Tuning Engines

Tuning Engines is a unified AI control and governance layer developed by CerebrixOS, designed for teams building production intelligence across models, agents, tools, and fine-tuned systems. It functions as a comprehensive platform that brings together the full AI lifecycle in one governed environment, enabling organizations to move beyond isolated AI experiments into a secure, observable, cost-aware, and extensible AI operating layer. The platform supports inference, model routing, fallback policies, fine-tuning jobs, datasets, evaluations, model imports and exports, custom models, agents, MCP servers, reusable skills, guardrails, AGT YAML policies, data capture, runtime traces, usage analytics, API keys, billing, team roles, and integrations. Developers benefit from OpenAI-compatible APIs, Anthropic-compatible routes, CLI workflows, MCP access, coding-agent integrations, and resource catalogs for models, agents, tools, and skills. Teams can connect Claude Code, OpenCode, Aider, Cline, Roo, Continue.dev, Cursor, VS Code, Windsurf, and other AI workflows through a single governed platform. Admins gain production controls including role-based access, per-key budgets, rate limits, routing profiles, fallback rules, guardrails, policy-as-code, credential sources, auditability, usage traces, billing controls, tenant isolation, and team management. Tuning Engines is backed by Google Cloud for Startups, NVIDIA Inception, Rogers Cybercatalyst, ElevenLabs Grants, AWS Activate, and BDC Capital, reflecting strong institutional support for its mission to provide a universal intelligence runtime that secures, governs, and optimizes every AI interaction.

Unified Inference

Tuning Engines provides a single OpenAI-compatible endpoint that gives developers access to over 100 models, including open weight models, commercial frontier models, and custom fine-tuned variants. This unified API eliminates the need for multiple integrations or code rewrites, as existing SDKs can point to the Tuning Engines endpoint with a simple base URL change. The platform supports streaming, structured output, and centralized policy enforcement across every request, ensuring consistency and control regardless of the underlying model being called.

Model Tuning and Lifecycle Management

The platform enables teams to adapt open models to their specific data, workflows, and production goals through supervised fine-tuning and LoRA adapters. Tuning Engines manages the full model lifecycle from build to tune to scale, allowing organizations to run supervised fine-tuning jobs, evaluate quality through evaluation gates, and host custom models without managing GPU infrastructure. This feature ensures that model quality evolves with business requirements while maintaining operational simplicity.

Policy and Governance Controls

Tuning Engines delivers comprehensive administrative controls for production environments, including role-based access, per-key budgets, rate limits, routing profiles, fallback rules, guardrails, and policy-as-code through AGT YAML policies. The platform provides full auditability with runtime traces and usage analytics, enabling organizations to enforce compliance, manage token economics with cost ceilings and quotas, and maintain tenant isolation across teams. These controls ensure that AI operations remain secure, observable, and cost-predictable.

Developer Tooling and Integrations

Developers receive OpenAI-compatible APIs, Anthropic-compatible routes, CLI workflows, MCP access, and coding-agent integrations that connect seamlessly with popular development tools. Tuning Engines supports integrations with Claude Code, OpenCode, Aider, Cline, Roo, Continue.dev, Cursor, VS Code, Windsurf, and other AI workflows through a single governed platform. Resource catalogs for models, agents, tools, and skills further streamline development by providing discoverable, reusable components.

Code Assistance

Tuning Engines powers IDE copilots, code generation tools, refactoring agents, and debugging workflows by providing a unified inference endpoint that supports multiple models. Development teams can integrate the platform with VS Code, Cursor, Windsurf, and other IDEs to deliver real-time code suggestions, automated refactoring, and intelligent debugging assistance. The centralized governance layer ensures that all code assistance interactions remain auditable and compliant with organizational policies.

Conversational AI

Organizations deploy customer support bots, internal helpdesks, and multilingual chat systems using Tuning Engines unified API. The platform enables teams to route conversations across multiple models, apply guardrails for content safety, and manage token costs through per-key budgets and rate limits. Runtime traces and usage analytics provide visibility into conversation quality and operational efficiency, supporting continuous improvement of conversational AI deployments.

Agentic Systems

Tuning Engines supports multi-step reasoning, planning, and tool-using execution pipelines by providing a governed runtime for AI agents. Teams can build agents that leverage MCP servers, reusable skills, and fallback policies to handle complex workflows reliably. The platforms policy-as-code capabilities ensure that agent actions remain within defined boundaries, while audit trails capture every step for compliance and debugging purposes.

Enterprise RAG

Secure, scalable retrieval over knowledge bases and private documents is enabled through Tuning Engines unified inference and governance capabilities. Organizations can deploy enterprise RAG systems that use embedding models, language models, and custom fine-tuned models accessed through the same OpenAI-compatible endpoint. Centralized policy controls ensure that sensitive data remains protected, while token economics features manage the costs associated with large-scale retrieval and generation workloads.

What models are available through Tuning Engines?

Tuning Engines provides access to over 100 models through a single OpenAI-compatible endpoint. The model library includes popular open weight models such as Llama 3.3 70B, Llama 3.1 8B, DeepSeek V3, DeepSeek R1, Qwen 2.5 72B, Qwen 2.5 Coder 32B, Mistral Small 3, Mixtral 8x7B, Gemma 2 27B, Llama 3.2 Vision, Whisper Large v3, and embeddings from the BGE and E5 families. Additionally, the platform supports commercial frontier models and any custom models fine-tuned through the platform itself.

How does Tuning Engines handle AI governance and compliance?

Tuning Engines provides comprehensive governance controls including role-based access, per-key budgets, rate limits, routing profiles, fallback rules, guardrails, and policy-as-code through AGT YAML policies. The platform offers full auditability with runtime traces, usage analytics, and credential source management. Administrators can enforce tenant isolation, manage team roles, and maintain detailed records of all AI interactions for compliance purposes.

Can I fine-tune models using my own data?

Yes, Tuning Engines supports model tuning through supervised fine-tuning and LoRA adapters. Teams can adapt open models to their specific data, language, and tasks, then evaluate quality through evaluation gates before deploying. The platform manages the underlying GPU infrastructure, allowing organizations to focus on data preparation and model optimization rather than hardware management.

What integrations does Tuning Engines support?

Tuning Engines integrates with a wide range of development tools and AI workflows. The platform supports Claude Code, OpenCode, Aider, Cline, Roo, Continue.dev, Cursor, VS Code, Windsurf, and other coding agents through MCP access and CLI workflows. Developers can use OpenAI-compatible SDKs with any programming language, and the platform provides resource catalogs for models, agents, tools, and skills that can be reused across projects.