Question answering systems built on large language models face a predictable tension. Accuracy demands rich context, yet passing full documents, conversation history, and retrieval results into every inference cycle inflates latency and cost. For production pipelines, the optimization problem is not simply choosing the strongest model, but structuring the entire stack so that context length, model capability, and pricing align with the workload. Oxlo.ai addresses this directly through request-based pricing and a broad model catalog designed for long-context and agentic QA.
Retrieval Architecture and Context Window Management
Most production QA systems rely on retrieval-augmented generation to ground answers in private or dynamic knowledge. The standard pattern chunks source documents, embeds them, and injects the top-k results into the prompt. The challenge is that retrieved context often spans thousands of tokens, and multi-turn follow-up questions compound the total input length.
Under token-based billing, every additional sentence in the prompt increases cost. This creates pressure to shrink chunks or truncate history, which degrades answer quality. Oxlo.ai uses a flat per-request pricing model, so cost does not scale with input length. Teams can pass larger retrieved contexts, full conversation threads,
