
Tasks ready to use
Paradigm offers a ready-to-go list of task that your company can use to automate repetitive tasks.
Get a well-organized view of all task that your co-workers have built.

Generative task builder
Behind every AI application, there is a prompt.
Prompt engineering is a new skill that is not easy to acquire.
Paradigm helps your team to reduce the effort to build powerful tasks by a simple journey (No code).

Task sharing
With Paradigm, you can share a ready-to-go interface with your colleagues, where they only need to give context to the large language model and receive their results with minimal effort.
Build your task in 4 steps
Requirement
Provide a brief description of the task template you want to create
Success criteria
Select success criteria auto generated to clarify your need
Context fields
Select fields that will guide users in providing essential context
Save and share
Save and share the task with your colleagues
Introducing Alfred (40B)
Alfred (40B) is designed to be the reliable partner on your journey to integrate Generative AI into your business workflow.
You can use Alfred or build your own LLM
Privacy
Deploy Paradigm in your infrastructure (on-premise or private cloud).
Custom
Create customized tasks that are specifically designed for each business unit.
Productivity
Enhance your colleague's productivity with the implementation of automated tasks.
3 plans adapted to your need
SaaS
Business
- 100K completions/month
- API
- Fine-tuning
Private cloud
Enterprise
- Completion unlimited
- API
- Fine-tuning
On-premise
Enterprise +
- Completion unlimited
- API
- Fine-tuning
Let's embark on a journey together. Contact us and let's explore the possibilities.
Other products

Model Factory
LightOn has crafted a dozen notable LLMs from 1B to > 100B parameter. Beyond the ability to build base models, customization through fine tuning is also provided, and community collaboration is fostered by open sourcing models.

Chat with docs (RAG)
Using the power of natural language processing, Chat with docs (RAG) understands user queries and retrieves specific sections or answers from documents, saving valuable time spent on manual searching.