LLMs that keep getting better. In your infrastructure.
Deploy leading open-source LLMs in minutes, and automatically improve their model weights using past conversations.
Custom Intelligence brings self-hosted inference and continuous reinforcement learning into one automated platform. Build toward frontier-beating performance on your workflows while keeping inference, training, and model weights in your infrastructure.
Inference
Handles requests, caches responses, scales to zero.
Data extraction
Finds conversation data suited to reinforcement learning.
Weight updates
Asynchronous training service runs in your cloud.
Zero-interruption
Updated weights go live without downtime.
Don't just host a model. Make it learn your business.
A general-purpose model starts with broad capabilities. But it doesn't automatically get better at your company's terminology, recurring tasks, or operational workflows.
Custom Intelligence closes that gap.
Start with a leading open-source LLM. Run it in your environment. Then turn relevant conversation history into post-training data through an automated, asynchronous improvement loop.
The result: a model that evolves around the work your enterprise actually needs it to do.
What changes
Improvement reaches the model weights themselves, not only the context you supply.
Serving, data preparation, training, and deployment operate as one continuous loop.
Weights and conversation history stay in the environment your enterprise manages.
Inference scales to zero in seconds when demand disappears.
Deploy in minutes. Improve continuously. Pay for inference when you need it.
Bring leading open-source models into your infrastructure
Deploy leading open-source models without building a self-hosting stack from scratch.
Run inference in your AWS, Azure, or GCP environment, with your model and conversation history inside your infrastructure.
Move from model selection to deployment without infrastructure work.Improve the weights, not just the prompts
Custom Intelligence analyzes past conversations to extract relevant reinforcement learning data, then runs post-training to update the model's weights.
As your model learns from your workflows, it can move beyond generic performance toward a more capable, task-specific system—with the goal of outperforming frontier models on your own work.
Turn everyday usage into a continuous improvement opportunity.Stop paying for idle inference
A proxy starts or pauses your inference service based on demand. Scale to zero in seconds when the model isn't in use, and use response caching to avoid unnecessary inference.
Reduce idle compute costs without keeping a model running around the clock.Post-training compute, storage, and other cloud resources are billed separately by your infrastructure provider.
From conversations to better model weights, automatically
Four automated stages that turn real usage into a better model—running entirely inside your cloud account.
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1
Deploy an open source LLM in your cloud
Your applications connect through a proxy that manages the inference service in your infrastructure.
The proxy
- Starts or pauses the service as needed.
- Manages a response cache to optimize costs.
- Stores conversation history for post-training.
Your team gets a single entry point for inference while Custom Intelligence handles the underlying lifecycle.
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2
Turn conversations into training data
Conversation history is automatically analyzed to identify relevant data for reinforcement learning.
Real usage becomes the foundation for model improvement without a manual data-preparation cycle for every update.
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3
Update the model weights
An automated post-training service launches in your infrastructure and uses the extracted data to update the model's weights.
The learning loop runs asynchronously, separate from live inference.
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4
Deploy improvements without interruption
The updated weights are deployed without interrupting the inference service.
Your application keeps running while the model evolves. As new conversations arrive, the improvement loop continues.
From a general-purpose model to your workflow specialist
Frontier models are built for broad capability. Your business needs consistent performance on a particular set of tasks.
Custom Intelligence uses past conversations to drive ongoing, workflow-specific improvements. Rather than relying only on longer prompts or more context, it changes the model itself.
That improvement happens asynchronously, while your application continues serving requests.
Enterprise ownership, without stitching together the stack
Keep deployment and training in your environment
Run inference and post-training in your own AWS, Azure, or GCP infrastructure. Keep model weights and conversation history within the environment your enterprise manages.
Build on open-source models
Choose from leading open-source LLMs rather than making a proprietary model API the foundation of your AI stack.
Make improvement part of operations
Connect real usage to weight updates through an automated pipeline—not a series of disconnected data exports, training jobs, and deployment handoffs.
Align inference costs with demand
Combine on-demand serving, response caching, and rapid scale-to-zero to reduce wasted compute, especially for workloads with variable or intermittent traffic.
Measure success on your work—not someone else's benchmark
The best model for your enterprise is the one that performs best on your tasks at an acceptable cost. Custom Intelligence is designed to help you pursue three outcomes:
Improve model weights using relevant data from actual usage.
Run serving and post-training in your environment.
Reduce idle runtime and avoid redundant computation.
The goal is frontier-beating quality where it matters to your business—not a blanket claim of superiority on every task.
Frequently asked questions
What is Custom Intelligence?
Custom Intelligence is a platform for deploying and continuously improving self-hosted open source LLMs. It manages inference activity, uses past conversations to generate relevant post-training data, and runs an automated reinforcement learning loop to update model weights.
Which models can I deploy?
+100 model options including DeepSeek-V4.1, GLM-5.3, Kimi K3, Qwen3.8, and MiniMax-M3.
Contact us to discuss the right model for your workflows and infrastructure.
Where do inference and post-training run?
Both run in your own infrastructure on AWS, Azure, or GCP.
How is this different from prompting or retrieval?
Prompting and retrieval change the information a model receives at inference time. Custom Intelligence updates the model’s weights through post-training, changing the model itself based on relevant data extracted from past conversations.
Will my model outperform frontier models?
That is the objective for your specific workflows—not a claim of universal superiority. Results depend on the starting model, the available conversation data, and the tasks you evaluate. Performance should be measured against the frontier models and quality criteria relevant to your use case.
Does training interrupt live inference?
No. Post-training runs asynchronously, and updated weights are deployed without interrupting the inference service.
How does scale-to-zero reduce costs?
The proxy pauses the inference service when it is not needed and starts it when required. Scaling to zero reduces idle inference compute, while response caching helps avoid unnecessary model calls. Post-training, storage, and other cloud resources may still incur costs.
Build an AI advantage that improves with use
Start with a leading open-source model. Deploy it in your cloud. Turn your enterprise's conversations into a continuous path toward better performance.