LLM, type=EvalTagType.OBSERVATION_SPAN, config={ "multi_choice": False, "choices": ["Yes", "No"], "rule_prompt": "Evaluate if the response is correct", }, ) ] # Configure trace provider with custom evaluation tags trace_provider = register( endpoint="https://app.futureagi.com//tracer/observation-span/create_otel_span/", eval_tags=prepare_eval_tags(eval_tags), project_name="ANTHROPIC_TEST", project_version_name="v1", ) # Initialize the Anthropic instrumentation OpenAIInstrumentor().instrument(tracer_provider=trace_provider) client = OpenAI() completion = client.chat.completions.create( model="gpt-4o", messages=[ {"role": "developer", "content": "You are a helpful assistant."}, {"role": "user", "content": "Write a haiku about recursion in programming."}, ], ) print(completion.choices[0].message)


from fi.integrations.otel import OpenAIInstrumentor, register from fi.integrations.otel.types import ( EvalName, EvalSpanKind, EvalTag, EvalTagType, prepare_eval_tags, ) from openai import OpenAI # Configure trace provider with custom evaluation tags eval_tags = [ EvalTag( eval_name=EvalName.DETERMINISTIC_EVALS, value=EvalSpanKind.LLM, type=EvalTagType.OBSERVATION_SPAN, config={ "multi_choice": False, "choices": ["Yes", "No"], "rule_prompt": "Evaluate if the response is correct", }, ) ] # Configure trace provider with custom evaluation tags trace_provider = register( endpoint="https://app.futureagi.com//tracer/observation-span/create_otel_span/", eval_tags=prepare_eval_tags(eval_tags), project_name="ANTHROPIC_TEST", project_version_name="v1", ) # Initialize the Anthropic instrumentation OpenAIInstrumentor().instrument(tracer_provider=trace_provider) client = OpenAI() completion = client.chat.completions.create( model="gpt-4o", messages=[ {"role": "developer", "content": "You are a helpful assistant."}, {"role": "user", "content": "Write a haiku about recursion in programming."}, ], ) print(completion.choices[0].message)
Case Study, Blogs and more
Case Study, Blogs and more
Case Study, Blogs and More
Case Study, Blogs and More

Enhancing Meeting Summarization Through Future AGI’s Intelligent Evaluation Framework
50% Increase
Summary Quality
10x Faster
Summary Evaluation

Revolutionizing Lead Generation: How Future AGI Empowers AI SDR Companies with Intelligent Prompt Evaluation
25% Increase
Response rate
10X Faster
Prompt evaluation

Enhancing Meeting Summarization Through Future AGI’s Intelligent Evaluation Framework
50% Increase
Summary Quality
10x Faster
Summary Evaluation

Revolutionizing Lead Generation: How Future AGI Empowers AI SDR Companies with Intelligent Prompt Evaluation
25% Increase
Response rate
10X Faster
Prompt evaluation

Enhancing Meeting Summarization Through Future AGI’s Intelligent Evaluation Framework
50% Increase
Summary Quality
10x Faster
Summary Evaluation

Revolutionizing Lead Generation: How Future AGI Empowers AI SDR Companies with Intelligent Prompt Evaluation
25% Increase
Response rate
10X Faster
Prompt evaluation


Ready to deploy Accurate AI?

Ready to deploy Accurate AI?

Ready to deploy Accurate AI?

Ready to deploy Accurate AI?

