Ship AI features with confidence

What you can build with ModelRiver

• Customer-facing AI features – chat assistants, summarisation, content generation, or creative tooling that needs reliable provider failover.
• Operational workflows – internal automation that requires structured outputs, audit trails, and centralised governance.
• Data pipelines – batch jobs that orchestrate different models while capturing rich request metadata for analytics or billing.

Key capabilities at a glance

• Single API surface – call https://api.modelriver.com/v1/ai and route to OpenAI, Anthropic, Google, Cohere, Mistral, or your own custom provider without changing code.
• Dashboard-first management – create projects, rotate provider credentials, generate API keys, design structured outputs, and replay logs.
• Multi-provider resilience – configure fallback providers and models so your workloads continue running when a vendor is rate-limited or offline.
• Transparent observability – every request is logged with latency, usage, cached context, and workflow metadata for instant debugging.

Who should use this platform

• Backend and platform engineers who want a stable abstraction over multiple AI vendors.
• Product managers and operators who need self-serve tooling to configure prompts, workflows, and structured outputs without redeploying code.
• Security and compliance owners who require a single place to audit access, rotate secrets, and manage data retention.

Your first week with ModelRiver

1. Create your workspace – sign up at modelriver.com and create at least one project (for example, Production and Staging).
2. Connect providers – store the API keys for the providers you plan to use. ModelRiver encrypts them and surfaces connection status in the dashboard.
3. Issue API keys – generate project-scoped keys for your applications. Each key can be revoked independently.
4. Design workflows – choose preferred providers, add fallbacks, attach structured outputs, and capture cache fields for the data you want echoed back.
5. Ship an integration – follow the Getting Started guide to make your first request and validate the response in Request Logs.
6. Scale with confidence – monitor usage, add team members, and iterate on workflows directly in the dashboard.

Choose your next step

• Set up your project – account creation, provider connections, and issuing API keys.
• Master the dashboard – deep dive into projects, workflows, logs, and analytics.
• Integrate the API – request/response format, best practices, and sample code.
• Automate with workflows – structured outputs, cache fields, and fallbacks.
• Review policies & security – how we protect keys, data, and accounts.
• Troubleshoot issues – quick fixes for the most common questions.

ModelRiver is here to help you deliver reliable AI experiences faster. If you need a hand, reach us at [email protected].