# Saina > Saina is the decision layer for software systems. Saina builds specialist AI decision models and the developer platform around them. Developers use Saina to turn model predictions into versioned decision endpoints, observe how those decisions perform in production, turn failures into test cases, and improve their models or policies without breaking existing behavior. Saina works with open models and external inference providers, so teams can use their own infrastructure. Saina is operated by Rama Labs Inc. Helm is the family of specialist decision models. Saina Studio is the developer platform for building, testing, deploying and improving decisions. Applications supply context, questions and candidate answers; Helm returns structured scores rather than free-form generated answers, and the application controls what happens next. Several questions can share the same context in one request. Typical decisions are routing, gating, assignment, allocation, scheduling, prioritization and recovery. Saina is not a workflow orchestration platform: it makes decisions that existing applications and workflows execute. In decision mode, thresholds and minimum margins determine whether an answer is accepted. Low scores, a tie or an insufficient gap can produce an explicit fallback. The application decides what happens next: ask a person, gather more context or call another model. A probability score is not a guarantee of correctness or evidence of calibration on a particular workflow; evaluate the model and policy on your own held-out examples. ## Models and answer types - **Helm 3** (`saina-helm-3-0.8b`): a native multimodal decision model that reads text, images and audio together. - **Helm 2** (`saina-helm-2-0.8b`): text decisions, including classification and routing. - **Helm 2 Omni** (`saina-helm-2-omni-0.8b`): text, image and audio decisions; remains available alongside Helm 3. - **Helm 2 Routing** (`saina-helm-2-routing-0.8b`): text-based selection of an answering model using the request and candidate capabilities and prices. - **Mechanical specialist**: use `specialist: "mechanical"` with Helm 2 Omni for machine-sound decisions. This specialist accepts text and audio, not images. Supported question types are `single_choice` (mutually exclusive options), `yes_no`, `rating` (an ordered rubric) and `multi_choice` (independent scores for overlapping labels). Multi-choice scores do not need to sum to one. See the API documentation for each type's request and response fields. Helm weights are published under Apache-2.0. Check the selected model card for capabilities and limitations, and pin a checkpoint revision for reproducible self-hosting. Hosted availability is reported by `GET https://api.saina.run/v1/models`; the `saina-helm` alias refers to the model served by the endpoint, rather than a fixed release. ## Saina Studio Studio turns a model into a production decision endpoint and keeps it reliable: a playground for trying models, inputs and questions; versioned decision endpoints served through the API; observability that records decisions, scores, execution traces and reported outcomes; test cases with expected results; regression evaluation that compares versions; promotion of a tested version to production and rollback to an earlier one; and data labeling and fine-tuning that turn observed failures into training data. A decision improves in one of two ways, both through the same tests and release process: change the configuration (questions, options, instructions, thresholds or model selection), or improve the underlying model. Studio is model-agnostic: it works with Helm, compatible third-party models and models a team fine-tunes, and connects to external inference providers or locally hosted models. ## Integration and deployment Use the hosted API at `https://api.saina.run`, or run Helm locally with the Saina SDK or a self-hosted HTTP server. Python and JavaScript clients and an n8n integration are available. CPU and GPU deployment instructions are in the deployment repository; latency and memory depend on the model, runtime and input. Use the current API pages for pricing, account access, limits and data handling. ## Studio MCP Saina Studio exposes an MCP server at `https://api.saina.run/v1/mcp` (HTTP transport). It gives an AI client, such as Claude, Claude Code, Codex or Cursor, access to one account's Studio tools, with the access level chosen when the connection is approved. Clients with sign-in support connect through OAuth: add the URL as a custom connector, sign in to Saina Studio, choose Read only or Read and write, and approve. No key is copied, access renews for up to 90 days, and the account owner can revoke a connection from the Studio settings. Clients without sign-in send an API key with the `mcp` scope in the `Authorization: Bearer` header. For example, `claude mcp add --transport http saina https://api.saina.run/v1/mcp` connects Claude Code with sign-in. Per-client setup and troubleshooting are at `https://saina.run/studio/#mcp`; the tool groups, workflows and rules for agents are in the agent skill at `https://saina.run/skill.md`. ## Product and documentation - [Saina Helm](https://saina.run/): Model family, benchmarks and deployment options. - [Decision models](https://saina.run/decision-model/): What a decision model does and when to use one. - [Hosted API](https://saina.run/api/): Hosted service overview. - [API documentation](https://saina.run/api/docs/): Request formats and integration guidance. - [Playground](https://saina.run/studio/): Try model inputs and inspect scores and decisions. - [Agent skill](https://saina.run/skill.md): Connect to Saina Studio over MCP, the tool groups, common workflows and rules for agents. - [Studio MCP](https://saina.run/studio/): Endpoint, sign-in and API-key setup for MCP clients (Studio, Settings, MCP). - [Saina SDK](https://github.com/run-saina/saina): Python and JavaScript clients, decision contract and optional local inference. - [Self-hosting](https://github.com/run-saina/deploy): CPU and GPU deployment instructions and container images. - [n8n integration](https://github.com/run-saina/n8n-nodes): Workflow integration. - [Model weights](https://huggingface.co/run-saina): Published Saina models and model cards. - [Decision Index](https://github.com/run-saina/saina-decision-index): Evaluation harness and methodology. ## Use cases - [Use cases](https://saina.run/use-cases/): Workflow examples. - [Support ticket triage](https://saina.run/use-cases/support-ticket-triage/): Classify and route tickets. - [Email inbox routing](https://saina.run/use-cases/email-inbox-routing/): Route incoming messages. - [LLM routing](https://saina.run/use-cases/llm-routing/): Select models and gate actions. - [Document and receipt checks](https://saina.run/use-cases/document-receipt-checks/): Compare documents with claims. - [Machine fault detection](https://saina.run/use-cases/machine-fault-detection/): Classify equipment sounds. ## Optional - [Blog](https://saina.run/blog/): Product announcements and integration articles. - [Sitemap](https://saina.run/sitemap.xml): Public page inventory. - [Privacy](https://saina.run/privacy/): Privacy policy. - [Terms](https://saina.run/terms/): Terms of service.