AI Control Plane for LLM Reliability
Make sure your AI delivers exactly what you envisioned with Qualifire’s state-of-the-art evaluation, guardrails and controls platform
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Evaluations
Observability
Realtime policy enforcement
Guardrails
Prompt management
Data curation
Trusted by Leading AI Innovators
Product Capabilities
Instruction Following
Custom Assertions
Grounding
Customized evaluations
SOTA models purposely built to evaluate your LLM application
Real time enforcement
Detect and block unwanted AI behaviours for Chatbots, RAG and multi agent application
Prompt management
Streamline prompt engineering with safety and quality built in
Tracing and observability using OTEL
Monitor, evaluate and control multi agent applications
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Easy Integration
OpenAI V4+
JS OpenAI
Python OpenAI
from openai import OpenAI
client = OpenAI(
api_key=os.environ[“OPENAI_API_KEY”],
base_url=”https://proxy.qualifire.ai/api/providers/opeai”,
default_headers={
”X-Qualifire-Api-Key”: f”{os.environ[‘QUALIFIRE_API_KEY’]}”,
},
)
api_key=os.environ[“OPENAI_API_KEY”],
base_url=”https://proxy.qualifire.ai/api/providers/opeai”,
default_headers={
”X-Qualifire-Api-Key”: f”{os.environ[‘QUALIFIRE_API_KEY’]}”,
},
)
import qualifire
client = qualifire.client.Client(
api_key=”YOUR API KEY”,
)
client = qualifire.client.Client(
api_key=”YOUR API KEY”,
)
res = client.evaluate (
input=”what is the capital of France”,
output=”Paris”,
prompt_injections=”True”,
pii_check=”True”,
hallucinations_check=”True”,
grounding_check=”True”,
consistency_check=”True”,
assertions=”don\’t give medical advice’”,
dangerous_content_check=”True”,
harassment_check=”True”,
hate_speech_check=”True”,
sexual_content_check=”True”,
)
input=”what is the capital of France”,
output=”Paris”,
prompt_injections=”True”,
pii_check=”True”,
hallucinations_check=”True”,
grounding_check=”True”,
consistency_check=”True”,
assertions=”don\’t give medical advice’”,
dangerous_content_check=”True”,
harassment_check=”True”,
hate_speech_check=”True”,
sexual_content_check=”True”,
)
Our Models
Our research team has developed several cutting-edge detection models trusted by developers and organizations around the globe
Sentinel
SOTA jailbreak detection
0.980
F1-Score
~20ms
latency
Cleric
SOTA unsafe content detection
0.874
F1-Score
~35ms
latency
Paladin
Content grounding evaluation
98.2%
Bacc
~70ms
latency
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Strategic partnership that drives the AI innovation forward
We’re Making an Impact
Extremely easy!
With Qualifire it’s extremely easy to build, monitor and enforce quality standards. No more blind spots!
Shay Erlichmen
Technical Director
Great results!
Qualifire has been a game-changer for our AI initiatives, ensuring we produce accurate, safe, and trustworthy outputs—without adding latency or disrupting workflows. Since integrating Qualifire, we’ve accelerated AI adoption and seen great results.
Alex Amdor
Head Of Product
That was incredible!
Qualifire seamlessly integrated into our production, and allows us to easily monitor and troubleshoot our AI features.
Ira Cohen
Chief Data Scientist
Real game-changer!
Qualifire has been a game-changer for us, seamlessly enhancing the quality and consistency of our AI-generated outputs.
Uria Franko
CTO & Co-Founder
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