AI That Writes in Your Brand Voice, Not Everyone Else's
Ertas Studio helps content and marketing teams fine-tune AI models on their own brand guidelines, published content, and style preferences — producing on-brand copy at scale without per-word API costs.
The Challenges You Face
Generic AI Content Sounds Generic
Every marketing team using the same LLM API produces content with the same voice, the same sentence structures, and the same filler phrases. Your brand's distinct personality gets flattened into the model's default writing style, making your content indistinguishable from competitors.
Prompt Engineering Is Fragile and Unsustainable
You can coax better outputs from a generic model with elaborate system prompts and style guides, but these break silently when the model updates, require constant maintenance as brand guidelines evolve, and still produce inconsistent results across different content types.
Content Volume Demands Exceed Team Capacity
Modern content marketing requires blog posts, social media updates, email campaigns, product descriptions, landing pages, and ad copy — often in multiple languages and for multiple audience segments. No human team can produce this volume while maintaining quality.
API Costs Make AI-Assisted Content Uneconomical at Scale
When every generated paragraph costs money, teams self-ration AI usage. Writers avoid regenerating until they get a good output, skip AI assistance for lower-priority content, and the promised productivity gains never fully materialize.
How Ertas Solves This
Ertas Studio lets your content team fine-tune a model that has internalized your brand voice. Upload your best-performing blog posts, email campaigns, social captions, and product copy as training examples. Studio trains a model that writes the way your brand writes — not the way a generic LLM writes.
The training process is visual and requires no ML expertise. Your content strategist or brand manager can run the entire workflow: upload examples, click train, test outputs in the playground, and export the model. When brand guidelines evolve or you launch a new product line, add fresh examples and retrain.
Exported GGUF models run on your own infrastructure at zero per-word cost. Generate as much content as you need — first drafts, variations for A/B testing, translations, social adaptations — without watching a usage meter. AI becomes a fixed-cost tool in your content stack, not a variable expense.
Key Features for Content & Marketing Teams
Brand Voice Training
Fine-tune on your published content — blog posts, emails, social captions, ad copy — so the model learns your vocabulary, sentence rhythm, tone, and the specific way your brand communicates complex ideas simply.
Content-Type Specialization
Train separate models for different content formats — one optimized for long-form blog posts, another for punchy social captions, a third for conversion-focused email subject lines. Each model excels at its specific task.
Unlimited Generation
Self-hosted GGUF models produce content at zero marginal cost. Generate 10 headline variations, create first drafts for 50 blog posts, or produce product descriptions for every SKU — without a usage meter limiting your creativity.
A/B Variation Generation
Quickly generate multiple on-brand variations of any content piece for testing. Because generation is free, you can test more aggressively — more subject line variants, more ad copy options, more landing page headlines.
Why It Works
- Content teams using brand-tuned models report that AI-generated first drafts require 60-80% less editing compared to outputs from generic LLMs, because the model already writes in the correct voice and style.
- Self-hosted models have enabled content teams to increase output volume 3-5x without proportional headcount increases, using AI for first drafts and human editors for refinement and fact-checking.
- Brand voice consistency across AI-generated content eliminates the tonal drift that occurs when multiple freelancers or generic AI tools produce content with slightly different styles.
- Zero-cost generation has unlocked A/B testing strategies that were previously cost-prohibitive — teams generate 10-20 variations of critical content pieces and test them all.
- Monthly retraining with new published content keeps the model current with evolving brand guidelines, seasonal campaigns, and new product launches — a process that takes under an hour.
Example Workflow
A B2B SaaS company's content team needs to produce 40 blog posts per month across four product lines. The content strategist exports the company's 200 best-performing blog posts as JSONL training examples — each pairing an outline with the finished post. She uploads the dataset to Ertas Studio and trains a 7B model.
The playground confirms the model captures the brand's conversational-but-authoritative tone, correctly uses product terminology, and structures posts the way their audience prefers. She exports the GGUF and deploys it on the team's content production server.
Writers now start each post by feeding the model an outline and getting a complete first draft in seconds. They focus their time on adding expert insights, verifying technical accuracy, and polishing — the creative work that humans do best. Monthly output increases from 15 to 40 posts without adding headcount. When the brand refreshes its voice guidelines, the strategist adds 50 new examples and retrains in 30 minutes.
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Early bird pricing starts at $14.50/mo — locked in for life. Plans for builders and agencies.