One Platform, Seven AI Providers

The AI model landscape is evolving at a pace that makes vendor lock-in genuinely dangerous. An organization that bets everything on a single provider — training workflows around OpenAI’s API, building prompts for Claude’s specific behavior, or architecting around Gemini’s tool-calling format — faces real business risk when that provider changes pricing, deprecates models, or introduces terms of service that conflict with regulatory requirements. Healthcare organizations face an additional burden: different clinical use cases may require different models. A small, fast local model might be perfect for triage routing, while a larger cloud model may be needed for complex diagnostic reasoning. No single provider is the right answer for every task.

Fortaleza AI’s multi-agent architecture solves this with a provider-agnostic design that supports seven AI providers out of the box: Ollama (local), OpenAI, Anthropic (Claude), Google (Gemini), xAI (Grok), Meta (Llama via cloud), and a configurable Corporate endpoint for custom deployments. All provider and model configurations are managed through a single configuration file that serves as the source of truth for both the backend server and the frontend UI. Adding a new model from any supported provider is a configuration change, not a code change.

The platform ships with multiple built-in agent types, each serving a different operational need. The primary agent runs on local Ollama models for data-sensitive workloads where no information can leave your network. The external agent connects to cloud providers when you need frontier model capabilities for non-sensitive tasks. A dynamic agent, created through our WorkBench visual builder, lets administrators design custom agents with specific provider-model combinations, system prompts, tool access, and behavioral guardrails — all without writing code. The orchestrator routes requests to the correct agent, normalizes responses into a consistent format regardless of the underlying provider, and tracks everything through our observability platform.

The WorkBench agent builder deserves special attention because it’s where non-technical staff can configure AI behavior for their specific use cases. Each agent definition includes 16 tunable LLM parameters (temperature, top_p, repeat_penalty, frequency_penalty, and more), a free-text guardrails field for behavioral rules, helper tool assignments, and conversation history depth settings. These configurations persist to the back end server and take effect immediately — no system restarts required. An administrator can create a “Clinical Documentation Assistant” agent using a local model with strict guardrails and a “Research Summarizer” agent using Claude with more creative parameters, and both are available to appropriate users simultaneously.

For healthcare organizations, provider flexibility is a strategic advantage, not a technical luxury. When a model provider changes their data processing terms, you switch providers with a configuration change instead of a re-architecture. When a new model delivers better performance for radiology report summarization, you deploy it alongside your existing agents without disrupting workflows. And when regulations require that certain data types never leave your premises while others can use cloud inference, you route accordingly through the orchestrator. Fortaleza AI gives you the freedom to use the right model for every job without betting your infrastructure on any single vendor. Learn more at www.fortalezaai.com.