> ## Documentation Index > Fetch the complete documentation index at: https://docs.mavera.io/llms.txt > Use this file to discover all available pages before exploring further. # Run schema on meeting > Run a schema against a meeting's transcript to extract structured data. Uses AI to analyze the transcript and extract field values based on the schema definition. ## OpenAPI ````yaml /openapi.json post /meetings/{id}/schemas/{schemaId}/run openapi: 3.0.3 info: title: Mavera API version: 1.0.0 description: >- # Getting Started The Mavera Responses API provides persona-powered AI responses using the **OpenAI Responses API format**. Use `client.responses.create()` with the OpenAI SDK — just set the base URL to `https://app.mavera.io/api/v1`. ## Authentication All API requests require a Bearer token. Create an API key in **Settings > Developer > API Keys**. ``` Authorization: Bearer mvra_live_your_key_here ``` ## Quick Start ### Step 1: Get a Persona ID Every response request requires a `persona_id`. First, list available personas: ```bash curl https://app.mavera.io/api/v1/personas \ -H "Authorization: Bearer mvra_live_your_key_here" ``` This returns personas you can use. Copy the `id` field from any persona. ### Step 2: Create a Response Use the persona ID in your request: ```bash curl https://app.mavera.io/api/v1/responses \ -H "Authorization: Bearer mvra_live_your_key_here" \ -H "Content-Type: application/json" \ -d '{ "model": "mavera-1", "persona_id": "YOUR_PERSONA_ID", "input": "Hello!" }' ``` ## Rate Limits Per-key sliding window limits based on your subscription tier: | Tier | Requests / min | |------|---------------| | Starter | 60 | | Basic | 120 | | Professional | 240 | | Enterprise | 600 | When rate limited, the response includes a `Retry-After` header indicating how many seconds to wait. ## Credits Each API call consumes credits from your subscription. The `usage.credits_used` field in the response shows the cost of each request. contact: name: Mavera Support url: https://mavera.io x-logo: url: /Mavera_Logo_Full.png altText: Mavera servers: - url: https://app.mavera.io/api/v1 description: Production - url: https://dev.mavera.io/api/v1 description: Development security: - BearerAuth: [] tags: - name: System description: Health checks and operational status. - name: Responses description: >- Generate persona-powered AI responses using the OpenAI Responses API format. Use `client.responses.create()` with the OpenAI SDK. - name: Models description: Discover available models and their capabilities. - name: Personas description: >- Browse available personas to inject specialized intelligence into responses via `persona_id`. - name: Custom Personas description: >- Create, manage, and customize AI-powered personas. Supports three creation pipelines: North Star (AI-generated from minimal input), Intermediate (guided 3-step process), and Advanced (full B2B/B2C customization with psychographics and purpose packs). 300 credits per persona. - name: Brand Voice description: >- Create, manage, and retrieve brand voice profiles for AI-powered content generation. Upload URLs and documents to analyze, and the AI will generate tone guidelines, vocabulary preferences, and writing style recommendations. - name: Generations description: >- Generate AI-powered content using pre-built templates with optional brand voice styling. Browse available generation apps, create content, and manage your generation history. Supports streaming responses. - name: Mave description: >- Mave is Mavera's AI-powered research and analysis agent. Send messages to conduct comprehensive investigations using multiple data sources, personas, and fact-checking. Mave uses a multi-phase orchestration process (Triage, Planning, Research, Execution, Validation) to deliver well-researched responses with sources. - name: Workspaces description: >- Manage workspaces for organizing your work. Workspaces contain projects, threads, personas, and other resources. Invite team members with role-based access control. Set budget alerts and usage limits. - name: Projects description: >- Organize work within workspaces using projects. Projects contain threads, generations, and other resources. Track usage and set per-project budget controls. - name: Meetings description: >- Access meeting recordings, transcripts, and AI-powered analysis. List meetings, retrieve transcripts in multiple formats (segments, text, SRT), get AI analysis with summaries, tasks, decisions, highlights, and coaching metrics. Run custom schemas to extract structured data from transcripts. - name: Schemas description: >- Create and manage meeting schemas for structured data extraction. Schemas define fields to extract from meeting transcripts, with support for various field types (text, enum, list, boolean, etc.), evidence tracking, and scoring. - name: Files description: >- Upload, manage, and retrieve files/assets. Use presigned URLs for direct uploads to avoid passing files through the API. Supports images, videos, documents, and more. File uploads count against your storage quota. - name: Folders description: >- Create and manage folders to organize your files. Folders can be favorited and shared with workspace members. - name: Video Analysis description: >- AI-powered video and advertisement analysis. Submit videos for comprehensive emotional, cognitive, behavioral, and technical analysis. Chat with AI about the results. - name: Focus Groups description: >- AI-powered synthetic focus group research. Create focus groups with personas to gather market research, product feedback, and audience insights. Supports 12 question types including NPS, Likert scales, and open-ended responses. - name: News description: >- AI-powered news intelligence. Browse trending stories, get AI analysis from persona perspectives, and manage scheduled news digests. Story analysis uses credits based on token usage. - name: Usage description: >- Monitor your subscription usage including credits, transcription minutes, storage, and API request metrics. Get real-time statistics on your billing period usage. paths: /meetings/{id}/schemas/{schemaId}/run: post: tags: - Meetings summary: Run schema on meeting description: >- Run a schema against a meeting's transcript to extract structured data. Uses AI to analyze the transcript and extract field values based on the schema definition. operationId: runMeetingSchema parameters: - name: id in: path required: true description: Meeting ID schema: type: string - name: schemaId in: path required: true description: Schema ID to run schema: type: string requestBody: required: false content: application/json: schema: $ref: '#/components/schemas/RunSchemaRequest' responses: '201': description: Schema run result content: application/json: schema: $ref: '#/components/schemas/MeetingSchemaResult' '400': description: Validation error (e.g., transcript not available) content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' '401': description: Authentication error content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' '404': description: Meeting or schema not found content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' '429': description: Rate limit exceeded content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' x-codeSamples: - lang: curl label: cURL source: >- curl -X POST https://app.mavera.io/api/v1/meetings/meeting_123/schemas/schema_456/run \ -H "Authorization: Bearer mvra_live_your_key_here" \ -H "Content-Type: application/json" \ -d '{}' - lang: python label: Python source: |- import requests result = requests.post( "https://app.mavera.io/api/v1/meetings/meeting_123/schemas/schema_456/run", headers={"Authorization": "Bearer mvra_live_your_key_here"} ).json() print(f"Schema: {result['schema_name']}") print(f"Overall score: {result['overall_score']}") for fv in result["field_values"]: print(f"{fv['field_name']}: {fv['value']}") if fv.get("quote"): print(f" Evidence: '{fv['quote']}'") components: schemas: RunSchemaRequest: type: object properties: model: type: string default: gpt-5.4 description: AI model to use for extraction temperature: type: number minimum: 0 maximum: 2 default: 0.2 description: Model temperature (lower = more deterministic) force_new: type: boolean default: false description: Force creating a new result even if one exists MeetingSchemaResult: type: object properties: id: type: string object: type: string enum: - meeting.schema_result meeting_id: type: string schema_id: type: string schema_name: type: string schema_category: $ref: '#/components/schemas/MeetingSchemaCategory' field_values: type: array items: $ref: '#/components/schemas/MeetingSchemaFieldValue' scores: type: object additionalProperties: type: number nullable: true description: Scores per field overall_score: type: number nullable: true description: Average score across all scored fields evidence: type: array items: $ref: '#/components/schemas/MeetingSchemaEvidence' description: Evidence spans from transcript processing_time_ms: type: integer created_at: type: string format: date-time updated_at: type: string format: date-time ErrorResponse: type: object description: >- All error responses follow this format. The `type` field indicates the category of error, and `code` provides a machine-readable error code. properties: error: type: object properties: message: type: string description: A human-readable error message. example: Invalid API key. type: type: string enum: - invalid_request_error - authentication_error - insufficient_credits - not_found - rate_limit_error - api_error description: The category of error. example: authentication_error code: type: string description: A machine-readable error code. example: invalid_api_key param: type: string nullable: true description: The request parameter that caused the error, if applicable. example: null example: error: message: Invalid API key. type: authentication_error code: invalid_api_key param: null MeetingSchemaCategory: type: string enum: - sales_discovery - qualification - objection_competitor - cs_health - product_feedback - custom description: Category of the meeting schema MeetingSchemaFieldValue: type: object properties: field_name: type: string field_label: type: string field_type: $ref: '#/components/schemas/MeetingSchemaFieldType' is_required: type: boolean value: description: Extracted value (type depends on field_type) quote: type: string nullable: true description: Supporting quote from transcript confidence: type: number nullable: true description: Extraction confidence (0-1) score: type: number nullable: true description: Field score if scoring enabled score_rationale: type: string nullable: true description: Explanation of the score MeetingSchemaEvidence: type: object properties: field_name: type: string text: type: string speaker: type: string nullable: true speaker_id: type: string nullable: true timestamp_start: type: number timestamp_end: type: number MeetingSchemaFieldType: type: string enum: - text - long_text - enum - multi_select - number - boolean - list - person - date description: Type of the schema field securitySchemes: BearerAuth: type: http scheme: bearer description: >- API key prefixed with `mvra_live_`. Create keys at **Settings > Developer > API Keys**. ````