GPT Trainer3
Overview
GPT-trainer is a versatile AI framework that supports the rapid creation, testing, and deployment of powerful voice-enabled AI agents. With CPaaS capabilities built-in, GPT-trainer enables you to design phone-based conversational experiences in addition to text-based ones through the web widget, SMS, Messenger, Instagram, and WhatsApp. The framework also supports direct integrations with external tools and databases via general purpose API, webhooks, function-calling, and connectors inside Zapier, Make, and n8n to enable advanced workflow automation.GPT-trainer has proven use cases in customer support, lead qualification, content creation, employee training, HR/IT administrative assistance, teaching assistance, document analysis, and data standardization.
GPT-trainer is equipped with powerful document extraction capabilities that can handle a variety of file types including Web pages, PDFs, PPT, Word, Excel, Markdown, and even embedded tables and images inside documents. Our state-of-the-art data ingestion pipeline enables best-in-class RAG accuracy. GPT-trainer supports popular LLMs natively from OpenAI, Anthropic, Google, Mistral, and DeepSeek.
GPT-trainer operates under a multi-agent architecture orchestrated by an AI supervisor. User-facing agents rely on intent-based routing to enable domain specialization while background-agents monitor the user interaction in real-time to extract key information, assign labels, and alert human operators when appropriate.
In addition to our subscription-based software, GPT-trainer also offers custom development services for clients looking to build custom middleware or applications. For enterprise deployments, managed or on-prem hosting is available. Fine tuning and custom LLM connectors are also supported.
GPT-trainer is SOC II, ISO 27001, and GDPR compliant.
Contact our team at [email protected] to learn more about full white-label and commercial resale licenses.
Supported features
Releases
- New AI Voice Agents!
- AI phone call support (inbound + outbound)
- Batch outbound calling
- Automatic follow-ups during outbound campaigns
RAG & Data Processing
- Improved knowledge indexing and document OCR support
- Dynamic chunk size support
- Retrieval bias mitigation
- Updates to existing language models for larger context and improved performance (up to 2 million tokens context window for select LLMs)
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