Brand Voice → Full Content Library

Scenario

You have a website and a product launch coming up. You need a blog post, a LinkedIn ad, an email sequence, social captions, a product description, and ad copy — all in the same voice. Normally you’d brief a writer, wait days, review for tone consistency, and iterate. This playbook creates a brand voice from your existing website pages, then fires every relevant Generate app against that voice. In one script run you get a full content library — every piece on-brand by construction.

Mavera-only. No external API, no CMS integration, no third-party auth. Just Brand Voice + Generate + the Mavera API.

Architecture

What You Need

Requirement	Details
Mavera API key	Starts with `mvra_live_`. Get one at Developer Settings.
Workspace ID	From your dashboard URL (`ws_...`).
2–5 website URLs	Public pages that represent your brand voice (about page, blog, homepage).
Product brief	Topic, audience, and key points for the content you want to generate.
Credits	~200–400 total. See Credits Estimate.
Python 3.8+ or Node.js 18+	`requests` for Python; native `fetch` for Node.

MAVERA_API_KEY=mvra_live_your_key_here
MAVERA_WORKSPACE_ID=ws_your_workspace_id

The Flow

Create brand voice from website URLs

POST /brand-voices with 2–5 public URLs. Mavera scrapes them and extracts tone, vocabulary, sentence style, and formality. Poll until status is READY.

Discover available generation apps

GET /generation-apps returns every template with its app_id and required input_data fields. Pick the apps that match your content plan.

Generate content for each app

Loop through your chosen apps. Every POST /generations call passes the same brand_voice_id, ensuring consistent voice across blog posts, emails, ads, and social content.

Collect and review

Each generation returns output (text) and usage.credits_used. Collect everything into a content library — ready for review, editing, and publishing.

Stage 1 — Create Brand Voice from URLs

import os
import time
import json
import requests

API_KEY = os.environ["MAVERA_API_KEY"]
WORKSPACE_ID = os.environ["MAVERA_WORKSPACE_ID"]
BASE = "https://app.mavera.io/api/v1"
HEADERS = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json",
}

BRAND_URLS = [
    "https://yourbrand.com/about",
    "https://yourbrand.com/blog/why-we-exist",
    "https://yourbrand.com/blog/our-approach",
]

bv_resp = requests.post(
    f"{BASE}/brand-voices",
    headers=HEADERS,
    json={
        "label": "Launch Voice — Website",
        "usage_context": "Product launch content across blog, email, social, and ads",
        "urls": BRAND_URLS,
        "workspace_id": WORKSPACE_ID,
    },
)
bv_resp.raise_for_status()
brand_voice = bv_resp.json()
voice_id = brand_voice["id"]
print(f"Brand voice created: {voice_id} (status: {brand_voice['status']})")


def wait_for_voice(bv_id, max_wait=300):
    """Poll until brand voice is READY."""
    for _ in range(max_wait // 10):
        resp = requests.get(f"{BASE}/brand-voices/{bv_id}", headers=HEADERS)
        data = resp.json()
        if data.get("status") == "READY":
            return data
        time.sleep(10)
    raise TimeoutError(f"Brand voice {bv_id} not ready after {max_wait}s")


voice = wait_for_voice(voice_id)
print(f"Voice ready: {voice.get('voice_summary', '')[:120]}...")
print(f"Tone: {voice.get('tone_adjectives', [])}")

Stage 2 — Discover Generation Apps

apps_resp = requests.get(f"{BASE}/generation-apps", headers=HEADERS)
apps_resp.raise_for_status()
all_apps = apps_resp.json()["data"]

for app in all_apps:
    print(f"  {app['id']:35s}  {app.get('name', '')}")

CONTENT_PLAN = [
    {
        "app_id": "blog_post_generator",
        "title": "Launch Blog Post",
        "input_data": {
            "topic": "Introducing Our New Product Line",
            "target_audience": "Small business owners looking for workflow automation",
            "tone": "professional yet approachable",
            "length": "1200 words",
            "key_points": [
                "What the product does",
                "Three key features",
                "Customer pain points it solves",
                "Getting started steps",
            ],
        },
    },
    {
        "app_id": "linkedin_ad_generator",
        "title": "Launch LinkedIn Ad",
        "input_data": {
            "product": "Workflow Automation Suite",
            "target_audience": "Operations managers at 50-500 person companies",
            "value_proposition": "Cut manual processes by 60%",
            "cta": "Start free trial",
        },
    },
    {
        "app_id": "email_sequence_generator",
        "title": "Launch Email Nurture",
        "input_data": {
            "sequence_goal": "Convert free trial signups to paid customers",
            "emails_count": 5,
            "target_audience": "Trial users who signed up in the last 7 days",
            "key_points": [
                "Welcome and quick-start guide",
                "Feature highlight: automation builder",
                "Customer success story",
                "Pricing and upgrade benefits",
                "Last chance / trial ending",
            ],
        },
    },
    {
        "app_id": "social_post_generator",
        "title": "Launch Social Captions",
        "input_data": {
            "topic": "Product launch announcement",
            "platform": "Instagram",
            "tone": "exciting, conversational",
            "count": 5,
        },
    },
    {
        "app_id": "product_description",
        "title": "Product Page Copy",
        "input_data": {
            "product_name": "Workflow Automation Suite",
            "features": [
                "Drag-and-drop automation builder",
                "200+ integrations",
                "Real-time analytics dashboard",
            ],
            "target_audience": "Small business owners",
            "tone": "clear, benefit-focused",
        },
    },
    {
        "app_id": "ad_copy_generator",
        "title": "Launch Ad Copy Variants",
        "input_data": {
            "product": "Workflow Automation Suite",
            "audience": "SMB operations teams",
            "goal": "Drive demo signups",
            "variants": 3,
        },
    },
]

Stage 3 — Generate the Full Library

Every generation passes the same brand_voice_id. The voice rules constrain tone, vocabulary, and sentence style across all output — blog posts read like your website, emails read like your blog, ads read like your emails.

def generate_content(app_id, title, input_data, brand_voice_id):
    """Run one generation app with the shared brand voice."""
    resp = requests.post(
        f"{BASE}/generations",
        headers=HEADERS,
        json={
            "app_id": app_id,
            "title": title,
            "brand_voice_id": brand_voice_id,
            "input_data": input_data,
            "workspace_id": WORKSPACE_ID,
        },
    )
    resp.raise_for_status()
    gen = resp.json()

    if gen.get("status") in ("PENDING", "RUNNING"):
        gen = wait_for_generation(gen["id"])

    return gen


def wait_for_generation(gen_id, max_wait=300):
    """Poll until generation completes."""
    for _ in range(max_wait // 10):
        resp = requests.get(f"{BASE}/generations/{gen_id}", headers=HEADERS)
        data = resp.json()
        if data.get("status") == "COMPLETED":
            return data
        time.sleep(10)
    raise TimeoutError(f"Generation {gen_id} timed out")


library = []
total_credits = 0

for item in CONTENT_PLAN:
    print(f"Generating: {item['title']} ({item['app_id']})...")
    gen = generate_content(
        app_id=item["app_id"],
        title=item["title"],
        input_data=item["input_data"],
        brand_voice_id=voice_id,
    )
    credits = gen.get("usage", {}).get("credits_used", 0)
    total_credits += credits

    library.append({
        "title": item["title"],
        "app": item["app_id"],
        "output": gen.get("output", ""),
        "credits": credits,
    })
    print(f"  ✓ {credits} credits | {len(gen.get('output', ''))} chars")

print(f"\n{'='*50}")
print(f"Library complete: {len(library)} pieces | {total_credits} total credits")

Variations

Multiple brand voices for audience segments

Create separate brand voices for different audiences (e.g. enterprise vs SMB), then run the same content plan against each voice. Compare outputs side by side:

voices = {
    "enterprise": create_voice(["https://yourbrand.com/enterprise"]),
    "smb": create_voice(["https://yourbrand.com/small-business"]),
}
for segment, vid in voices.items():
    for item in CONTENT_PLAN:
        gen = generate_content(item["app_id"], f"{segment} — {item['title']}", item["input_data"], vid)

Incremental library updates

Store the voice ID and re-run only new apps when new templates become available:

existing_apps = {p["app"] for p in manifest["pieces"]}
new_apps = [a for a in all_apps if a["id"] not in existing_apps]
for app in new_apps:
    gen = generate_content(app["id"], app["name"], default_input, voice_id)
    library.append(gen)

Voice consistency scoring

After generating the library, score each piece against the voice rules using Chat with response_format. Flag any pieces that drift:

from openai import OpenAI
mavera = OpenAI(api_key=API_KEY, base_url=BASE)

for piece in library:
    resp = mavera.responses.create(
        model="mavera-1",
        input=[
            {"role": "system", "content": f"Score brand voice consistency 1-10:\n{json.dumps(voice.get('tone_adjectives', []))}"},
            {"role": "user", "content": piece["output"][:2000]},
        ],
    )
    print(f"{piece['title']}: {resp.output[0].content[0].text}")

Parallel generation for speed

Each generation call is independent — use asyncio.gather (Python) or Promise.all (JS) to run all 6 apps concurrently instead of sequentially.

Credits Estimate

Operation	Typical Cost	Notes
Brand voice creation	50 credits	One-time per voice profile
Blog post generation	15–30 credits	Depends on length (500–1500 words)
LinkedIn ad generation	10–20 credits	Short-form
Email sequence (5 emails)	20–40 credits	Multi-part output
Social captions (5 posts)	10–20 credits	Short-form batch
Product description	10–25 credits	Medium-form
Ad copy variants (3)	10–20 credits	Short-form batch
Total (voice + 6 apps)	~125–205 credits

The brand voice is the most durable investment — create once, reuse across hundreds of generations. The 50-credit extraction pays for itself after 2–3 content runs.

What’s Next

Content Repurposing Pipeline

Turn one blog post into 5+ content formats

A/B Copy Production

Same brief, different voices — ready-to-test variants

Brand Voice Extraction & Audit

Score content consistency against extracted voice rules

Content Generation

Full API reference for generation apps

Brand Voice

Create and manage brand voice profiles

Credits & Budget

Usage tracking and cost estimation

Overview

Persona Research

Content Production

Video Intelligence

Strategic Research

Fundamentals

Brand Voice → Full Content Library

Scenario

Architecture

What You Need

The Flow

Stage 1 — Create Brand Voice from URLs

Stage 2 — Discover Generation Apps

Stage 3 — Generate the Full Library

Variations

Credits Estimate

What’s Next

Content Repurposing Pipeline

A/B Copy Production

Brand Voice Extraction & Audit

Content Generation

Brand Voice

Credits & Budget

Overview

Persona Research

Content Production

Video Intelligence

Strategic Research

Fundamentals

Documentation Index

​Scenario

​Architecture

​What You Need

​The Flow

​Stage 1 — Create Brand Voice from URLs

​Stage 2 — Discover Generation Apps

​Stage 3 — Generate the Full Library

​Variations

​Credits Estimate

​What’s Next

Content Repurposing Pipeline

A/B Copy Production

Brand Voice Extraction & Audit

Content Generation

Brand Voice

Credits & Budget

Scenario

Architecture

What You Need

The Flow

Stage 1 — Create Brand Voice from URLs

Stage 2 — Discover Generation Apps

Stage 3 — Generate the Full Library

Variations

Credits Estimate

What’s Next