Glossary
LLM orchestration.
Routing different tasks through different foundation models (OpenAI, Anthropic, Google, and others) based on which one is best at each task, instead of picking a single model for everything.
GPT models reason well. Claude handles long context. Gemini does multimodal. Each foundation model has strengths and trade-offs. An application that hardcodes one model gets locked into that model's weaknesses.
What this looks like in practice
One model is never best at every task.
GPT models reason well but hallucinate confidently. Claude handles long context but costs more per call. Gemini does multimodal well but isn't always best for pure text. An application that hardcodes one model inherits its weaknesses. Routing per task means each step uses the right model for the job.
Providers go down. Rate limits hit.
At any meaningful scale, you will get 429s and 5xxs from at least one provider in any given week. Hardcoded-to-one-vendor applications break or degrade visibly. Orchestration routes around outages, manages rate limits per-provider, and tries the next-best model when the first declines. The user sees one answer. The fallback is invisible.
Grounded in your business.
The orchestration layer also grounds every answer in your actual data with a source citation. Foundation models don't have to guess what your business does. They get the context they need to answer correctly, then the answer points back to where it came from.
We don't compete with foundation models. We use them.
Simply Asking is an orchestration layer above OpenAI, Anthropic, and Google. The model that's best at each task gets that task. The brain that knows your business is the layer that makes those models actually useful for you.
Bring your knowledge.Simply ask.
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