What is CHAI?

CHAI (Cognitive Hive AI) is Aether's AI architecture. Instead of relying on one large, expensive language model for everything, CHAI orchestrates a team of specialized Small Language Models (SLMs) - each fine-tuned for a specific task.

Why a Hive?

A team of specialists outperforms a generalist. A fine-tuned 7B model often beats GPT-4 at its specialty while costing 99% less. CHAI gives you the best of both worlds: superior results and dramatically lower costs.

AI Specialists

Aether includes a complete set of AI specialists out of the box, each optimized for a specific content task:

Content Writer

Generates high-quality content in various styles and formats. The Content Writer adapts to your brand voice, follows your style guidelines, and creates engaging content for any purpose.

Usage
// Generate content with brand voice
let article = await hive.GenerateContent(
    content_type: "blog_post",
    prompt: "Write about sustainable packaging trends",
    context: TaskContext { language: "en", ... }
)

// Rewrite content in a different style
let rewritten = await hive.RewriteContent(
    content: article,
    style: "casual",
    tone: "friendly"
)

Capabilities:

  • Blog posts, articles, and long-form content
  • Product descriptions and marketing copy
  • Headlines and calls-to-action
  • Content expansion and compression
  • Style and tone adaptation

SEO Optimizer

Automatically optimizes your content for search engines. Generates meta titles, descriptions, and structured data while suggesting improvements to content structure.

Usage
// Generate SEO metadata with JSON-LD
let meta = await hive.GenerateSeoMeta(
    content: article,
    include_schema: true
)

// Returns: { title, description, keywords, json_ld }

Capabilities:

  • Meta title and description generation
  • Keyword extraction and suggestions
  • JSON-LD structured data for rich snippets
  • Content structure recommendations
  • Readability analysis

Taxonomy & Tagging

Automatically categorizes and tags content using your existing vocabularies. Extracts named entities (people, places, organizations) and suggests new taxonomy terms when content covers novel topics.

Usage
// Suggest tags from existing vocabulary
let tags = await hive.AutoTag(
    content: article,
    vocabulary_id: "topics",
    max_tags: 5
)

// Returns: [{ label, confidence, is_new }, ...]

Capabilities:

  • Automatic category assignment
  • Tag suggestions with confidence scores
  • Named entity extraction
  • New term suggestions for novel topics
  • Consistent categorization across content

Translation

Translates content between languages while preserving meaning, tone, and formatting. Handles cultural adaptations and technical terminology correctly.

Usage
// Translate while preserving HTML
let spanish = await hive.TranslateContent(
    content: article,
    source_lang: "en",
    target_lang: "es"
)

Capabilities:

  • 50+ language pairs
  • HTML/Markdown preservation
  • Cultural adaptation
  • Technical terminology handling
  • RTL language support

Content Moderation

Automatically reviews content for policy compliance. Detects harmful content, assesses audience appropriateness, and flags items for human review.

Usage
// Check content against policies
let result = await hive.ModerateContent(
    content: user_submission,
    policies: Set::from(["hate_speech", "violence", "adult"])
)

// Returns: { approved, flags, confidence, action }

Capabilities:

  • Toxicity detection
  • Policy compliance checking
  • Sentiment analysis
  • Audience appropriateness
  • Configurable policies

Multi-Provider Support

CHAI supports multiple AI providers, giving you flexibility in model selection and avoiding vendor lock-in:

Provider Models Best For
Anthropic Claude Sonnet, Claude Haiku Content generation, analysis
OpenAI GPT-4, Embeddings Semantic search, complex reasoning
Google Gemini Pro Multilingual, long context
Mistral Mistral Large, Mixtral European data residency
Local Any GGUF model Air-gapped, full control

Cost Efficiency

CHAI uses efficient model selection to minimize costs while maximizing quality. Classification tasks use fast, cheap models (Claude Haiku), while generation tasks use more capable models (Claude Sonnet).

Task Type Default Model Cost per 1K tokens
Content Generation Claude Sonnet $0.003 in / $0.015 out
SEO Optimization Claude Sonnet $0.003 in / $0.015 out
Translation Claude Sonnet $0.003 in / $0.015 out
Auto-Tagging Claude Haiku $0.00025 in / $0.00125 out
Moderation Claude Haiku $0.00025 in / $0.00125 out
Summarization Claude Haiku $0.00025 in / $0.00125 out
Embeddings text-embedding-3 $0.00013

Result: 96% Cost Savings

By using the right model for each task - and caching where possible - Aether delivers enterprise AI capabilities at a fraction of the cost of calling GPT-4 for everything.