Ertas for Content Creation

Fine-tune AI models on your brand guidelines, past content, and editorial standards to produce on-brand copy, articles, and creative assets at scale.

The Challenge

Content teams at media companies, agencies, and brand marketing departments face a relentless demand for high-quality, on-brand content across an ever-growing number of channels — blog posts, social media, email campaigns, product pages, video scripts, and more. Generic AI writing tools can produce grammatically correct text quickly, but the output reads like it was written by the same anonymous assistant powering every other brand. It lacks the distinctive voice, editorial standards, and institutional knowledge that separate great content from filler.

The problem compounds at scale. A media company publishing hundreds of articles per week or a brand maintaining content across dozens of product lines cannot manually edit every AI draft into compliance with style guides. Prompt engineering helps at the margins, but it cannot teach a model the nuanced voice differences between a luxury brand's Instagram captions and its investor communications. Teams end up spending almost as much time fixing AI-generated content as they would writing from scratch, negating the productivity promise entirely.

The Solution

Ertas lets content teams create AI models that are purpose-built for their brand. With Ertas Studio, editorial and marketing teams can fine-tune foundation models on curated datasets of approved past content — published articles, brand voice guidelines, style manuals, tone-of-voice documents, and high-performing social posts. LoRA adapters make it practical to maintain multiple voice models: one for the corporate blog, another for social media, a third for technical documentation. Each model absorbs the vocabulary, sentence rhythm, and editorial preferences that define the brand.

Models deploy through Ertas Cloud as private endpoints that integrate directly into content management systems and editorial workflows. Writers use the AI as a first-draft accelerator that already sounds right, dramatically reducing the editing cycle. Ertas Hub enables content teams to version, share, and iterate on voice models across departments and agencies, creating a living brand-voice asset that improves with every fine-tuning cycle. The result is 3-5x content throughput with brand consistency that manual processes struggle to achieve even at low volume.

Key Features

Studio

Brand Voice Fine-Tuning

Use Studio's visual canvas to fine-tune models on JSONL datasets of approved brand content, style guides, editorial standards, and tone-of-voice documents. Create separate LoRA adapters for different content types — blog posts, social captions, email campaigns, product copy — from a single base model.

Hub

Creative Model Library

Browse Hub for community-contributed writing models and adapters — including models pre-trained on journalism corpora, creative writing datasets, and marketing copy — and share your own brand-voice adapters across teams, departments, and agency partners.

Cloud

CMS-Integrated Endpoints

Deploy brand-voice models to Cloud endpoints that plug directly into WordPress, Contentful, Notion, or custom CMS platforms. Content creators get AI-assisted drafting inside their existing tools, with consistent brand voice applied automatically to every first draft.

Vault

Content Asset Governance

Vault manages training datasets of proprietary brand content with encryption, access controls, and versioning. Track which content was used to train which model version, enforce retention policies for archived brand materials, and maintain an audit trail for content provenance.

Example Workflow

A direct-to-consumer skincare brand publishes 40 blog articles, 200 social media posts, and 15 email campaigns per month across three product lines, each with a slightly different voice. The marketing team exports two years of approved content — tagged by product line, channel, and content type — as a JSONL dataset and uploads it to Ertas Vault. In Ertas Studio, the team selects a Mistral-7B base model from Hub and fine-tunes three LoRA adapters: one for the clinical-authority voice of the professional skincare line, one for the playful-casual tone of the everyday products, and one for the luxurious-aspirational feel of the premium range. Each adapter is deployed as a private Cloud endpoint and connected to the brand's Contentful CMS via a custom plugin. Writers select the product line before drafting, and the AI generates first drafts that already match the correct voice. The editorial team reports that editing time per piece drops from 45 minutes to 12 minutes, and brand voice audit scores — measured by an internal consistency rubric — improve from 72% to 94% across all channels.