Trainkore — Unified AI Prompting, Model Management and Observability

Trainkore is a unified platform for building, evaluating, and deploying prompts across multiple AI models. It combines auto prompt generation, model switching, cost optimization, versioning, and observability into a single workflow-friendly environment. It works with OpenAI and other providers out of the box and integrates natively with popular tooling like Langchain and LlamaIndex.

Note: The platform includes demo/video content placeholders and showcases coming soon features. It is designed to help teams iterate prompts, compare model behavior, and optimize costs across models and use cases.

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How It Works

1. Connect models and providers. Trainkore supports OpenAI, Gemini, Coherence, Anthropic, Azure, and more, plus your own models. It can be used natively with Langchain, LlamaIndex, and other ecosystems.
2. Prompt Versioning. Manage, compare, and roll back prompts across different model versions to ensure reproducibility.
3. Auto Prompt Generation. Generate effective prompts automatically for any supported model, accelerating experimentation.
4. Model Switching. Automatically select the best model for a given prompt based on runtime signals, prompts, and metadata.
5. Observability. Get insights from key metrics, detailed logs, and structured input/output data to debug and optimize prompts and models.
6. Playground & Iteration. Use the best-in-class playground to iterate on prompts across your organization, with support for batch evaluations and performance analysis.
7. Usage & Cost Awareness. Monitor usage and optimize cost by selecting the most cost-effective models automatically.

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Features Overview

• Auto Prompt Generation for any model
• Model Switching to the best model based on prompts and usage
• Observability with input/output, prompts, metadata, and performance metrics
• Prompt Versioning across OpenAI, Gemini, Coherence, Anthropic, Azure, and more
• Integration with Langchain, LlamaIndex, and external tools
• Interactive Playground for enterprise-grade prompt management
• Unified platform for standard LLMs and your own models
• Cost optimization: cost-aware model selection and usage
• Iterative Logs and Performance Analysis (input, output, rating, prompts, metadata, etc.)
• Natively supports a wide range of providers and models

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Use Cases

• Prompt engineering and experimentation across multiple models
• Comparing model responses for consistency, bias, and quality
• Scalable prompt management for large organizations
• Cost-aware routing to the most economical model per task
• Auditable prompt history with versioned deployments
• Integrations with existing AI stacks (Langchain, LlamaIndex, etc.)

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Safety and Compliance

• Centralized visibility helps enforce governance, auditing, and compliance for prompts and model usage.
• Use recommended governance practices when deploying prompts to production.

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Core Features

• Auto Prompt Generation for any model
• Model Switching to the best model automatically based on prompts and usage
• Observability Suite with metrics, logs, and detailed debugging
• Prompt Versioning across multiple model providers
• Built-in Playground for rapid experimentation
• Natively integrated with Langchain, LlamaIndex and more
• Supports standard LLMs and user-provided models
• Cost optimization: reduced prompt-generation and model-switching costs (targeting efficiency)