About Tiny Models AI

TinyModels.AI develops custom machine learning models with fewer than 2 billion parameters, designed for high accuracy and rapid deployment in specialized tasks across various industries. These lightweight models operate efficiently on consumer-grade hardware, providing a cost-effective alternative to large language models while achieving over 97% accuracy in domain-specific applications.

```xml <problem> Many organizations rely on large language models (LLMs) for specialized tasks, which can be computationally expensive, slow, and less accurate than purpose-built solutions. Rule-based systems often fall short when dealing with complex patterns and unstructured data, while scaling human-like perception to handle high volumes is impractical. </problem> <solution> TinyModels.AI develops custom, small-parameter machine learning models tailored for specific industry applications, offering a cost-effective and efficient alternative to LLMs. These models, with fewer than 2 billion parameters, achieve high accuracy (over 97%) on specialized tasks while operating efficiently on consumer-grade hardware. TinyModels.AI delivers these custom models rapidly, enabling organizations to quickly deploy AI solutions that outperform larger, general-purpose AI models in terms of accuracy, speed, and cost. The models can be used for a variety of tasks, including API and function routing, high-precision classification, custom named entity recognition, tailored natural language processing, PDF parsing, and RAG with intelligent chunking. </solution> <features> - Custom machine learning models with fewer than 2 billion parameters - High accuracy (over 97%) in domain-specific applications - Rapid development and deployment - Efficient operation on consumer-grade hardware - API & Function Routers: Models that direct requests to the right APIs or functions. - High-Precision Classifiers: Models for categorizing data in any domain. - Custom Named Entity Recognition: Models to extract specific entities from text. - Tailored Natural Language Processing: Bespoke models for understanding and processing text. - PDF Parser: 500 pages/min extraction of structured data from PDF documents. - RAG with Intelligent Chunking: Smart text segmentation and retrieval. </features> <target_audience> The primary target audience includes organizations across various industries seeking efficient and accurate AI solutions for specialized tasks, particularly those who find LLMs too resource-intensive or inaccurate for their specific needs. </target_audience> ```

What does Tiny Models AI do?

Where is Tiny Models AI located?

Tiny Models AI is based in Miami, United States.

When was Tiny Models AI founded?

Tiny Models AI was founded in 2023.

Who founded Tiny Models AI?

Tiny Models AI was founded by Ana Olano.

Ana Olano - Founder

Location

Miami, United States

Founded

2023

Employees

2 employees

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Tiny Models AI

Score: 16/100

AI-Generated Company Overview (experimental) – could contain errors

Executive Summary

tinymodels.ai 10+

Founded 2023 – Miami, United States

Funding

No funding information available. Click "Fetch funding" to run a targeted funding scan.

Team (<5)

Ana Olano

Founder

Company Description

Problem

Many organizations rely on large language models (LLMs) for specialized tasks, which can be computationally expensive, slow, and less accurate than purpose-built solutions. Rule-based systems often fall short when dealing with complex patterns and unstructured data, while scaling human-like perception to handle high volumes is impractical.

Solution

TinyModels.AI develops custom, small-parameter machine learning models tailored for specific industry applications, offering a cost-effective and efficient alternative to LLMs. These models, with fewer than 2 billion parameters, achieve high accuracy (over 97%) on specialized tasks while operating efficiently on consumer-grade hardware. TinyModels.AI delivers these custom models rapidly, enabling organizations to quickly deploy AI solutions that outperform larger, general-purpose AI models in terms of accuracy, speed, and cost. The models can be used for a variety of tasks, including API and function routing, high-precision classification, custom named entity recognition, tailored natural language processing, PDF parsing, and RAG with intelligent chunking.

Features

Custom machine learning models with fewer than 2 billion parameters

High accuracy (over 97%) in domain-specific applications

Rapid development and deployment

Efficient operation on consumer-grade hardware

API & Function Routers: Models that direct requests to the right APIs or functions.

High-Precision Classifiers: Models for categorizing data in any domain.

Custom Named Entity Recognition: Models to extract specific entities from text.

Tailored Natural Language Processing: Bespoke models for understanding and processing text.

PDF Parser: 500 pages/min extraction of structured data from PDF documents.

RAG with Intelligent Chunking: Smart text segmentation and retrieval.

Target Audience

The primary target audience includes organizations across various industries seeking efficient and accurate AI solutions for specialized tasks, particularly those who find LLMs too resource-intensive or inaccurate for their specific needs.