Enabling AI companies to thrive
Enabling AI companies to thrive
Embedded Learning & Human In The Loop
Embedded Learning & Human In The Loop
Why it matters?
Democratize
As of today only AI labs have access to high quality RLHF/PPO annotators by spending millions. Small companies can not afford it, but they can directly ask to their users! We provide tools to democratise high quality complex human feedback to improve every AI agent .
Understand
Models are not trained to make questions, they rarely do it. Probably only based on
training sets, but our research shows that we can define and detect LLMs uncertainty.
We think that here it lies the road to more secure and hallucination free systems.
Security & Safety
Autonomous agents will handle most of our digital work, but some task are simply too
important or mission critical to leave the agent execute by himself. For this reason
approval methods will be needed for most deep integrations.
Human Experience
Companies represent people and each of us is different, we envision a world where agents UX will greatly depend on who will use it and today we completely lack infrastructure to
reinforce specific traits based on subjective people preferences.
Avoid errors with human guidance
Can you find for me the last quarter financial data based on the report?
Annual_Report
Can you find for me the last quarter financial data based on the report?
I'm not sure, which report are you referring to:
Financials_2024
In AI applications models can hallucinate for lack of informations, we give it the tool to actually call for help to humans when it feel the necessity to have more informations to answer correctly
Approve or reject critical actions
Can you modify the database scheme to make it more efficient?
Approve
Reject
I can create the new scheme, but this action could be dangerous. Do you want me to execute it?
If your AI agent has access to critical code functions that act as tools for executing its tasks, we can add special decorators to enable strict approval policies if that function is being called
An easy to use generative UI widget to embed new experiences in your AI product, we leverage your model uncertanty to enhance learning trough human interaction
An easy to use generative UI widget to embed new experiences in your AI product, we leverage your model uncertanty to enhance learning trough human interaction
Remove instantly reliability bottlenecks and deploy your AI agents
Remove instantly reliability bottlenecks and deploy your AI agents
Ask human permission to approve critical tasks
Request feedback and guidance if the model is confuse
RLAIF & RLHF or PPO ready to use dataset to enhance your AI
ADAS tell us about the future
At Waymo combining human teleoperator with autonomous agents reduced collision by 90%
Improve user experience
Alice C.
Please contact the founder of the company on Linkedin
David M.
Can you find for me the last quarter financial data based on the report?
Yes, I can do it! I found multiple founders who should I write to?
Aaron K.
Agent interactions are nowadays like GUI in the 80', we let your model experiment with multiple generative interactions and perform optimisations based on A/B tests
Finetune your agent with user feedbacks
Can you explain to me how this snippet of code works?
Answer 2
Can you find for me the last quarter financial data based on the report?
Yes, please let me know which one you like
most for the next time:
Answer 1
Let your users steer the agent by deciding which answers they prefer. Our tool dynamically popup multiple answers when it is less confident or when you program it to do it. After this phase we can create a synthetic dataset to enhance your model performance based on feedbacks
Why it matters?
Democratize
AI labs have access to high quality RLHF/PPO annotators by spending millions.
Small companies can not afford it, but they can directly ask to their users! We provide
tools to democratise high quality complex human feedback to improve every AI company
in the world.
Understand
Models are not trained to make questions, they rarely do it. Probably only based on
training sets, but early research show that we can define and detect LLMs uncertainty.
We think that here it lies the road to more secure and hallucination free systems.
Security & Safety
Autonomous agents will handle most of our digital work, but some task are simply too
important or mission critical to leave the agent execute by himself. For this reason
approval methods will be needed for most deep integrations.
Human Experience
Companies represent people and each of us is different, we envision a world where agents UX will greatly depend on who will use it and today we completely lack infrastructure to
reinforce specific traits based on subjective people preferences.
Enable Continuous Learning
Prompt Search
Based on previous positive interaction classify future similar one and inject the same behaviour
SFT/PPO
Process Reward Model
An internal model based on privacy preserving data will help guide your model towards optimal interactions
Fine-tuning dataset starting from positive interactions and augmented with synthetic data, ready to use on major platforms (OpenAI, Anthropic etc)
As humans we evolve continuously based on experiences, current AI models can't do this.
The problem lies in the fact that they don't have a world model.
Thanks to human interactions we have grounded data on when the interaction was good or bad.
This enable continuous optimization based on data.
As humans we evolve continuously based on experiences, current AI models can't do this.
The problem lies in the fact that they don't have a world model.
Thanks to human interactions we have grounded data on when the interaction was good or bad.
This enable continuous optimization based on data.
Enable Continuous Learning
Prompt Search
RLHF/PPO
Reward Process Model
Based on previous interaction classify future similar one and inject the same behaviour
A ready to use dataset starting from positive interactions and augment it with synthetic data, ready to use on major platforms (OpenAI, Anthropic etc)
An internal model based on privacy preserving data will help guide your model towards optimal interactions
Humans-in-the-loop as teachers
Humans-in-the-loop as teachers
Product users
Product users
Team members
Team members
Experts as a service
AI coworkers
Klara Engine
Klara Engine
AI coworkers
Speed of deployment
6x
Accuracy
2x
3x
Speed of deployment
Reliability
+80%