> ## Documentation Index > Fetch the complete documentation index at: https://docs.nugen.in/llms.txt > Use this file to discover all available pages before exploring further. # Set a target score and let your model reach it autonomously > Submit a target alignment score and let the system plan the run. The system analyses your data, base model and prior training history to build a multi-phase alignment plan. Phases are sequenced and executed continuously. The system may apply gradient-free representation adjustments, supervised learning, reward-guided optimisation, or a combination, re-planning when the score trajectory demands it. Checkpoints are evaluated between phases, and the system revisits earlier phases if regressions surface. Every parameter defaults to `auto`. You can override any of them to constrain the search, and the system performs best when given room to explore. **Path Parameters:** - `alignment_id`: The alignment project to auto-align. Its base model, data and prior training history warm-start the planning. **Request Body:** - `target_score` (required): Target quality on a 0-99 scale - `benchmark_id` (optional): Benchmark that measures the achieved score against the target. Defaults to the benchmark already on the project - `method`, `representations`, `sequencing` (optional): Constrain which optimisation families run and in what order - `max_compute_hours` (optional): Upper bound on GPU hours across all phases - `notify_on_phase_transition` (optional): Notify on each phase change - `config` (optional): Per-phase overrides for dataset, rendering, sampling, reward, advantage, loss, adapter, optimizer, training, reference policy, environment, checkpointing, evaluation and logging **Returns:** - `auto_align_id`: Identifier of this request - `alignment_id`: The project it targets - `status`, `message` **Raises:** - `404`: If the alignment project is not found or does not belong to you **Notes:** - Auto-alignment is an Enterprise Edition capability. The request is recorded and our team follows up; raise a support ticket to enable it - Once enabled, poll `GET /alignment-projects/{alignment_id}/status` for progress, phase transitions and intermediate scores ## OpenAPI ````yaml https://api.nugen.in/openapi-public.json post /api/v3/alignment-projects/{alignment_id}/auto-align openapi: 3.1.0 info: title: Nugen Intelligence API description: >+ Nugen Intelligence: Powering Specialized Intelligence At Scale. **Need an API key?** Sign up, log in to the platform and generate an API key. **Need help?** Sign up, log in to the platform and raise a support ticket. **What this API does.** Align models to your domain corpus and run them with confidence. Train-time alignment builds domain-aligned adaptors through proprietary methods spanning pre-training, supervised and reinforcement stages. Inference-time alignment quantifies uncertainty across the full generation trajectory, producing confidence scores and keeping every response on domain. **Workflow.** Upload documents, create or generate a benchmark, create an alignment project, receive an aligned model id. That one id serves chat completions, completions, embeddings and reranking, and its embeddings carry your domain's representation space, making retrieval domain aware. You define the success criteria for your domain; the platform handles the deep learning, the GPUs and the data preparation. **Authentication.** Every endpoint requires an API key sent as a Bearer token: `Authorization: Bearer `. contact: name: Nugen Intelligence - Customer Support url: https://nugen.in/signup email: support@nugen.in version: 25.4.20 servers: - url: https://api.nugen.in description: Production security: [] paths: /api/v3/alignment-projects/{alignment_id}/auto-align: post: tags: - General summary: Set a target score and let your model reach it autonomously description: >- Submit a target alignment score and let the system plan the run. The system analyses your data, base model and prior training history to build a multi-phase alignment plan. Phases are sequenced and executed continuously. The system may apply gradient-free representation adjustments, supervised learning, reward-guided optimisation, or a combination, re-planning when the score trajectory demands it. Checkpoints are evaluated between phases, and the system revisits earlier phases if regressions surface. Every parameter defaults to `auto`. You can override any of them to constrain the search, and the system performs best when given room to explore. **Path Parameters:** - `alignment_id`: The alignment project to auto-align. Its base model, data and prior training history warm-start the planning. **Request Body:** - `target_score` (required): Target quality on a 0-99 scale - `benchmark_id` (optional): Benchmark that measures the achieved score against the target. Defaults to the benchmark already on the project - `method`, `representations`, `sequencing` (optional): Constrain which optimisation families run and in what order - `max_compute_hours` (optional): Upper bound on GPU hours across all phases - `notify_on_phase_transition` (optional): Notify on each phase change - `config` (optional): Per-phase overrides for dataset, rendering, sampling, reward, advantage, loss, adapter, optimizer, training, reference policy, environment, checkpointing, evaluation and logging **Returns:** - `auto_align_id`: Identifier of this request - `alignment_id`: The project it targets - `status`, `message` **Raises:** - `404`: If the alignment project is not found or does not belong to you **Notes:** - Auto-alignment is an Enterprise Edition capability. The request is recorded and our team follows up; raise a support ticket to enable it - Once enabled, poll `GET /alignment-projects/{alignment_id}/status` for progress, phase transitions and intermediate scores operationId: create_auto_alignment parameters: - name: alignment_id in: path required: true schema: type: string title: Alignment Id requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/AutoAlignRequest' responses: '202': description: Auto-alignment request accepted and recorded. content: application/json: schema: $ref: '#/components/schemas/AutoAlignResponse' '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' security: - HTTPBearer: [] components: schemas: AutoAlignRequest: properties: target_score: type: number maximum: 99 minimum: 0 title: Target Score description: >- Target alignment quality on a 0-99 scale. The system iterates across phases until this score is reached or the compute budget is consumed. Targets above 85 typically cause the system to explore multiple optimisation strategies in sequence. examples: - 88 benchmark_id: anyOf: - type: string - type: 'null' title: Benchmark Id description: >- Benchmark used to measure the achieved score against the target. Defaults to the benchmark already on the alignment project. method: anyOf: - type: string - type: 'null' title: Method description: >- Which optimisation families the system may use. On auto it selects and sequences them from the score trajectory, and may begin with one approach, move to another, and revisit earlier ones. default: auto representations: anyOf: - type: string - type: 'null' title: Representations description: >- How internal model representations are handled before and during alignment. On auto the system profiles the base model and picks a strategy before any gradient-based phase begins. default: auto sequencing: anyOf: - items: type: string type: array - type: 'null' title: Sequencing description: >- Explicit phase ordering. When null the system orders phases itself and may interleave or revisit them. When given, it follows the order and still controls the hyperparameters within each phase. max_compute_hours: anyOf: - type: number - type: 'null' title: Max Compute Hours description: >- Upper bound on GPU hours across all phases, allocated by expected marginal score gain. When null the run continues until the target. notify_on_phase_transition: anyOf: - type: boolean - type: 'null' title: Notify On Phase Transition description: Notify when the system moves between alignment phases. default: true config: anyOf: - additionalProperties: true type: object - type: 'null' title: Config description: >- Optional overrides for any phase of the run: dataset, rendering, sampling, reward, advantage, loss, adapter, optimizer, training, reference policy, environment, checkpointing, evaluation and logging. Anything omitted resolves to auto. type: object required: - target_score title: AutoAlignRequest description: |- Target a score and let the system plan the alignment to reach it. Every field except target_score defaults to auto. The deep configuration blocks are accepted as given and stored verbatim, so a run can pin any detail without the public schema enumerating every knob. AutoAlignResponse: properties: auto_align_id: type: string title: Auto Align Id description: Identifier of the auto-alignment request. alignment_id: type: string title: Alignment Id description: Alignment project the request targets. status: type: string title: Status description: Request status. message: type: string title: Message description: Human-readable outcome of the request. type: object required: - auto_align_id - alignment_id - status - message title: AutoAlignResponse description: Acknowledgement of an auto-alignment request. HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' type: array title: Detail type: object title: HTTPValidationError ValidationError: properties: loc: items: anyOf: - type: string - type: integer type: array title: Location msg: type: string title: Message type: type: string title: Error Type type: object required: - loc - msg - type title: ValidationError securitySchemes: HTTPBearer: type: http scheme: bearer ````