> ## 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. # Create a focus group > Create a new AI-powered focus group study. Focus groups generate synthetic responses from personas for market research, product feedback, and audience insights. **Pricing**: 1 credit per response (minimum 100 credits). Total cost = personas × sample_size × questions. **Processing**: Analysis is performed asynchronously. Poll `GET /focus-groups/{id}` to check status. ## OpenAPI ````yaml /openapi.json post /focus-groups 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: /focus-groups: post: tags: - Focus Groups summary: Create a focus group description: >- Create a new AI-powered focus group study. Focus groups generate synthetic responses from personas for market research, product feedback, and audience insights. **Pricing**: 1 credit per response (minimum 100 credits). Total cost = personas × sample_size × questions. **Processing**: Analysis is performed asynchronously. Poll `GET /focus-groups/{id}` to check status. operationId: createFocusGroup requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/FocusGroupRequest' responses: '202': description: Focus group created and processing started. content: application/json: schema: $ref: '#/components/schemas/FocusGroupResponse' '400': description: Validation error. content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' '401': description: Authentication error. content: application/json: schema: $ref: '#/components/schemas/ErrorResponse' '402': description: Insufficient credits. 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/focus-groups \ -H "Authorization: Bearer mvra_live_your_key_here" \ -H "Content-Type: application/json" \ -d '{ "name": "Product Feedback Study", "sample_size": 50, "persona_ids": ["persona_id_1", "persona_id_2"], "questions": [ { "question": "How likely are you to recommend this product?", "type": "net_promoter_score" }, { "question": "What features do you value most?", "type": "multiple_choice_multiple", "options": ["Price", "Quality", "Design", "Brand"] } ] }' - lang: python label: Python source: |- import requests headers = {"Authorization": "Bearer mvra_live_your_key_here"} response = requests.post( "https://app.mavera.io/api/v1/focus-groups", headers=headers, json={ "name": "Product Feedback Study", "sample_size": 50, "persona_ids": ["persona_id_1", "persona_id_2"], "questions": [ {"question": "How likely are you to recommend?", "type": "net_promoter_score"}, {"question": "What features matter?", "type": "multiple_choice_multiple", "options": ["Price", "Quality"]} ] } ) focus_group = response.json() print(f"Focus group created: {focus_group['id']}") print(f"Credits charged: {focus_group['credits_charged']}") - lang: javascript label: JavaScript source: >- const response = await fetch("https://app.mavera.io/api/v1/focus-groups", { method: "POST", headers: { "Authorization": "Bearer mvra_live_your_key_here", "Content-Type": "application/json" }, body: JSON.stringify({ name: "Product Feedback Study", sample_size: 50, persona_ids: ["persona_id_1", "persona_id_2"], questions: [ { question: "How likely are you to recommend?", type: "net_promoter_score" }, { question: "What features matter?", type: "multiple_choice_multiple", options: ["Price", "Quality"] } ] }) }); const focusGroup = await response.json(); console.log("Focus group created:", focusGroup.id); components: schemas: FocusGroupRequest: type: object required: - name - sample_size - persona_ids - questions properties: name: type: string minLength: 3 maxLength: 100 description: Name/title for the focus group study. example: Product Feedback Study Q1 2026 sample_size: type: integer minimum: 1 maximum: 500 description: Number of responses to generate per persona per question. example: 50 persona_ids: type: array minItems: 1 maxItems: 20 items: type: string description: >- Array of persona IDs to participate in the focus group. Get persona IDs from GET /personas. example: - clx1abc2d0001abcdef123456 - clx1abc2d0002abcdef789012 questions: type: array minItems: 1 maxItems: 50 description: Array of questions for the focus group. items: $ref: '#/components/schemas/FocusGroupQuestion' FocusGroupResponse: type: object properties: id: type: string description: Unique focus group ID. example: cly2xyz3e0001efghij456789 object: type: string enum: - focus_group example: focus_group status: type: string enum: - pending - generating - completed - failed description: Current processing status. example: pending created_at: type: integer description: Unix timestamp of creation. example: 1706380800 name: type: string example: Product Feedback Study Q1 2026 sample_size: type: integer example: 50 credits_charged: type: integer description: Total credits charged for this focus group. example: 500 personas: type: array items: type: object properties: id: type: string name: type: string questions: type: array items: type: object properties: id: type: string question: type: string type: type: string options: type: array items: type: string order: type: integer 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 FocusGroupQuestion: type: object required: - question - type properties: question: type: string minLength: 3 maxLength: 500 description: The question text. example: How likely are you to recommend this product to a friend? type: type: string enum: - rating - multiple_choice_single - multiple_choice_multiple - open_ended_text - likert_scale - ranking_order - semantic_differential_scale - net_promoter_score - matrix_questions - dichoctomous_questions - slider_scale - attribute_rating_scale description: The type of question. example: net_promoter_score options: type: array items: type: string description: Options for multiple choice, ranking, and dichotomous questions. example: - Very Satisfied - Satisfied - Neutral - Dissatisfied config: type: object description: >- Type-specific configuration. See documentation for each question type. properties: min_value: type: number description: Minimum value for rating/slider. max_value: type: number description: Maximum value for rating/slider. min_label: type: string description: Label for minimum value. max_label: type: string description: Label for maximum value. likert_labels: type: array items: type: string description: Custom Likert scale labels. left_label: type: string description: Left label for semantic differential. right_label: type: string description: Right label for semantic differential. scale_points: type: integer description: Number of scale points. matrix_rows: type: array items: type: string description: Row labels for matrix questions. matrix_columns: type: array items: type: string description: Column labels for matrix questions. attributes: type: array items: type: string description: Attributes for attribute rating. rating_scale: type: integer description: Maximum rating for attribute rating. max_length: type: integer description: Maximum length for open-ended responses. description: type: string description: Optional description or instructions for the question. is_required: type: boolean default: true description: Whether the question is required. securitySchemes: BearerAuth: type: http scheme: bearer description: >- API key prefixed with `mvra_live_`. Create keys at **Settings > Developer > API Keys**. ````