Ollaya

Free Download Ollaya for Windows PC. It is a private local decision-model platform that lets you download, run, and serve open models directly on your own hardware.

Overview of Ollaya

An open-source solution for running decision models locally without sending your information to an external cloud service. It works like Ollama, but it focuses on decision models rather than traditional text-generating language models. You can pull and serve models for tasks such as classification, natural language inference, decision-making, and structured question answering.

The software is useful when you need quick and predictable answers from text or structured data. A decision model receives a state, such as an email, support ticket, document, message, or JSON object, along with typed questions. It then analyzes that information and returns typed answers with calibrated probabilities in a single forward pass.

Run Decision Models Locally

One of the tool's main benefits is local model execution. Models are downloaded to your computer and can run on your own hardware. This gives you greater control over your information and reduces dependence on remote AI services.

The local approach is especially useful for private documents, internal business information, customer messages, or other sensitive content. Instead of uploading data to a third-party server for processing, you can keep the workflow on your own machine.

Support for Open Decision Models

The platform is designed to pull and serve open decision models, including Laya, decider, NLI, and GLiClass models. Each model can handle a different type of decision task, depending on your workflow and the information you want to analyze.

This gives developers the freedom to choose a suitable model instead of relying on one general-purpose AI system. Think of it as a toolbox with several specialized tools, so you can choose the right one for the job.

Typed Questions and Answers

A key feature is the ability to ask typed questions about your input. The input can be normal text or structured JSON data. Instead of generating a long natural-language response, a model provides an answer in the expected data type.

This approach can make AI-powered applications easier to control. Developers know what type of result to expect, which can simplify validation and integration with other software. It is particularly helpful for applications that require clear decisions instead of creative text generation.

Calibrated Probabilities

The software does more than return a simple answer. Its decision models can also provide calibrated probabilities that indicate how strongly the model supports a result. This extra information helps applications understand the confidence associated with a particular prediction.

For example, a system analyzing a support ticket could determine its category while also returning a probability for that decision. Developers can then use the probability in their own application logic, such as deciding whether automatic processing is suitable or further review is needed.

Fast Single-Pass Processing

Decision models return answers in a single forward pass. They do not need to generate a response one word or token at a time like a traditional generative language model. This can make the process fast and well-suited to workflows that need to handle many decisions efficiently.

Results can return in milliseconds, depending on the selected model, hardware, and workload. This makes the tool useful for applications such as ticket routing, classification, content analysis, automated workflows, and real-time decision systems.

No Text Generation

Unlike a typical chatbot or generative AI assistant, the models used by this platform do not generate paragraphs of text. Their job is to examine the supplied state and answer specific typed questions. This keeps the workflow focused on decisions rather than content creation.

Such an approach is useful when you need structured results that another program can process immediately. Instead of extracting an answer from a long AI response, an application can work directly with the returned value and its associated probability.

TypeSafe-Compatible API

The tool serves its models behind a TypeSafe-compatible API. This provides developers with a structured way to connect local decision intelligence to applications, scripts, services, and automated workflows.

An API-based approach also keeps the model layer separate from the main application. Your program can send the required state and typed questions to the local service, receive the results, and then decide what action to take. This makes it easier to build reusable and organized AI workflows.

Works with Text and JSON

The software is not limited to plain sentences or documents. It works with both text and JSON, making it flexible for different types of applications. You might analyze an email, customer request, help-desk ticket, product information, or structured application state.

JSON support is particularly useful for developers because modern applications often store and exchange information in structured form. The model can receive this state along with questions, making decision intelligence part of an existing software workflow.

Privacy and Local Control

Privacy is an important advantage of running models on your own computer. Your data can remain on local hardware while decisions are processed. This can be valuable for developers and organizations that prefer greater control over where their information is handled.

Local operation can also reduce reliance on internet-based AI services. Once the required models and components are available on the machine, you have a self-controlled environment to build and test decision-based applications.

Useful for Developers and Automation

The platform can fit into many developer workflows. It can help classify information, analyze states, route requests, check conditions, and return structured decisions that software can use automatically. The combination of typed output and probability information makes it suitable for systems that require predictable results.

Developers can also experiment with different open models while keeping a familiar local serving workflow. This makes the software an interesting option for anyone exploring decision intelligence without depending entirely on large generative AI models or hosted services.

System Requirements

• Operating System: Windows 11 / 10 / 8.1 / 7
• Processor: Minimum 1 GHz Processor (2.4 GHz recommended)
• RAM: 2GB (4GB or more recommended)
• Free Hard Disk Space: 200MB or more is recommended

Conclusion

Ollaya provides a practical way to download and serve open decision models directly on local hardware. Its support for typed questions, structured answers, calibrated probabilities, text and JSON input, and a TypeSafe-compatible API makes it useful for developers building private decision-based applications. By focusing on fast decisions instead of generated text, it offers a focused approach to local AI processing while giving users greater control over their models and data.