OpenAI Assistants Agencies for Document Processing

Assistants API is the faster path to a working document Q&A system and handles most common document processing use cases well. LlamaIndex becomes the better choice when you need advanced retrieval techniques — hybrid search combining dense and sparse retrieval, metadata filtering at scale, custom reranking pipelines, or integration with specific vector databases you already operate. LlamaIndex also gives you more control over chunking strategies, which matters for technical documents with complex structure. For straightforward document Q&A, contract review, or PDF extraction workflows where you do not need custom retrieval, Assistants API is the pragmatic default.

Individual files are capped at 512 MB and 5 million tokens after parsing. A single assistant can reference up to 10,000 files in its vector store. These limits accommodate most business document processing scenarios — enterprise knowledge bases, contract repositories, product documentation libraries. Where the limits bind is in processing very large individual documents (full-length books, extensive engineering specifications) or extremely large corpora (millions of documents). For those cases, a custom retrieval pipeline with a dedicated vector database and chunking strategy will give you more headroom and better performance at the edges.

Assistants File Search accuracy is strong for retrieval-and-answer tasks on well-structured documents. It performs well on contracts, policies, manuals, and reports where the answer exists as a continuous passage. Accuracy degrades on highly tabular documents, scanned PDFs with OCR artifacts, and documents with complex multi-column layouts where chunking splits meaningful content. Custom pipelines using LlamaIndex or LangChain with purpose-built chunking and reranking can outperform Assistants on these edge cases. For most business documents, Assistants accuracy is production-grade and the time savings on infrastructure justify accepting the small accuracy trade-off.

Assistants API charges for file storage ($0.10 per GB per day for vector store), model tokens for each query, and a per-session Code Interpreter fee when extraction is involved. A custom pipeline adds costs for a vector database (Pinecone starts around $70/month for production tiers), embedding API calls for indexing, and compute to host the orchestration layer. For small to medium document volumes (under a few thousand documents, moderate query traffic), Assistants API is typically cheaper in both cost and engineering time. At large scale with high query volume, a self-managed vector database amortizes better.