Ango Hub
Ango Hub is a quality-focused, enterprise-ready data annotation platform for AI teams, available on cloud and on-premise. It supports computer vision, medical imaging, NLP, audio, video, and 3D point cloud annotation, powering use cases from autonomous driving and robotics to healthcare AI.
Built for AI fine-tuning, RLHF, LLM evaluation, and human-in-the-loop workflows, Ango Hub boosts throughput with automation, model-assisted pre-labeling, and customizable QA while maintaining accuracy. Features include centralized instructions, review pipelines, issue tracking, and consensus across up to 30 annotators. With nearly twenty labeling tools—such as rotated bounding boxes, label relations, nested conditional questions, and table-based labeling—it supports both simple and complex projects. It also enables annotation pipelines for chain-of-thought reasoning and next-gen LLM training and enterprise-grade security with HIPAA compliance, SOC 2 certification, and role-based access controls.
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Klu
Klu.ai is a Generative AI platform that simplifies the process of designing, deploying, and optimizing AI applications. Klu integrates with your preferred Large Language Models, incorporating data from varied sources, giving your applications unique context.
Klu accelerates building applications using language models like Anthropic Claude, Azure OpenAI, GPT-4, and over 15 other models, allowing rapid prompt/model experimentation, data gathering and user feedback, and model fine-tuning while cost-effectively optimizing performance. Ship prompt generations, chat experiences, workflows, and autonomous workers in minutes. Klu provides SDKs and an API-first approach for all capabilities to enable developer productivity.
Klu automatically provides abstractions for common LLM/GenAI use cases, including: LLM connectors, vector storage and retrieval, prompt templates, observability, and evaluation/testing tooling.
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Entry Point AI
Entry Point AI is the modern AI optimization platform for proprietary and open source language models. Manage prompts, fine-tunes, and evals all in one place. When you reach the limits of prompt engineering, it’s time to fine-tune a model, and we make it easy. Fine-tuning is showing a model how to behave, not telling. It works together with prompt engineering and retrieval-augmented generation (RAG) to leverage the full potential of AI models. Fine-tuning can help you to get better quality from your prompts. Think of it like an upgrade to few-shot learning that bakes the examples into the model itself. For simpler tasks, you can train a lighter model to perform at or above the level of a higher-quality model, greatly reducing latency and cost. Train your model not to respond in certain ways to users, for safety, to protect your brand, and to get the formatting right. Cover edge cases and steer model behavior by adding examples to your dataset.
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Airtrain
Query and compare a large selection of open-source and proprietary models at once. Replace costly APIs with cheap custom AI models. Customize foundational models on your private data to adapt them to your particular use case. Small fine-tuned models can perform on par with GPT-4 and are up to 90% cheaper. Airtrain’s LLM-assisted scoring simplifies model grading using your task descriptions. Serve your custom models from the Airtrain API in the cloud or within your secure infrastructure. Evaluate and compare open-source and proprietary models across your entire dataset with custom properties. Airtrain’s powerful AI evaluators let you score models along arbitrary properties for a fully customized evaluation. Find out what model generates outputs compliant with the JSON schema required by your agents and applications. Your dataset gets scored across models with standalone metrics such as length, compression, coverage.
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