An insurance company is designing a claims processing assistant that analyzes policy documents, damage photos, and adjuster notes simultaneously. A typical claim involves a 180-page policy PDF, 12 damage photos, and 8,000 words of adjuster notes. The architect must choose between (1) multiple specialized Claude instances processing each modality separately, or (2) a single multimodal Claude call with all inputs. The company prioritizes claim consistency and cross-modality reasoning. Which approach is architecturally superior and why?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — claims consistency depends heavily on cross-modal reasoning — for example, a photo showing pre-existing rust must be weighed against the policy exclusion language and the adjuster's written assessment simultaneously. Separating these into independent instances loses the correlative reasoning that catches inconsistencies and edge cases.
Full explanation below image
Full Explanation
Claims consistency depends heavily on cross-modal reasoning — for example, a photo showing pre-existing rust must be weighed against the policy exclusion language and the adjuster's written assessment simultaneously. Separating these into independent instances loses the correlative reasoning that catches inconsistencies and edge cases. Claude's 200K token context window easily accommodates 180 pages (~90K tokens), 12 images, and 8,000 words of notes within a single call. Option A's parallel approach would require a separate aggregation step that cannot reason natively across modalities. Option C (summary aggregation) loses detail from the source documents. Option D is factually incorrect — 200K tokens is sufficient for this workload; 180 pages of PDF text typically renders to 45,000-72,000 tokens, well within limits.