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GlossaryAI Model Router

AI Model Router

The AI Model Router is Learn Domains' abstraction that selects, invokes, and logs language models based on task type, quality needs, and cost constraints.

Also known as: model-router · llm-router

Why it matters

Hard-coding one model everywhere destroys margin or quality. Routing lets you classify with small models, generate long-form with premium models, and enforce consistent token and cost accounting for 70–80% gross margin targets.

How it works

Services declare task intent, classification, summary, draft, strategic analysis. The router maps intent to the right model, records usage against your credits, and respects your balance before running a job.

Common mistakes

  • Bypassing the router with direct API calls in feature code.
  • Using premium models for trivial yes/no classification.
  • Skipping cost logs, making margin dashboards blind.
  • Changing models in env vars without updating registry documentation.

Best practices

  • Route every AI call through the router, no exceptions.
  • Default to cheaper models unless QA requires premium output.
  • Alert when task cost drifts above registry estimates.
  • Review usage breakdown on the Credits page monthly.

Learn Domains perspective

Not every task needs the same depth, classifying a question is lighter work than writing a 2,000-word draft. Learn Domains meters AI through Mission Fuel so heavy generation costs more than a quick analyst question, you see usage on the Credits page without picking models yourself.

FAQ

Can I choose the model per request?
Learn Domains routes by task type for consistent quality and predictable credit use.
Are model costs visible to users?
Credits abstract the cost, see breakdowns and forecasts on your Account → Credits page.
What happens if a model provider fails?
Router fallbacks apply per configuration; failed jobs release credit reservations.

Next steps

  1. 1Review Credits usage breakdown by category.
  2. 2Identify tasks burning premium credits unnecessarily.
  3. 3Align team workflow to start with structured Content Operations drafts before optional AI enhancement.

Knowledge graph

Parent terms

  • Mission Fuel

Related concepts

rag · digital-asset-intelligence · content-operations

Entity relationships are structured for future graph visualization.

Continue learning

Related features

  • AI AnalystAsk what to do next, answered from your own data.
  • Content OperationsFrom opportunity to ready-to-publish draft.
  • Mission BriefToday's highest-impact moves, every morning.

Glossary terms

  • Mission FuelMission Fuel is the credit system that gates, reserves, and logs consumption of AI and premium operations so growth work stays margin-positive.
  • RAGRAG is an AI pattern that retrieves relevant documents from a knowledge store and supplies them as context to a language model before generating a response.
  • AI Growth AnalystAn AI Growth Analyst is a conversational interface that turns your website's connected data into specific, ranked recommendations for what to do next.
  • Content OperationsContent Operations is the end-to-end workflow for turning ranked content opportunities into review-ready drafts grounded in your Knowledge Base.

Documentation

  • AI AnalystAsk what to do next and get a specific answer grounded in your own search, analytics, and revenue data.
  • Content OperationsTurn an opportunity into a brief, outline, and a ready-to-publish draft in your brand voice, with human review built in.
  • CreditsWhat credits are, what consumes them, and how monthly grants and purchased packs differ.
  • BillingHow the $1 trial works, how it converts to Starter, and how plans and cancellation are handled.

Next steps

  • Pricing$1 trial, plans, and Mission Fuel credits.
  • Interactive demoDrive the command center on sample data. no signup.

Related concepts

rag · digital asset intelligence · content operations

Related terms

  • Mission Fuel
  • RAG
  • AI Growth Analyst
  • Content Operations
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Digital Asset Intelligence Command Center.

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