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Future agi

Create

Create

Create

Create

Trustworthy AI
Accurate AI
Responsible AI

Trustworthy AI

Trustworthy AI
Accurate AI
Responsible AI

Trustworthy AI

Trustworthy AI
Accurate AI
Responsible AI

Trustworthy AI

Trustworthy AI
Accurate AI
Responsible AI

Trustworthy AI

10x Faster
10x Faster
10x Faster
10x Faster

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.

Future agi

Create

Trustworthy AI
Accurate AI
Responsible AI

Trustworthy AI

10x Faster

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

Future agi

Integrated with

  • Open AI
  • Anthropic
  • Claude
  • Hugging Face
  • Together AI
  • Deepseek
  • Perplexity
  • Cohere
  • sagemaker
  • Palm model
  • Grok
  • Azure
  • Gemini
  • Mistral AI
  • Bedrock
  • Llama

Integrated with

  • Open AI
  • Anthropic
  • Claude
  • Hugging Face
  • Together AI
  • Deepseek
  • Perplexity
  • Cohere
  • sagemaker
  • Palm model
  • Grok
  • Azure
  • Gemini
  • Mistral AI
  • Bedrock
  • Llama
0x

Faster AI Evaluation

0x

Faster Agent Optimization

0%

Model and Agent Accuracy in Production

0x

Faster AI Evaluation

0x

Faster Agent Optimization

0%

Model and Agent Accuracy in Production

Future agi

Integrated with

  • Open AI
  • Anthropic
  • Claude
  • Hugging Face
  • Together AI
  • Deepseek
  • Perplexity
  • Cohere
  • sagemaker
  • Palm model
  • Grok
  • Azure
  • Gemini
  • Mistral AI
  • Bedrock
  • Llama
0x

Faster AI Evaluation

0x

Faster Agent Optimization

0%

Model and Agent Accuracy in Production

Future AGI
Future AGI
Future AGI
Future AGI
Future AGI

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.

Dataset
Datasets

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

Dataset
Datasets

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

Experiment
Experiment

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

Experiment
Experiment

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

Evaluate
Evaluate

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

Evaluate
Evaluate

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

Improve
Improve

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

Improve
Improve

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

Monitor & protect
Monitor & Protect

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.

Monitor & Protect
Monitor & Protect

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.

Custom and Multimodal for your horizontal use-case
Custom and Multimodal for your horizontal use-case

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

Custom and Multimodal for your horizontal use-case
Custom and Multimodal for your horizontal use-case

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

Dataset
Datasets

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

Experiment
Experiment

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

Evaluate
Evaluate

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

Improve
Improve

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

Monitor & protect
Monitor & Protect

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.

Custom and Multimodal for your horizontal use-case
Custom and Multimodal for your horizontal use-case

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

Dataset
Datasets

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

Experiment
Experiment

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

Evaluate
Evaluate

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

Improve
Improve

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

Monitor & Protect
Monitor & Protect

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.

Custom and Multimodal for your horizontal use-case
Custom and Multimodal for your horizontal use-case

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

Dataset
Datasets

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

Experiment
Experiment

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

Evaluate
Evaluate

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

Improve
Improve

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

Monitor & protect
Monitor & Protect

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.

Custom and Multimodal for your horizontal use-case
Custom and Multimodal for your horizontal use-case

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

Logo Icon
Logo Icon

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.

Open ai
anthropic
llama
langchain
haystack
bedrock
mistralai
dspy
groq
crewai
litellm
instructor
vertexai google
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)
Open ai
anthropic
llama
langchain
haystack
bedrock
mistralai
dspy
groq
crewai
litellm
instructor
vertexai google
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)
Open ai
anthropic
llama
langchain
haystack
bedrock
mistralai
dspy
groq
crewai
litellm
instructor
vertexai google
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)
Open ai
anthropic
llama
langchain
haystack
bedrock
mistralai
dspy
groq
crewai
litellm
instructor
vertexai google
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)
Open ai
anthropic
llama
langchain
haystack
bedrock
mistralai
dspy
groq
crewai
litellm
instructor
vertexai google
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

Future agi
Future agi

Ready to deploy Accurate AI?

Book a Demo
Future agi

Ready to deploy Accurate AI?

Book a Demo
Future agi

Ready to deploy Accurate AI?

Book a Demo
Future agi

Ready to deploy Accurate AI?

Book a Demo
Future agi
Future agi

Ready to deploy Accurate AI?

Book a Demo