AI Tools for Document Management: What Actually Works

Most AI document tools say they’ll make your files searchable in minutes. Then you upload a 450-page contract and ChatGPT stops at page 165 with a vague “processing limit” error.

The problem? Two different caps you won’t hear about up front.

The File Size Trap You Won’t Hear About

ChatGPT advertises a 512MB file limit. Big number. But there’s a second constraint: 2 million tokens per file for text documents (per OpenAI’s official docs, as of January 2026). A dense 400-page PDF can hit the token cap while staying well under the size limit.

Your upload succeeds. The analysis fails halfway through.

One user on OpenAI’s forum uploaded a 500-page research paper. ChatGPT processed 37% of it, then returned: “Due to time constraints, I couldn’t complete the entire document.” The file was under the size cap. The tokens weren’t.

Free users: 3 files per day. Plus subscribers ($20/month as of early 2026) get 80 files per 3-hour rolling window – but also a separate 50-image daily cap. Whichever limit you hit first blocks your workflow. Testing shows keeping individual files under 25MB avoids most backend processing errors, even though the official cap is 20x higher.

What These Tools Do

Three things: extraction (pulling text from scans), search (finding content by meaning), synthesis (answering questions across multiple docs).

Extraction uses OCR to turn scanned receipts, contracts, handwritten notes into searchable text. Google’s Document AI handles 200+ languages and recognizes checkboxes, tables, handwriting in 50 languages (as of 2026). Azure Document Intelligence bills by page analyzed, offers a free tier for testing.

Semantic search converts your query and every document into numerical vectors (embeddings), then matches by meaning. Ask “PTO policy for remote hires” and it surfaces docs mentioning “time off for telecommute employees” even if they never use your exact words. Traditional keyword search would miss them.

RAG (Retrieval-Augmented Generation) is the synthesis layer. When you ask a question, the system retrieves relevant snippets from your docs, feeds them as context to an LLM, and generates an answer grounded in your actual files. Notion AI searches your workspace this way. Enterprise tools like Glean index across Slack, Drive, Jira, email simultaneously.

Why this matters: knowledge workers lose 3+ hours daily hunting through email, chat threads, scattered folders (Adobe research, September 2025). A single search bar that understands intent and checks permissions could reclaim that time.

Think about your last “where did we decide that?” moment. How long did it take? That’s the use case.

Pick Based on the Problem

Uploading files for one-off analysis? ChatGPT Plus handles most document types but chokes on large PDFs (watch that token cap). Perplexity AI allows 10 files per prompt with a 40MB limit per file on free/Plus plans, 50MB on Pro (as of December 2025). For bulk uploads, services like OneFile merge multiple documents into a single text file to bypass per-file quotas.

Searching across a sprawling knowledge base? Notion AI works if your team already lives in Notion – it indexes your workspace, databases, connected tools like Slack or Google Drive. Glean is made for enterprise search across 250+ systems with role-aware permissions. According to a 2026 enterprise search buyer guide, deployment speed differs: Glean typically goes live in days; traditional solutions can take months.

Building a custom RAG system? Meilisearch offers hybrid search (keyword + vector) with SOC2 compliance. Open-source frameworks like Haystack let you chain retrievers, rerankers, generators. AWS Bedrock provides managed RAG with automatic vector handling. The trade-off: flexibility vs maintenance burden.

Pro tip: Test your actual document types during evaluation. A tool that works great on plain text may struggle with dense technical PDFs, scanned images, spreadsheets with complex formulas. Upload 5-10 representative files and check if summaries capture the details you need. (My first test? A 200-page technical spec. The summary missed half the edge cases I cared about.)

For legal or compliance use cases, specialized tools like LinkSquares (contract management) or simplify AI (legal document search) outperform general-purpose options because they’re trained on domain-specific terminology.

When Semantic Search Fails

Query ambiguity. “Capital requirements” could mean financial reserves, startup funding needs, or letter capitalization rules. The system picks based on what’s common in your corpus – if you meant the rare interpretation, results will be wrong but confident.

Embedding model mismatch. Your documents are technical (engineering specs, medical records) but the embedding model was trained on general web text? It won’t capture domain-specific relationships. Switching to a model fine-tuned for your field improves relevance – most SaaS tools don’t expose this setting.

Chunking problems. RAG systems split long documents into smaller chunks (typically 200-500 tokens) before indexing. If a critical explanation spans two chunks, neither fragment may rank high enough to surface. Tuning chunk size and overlap helps, but there’s no universal right answer – depends on your document structure.

One workaround: train your team to check citations. Good RAG tools link answers back to source passages. If a response cites page 47 but page 47 says something different, you’ve spotted the problem.

Why RAG Still Hallucinates

RAG should have fixed LLM hallucinations by grounding responses in real documents. It helps – but it’s not foolproof.

A 2025 Stanford study tested legal AI tools (Lexis+ AI, Westlaw AI-AR) on bar exam questions. Despite using curated legal databases, accuracy ranged from 42% to 65%. The rest? Hallucinations or incomplete answers. One system claimed a bankruptcy rule was “jurisdictional” and cited a paragraph that doesn’t exist.

RAG fails in two places: retrieval and generation.

Retrieval failures: the system pulls wrong or irrelevant docs. Maybe the query is too vague. Maybe the right document exists but ranks 11th and you only checked the top 10. Maybe the knowledge base is outdated – LLMs will confidently cite 2023 data in 2026 if that’s what you fed them.

Generation failures: the LLM ignores retrieved context or synthesizes incorrectly. Research published in March 2025 identified “context noise” as a problem – if the retriever dumps 10 documents into the prompt and only 2 are relevant, the LLM may latch onto the noise. Another failure mode: the model fuses information from separate sources in misleading ways, creating a “fact” that appears in neither original document.

A biomedical research team built MEGA-RAG, combining dense retrieval (FAISS), keyword search (BM25), and knowledge graphs. Adding a cross-encoder reranker to prioritize truly relevant snippets cut hallucination rates by over 40% compared to standard RAG. The lesson: retrieval quality matters more than the generator.

You can’t eliminate hallucinations. But you can reduce them. Use multiple retrieval methods (hybrid search), rerank results before generation, flag when retrieved docs don’t contain enough info to answer confidently. Some frameworks let the model return “I don’t know” instead of guessing – enable that.

Three Config Mistakes That Kill Accuracy

Ignoring permissions. Your search tool indexes everything without respecting source-system access controls? Users will see results they shouldn’t. Worse: they’ll assume the info is safe to share. Enterprise tools say “permission-aware” search, but implementation varies. Test it: create a restricted folder, index it, then search as a user without access. Results leak? Your tool isn’t checking permissions correctly. (Happened to a client – HR docs showed up in company-wide search. Took 48 hours to realize.)

Skipping reranking. Initial retrieval casts a wide net. A reranker (separate model) scores those candidates by relevance to the specific query. Skip this step? Lower-quality results in your top-K set. Generator works with mediocre context. Most open-source RAG setups omit reranking to save compute costs. Don’t.

Never refreshing the index. Your knowledge base changes – new policies, updated spreadsheets, deleted drafts. If the search index isn’t updated, queries return stale or nonexistent documents. Set up real-time sync or at least daily batch updates. Azure AI Search and Google Vertex AI Search handle this automatically for connected sources. DIY setups often forget until users complain.

Comparing the Big Names

The free tiers are playgrounds. If you’re doing this daily, budget for a paid plan or API costs.

A Note on Privacy

Uploaded files may train future models unless you opt out (as of early 2026). OpenAI states that data can be used for improvements by default – ChatGPT Team and Enterprise plans exclude training. Google’s NotebookLM similarly processes your docs; check their data policy if handling sensitive material.

For on-prem requirements, Azure Document Intelligence and AWS Bedrock let you keep data in your own cloud. Open-source stacks (Haystack, LangChain + local LLMs) give full control but require DevOps resources.

Next step: pick one tool that matches your file volume and test it with 10 real documents. Check if it handles your formats (scanned PDFs, spreadsheets, images), if answers cite sources, and if it respects the access rules you need. Most failures show up in the first hour of testing.

FAQ

Can AI tools search inside scanned PDFs or images?

Yes, via OCR. Google Document AI and Azure Document Intelligence extract text from scans, photos, handwritten notes. Quality depends on scan clarity – blurry images or ornate handwriting degrade accuracy.

Why does ChatGPT stop analyzing my document halfway through?

Two reasons: the 2 million token cap (separate from the 512MB file size limit as of 2026) or backend processing timeouts during peak hours. Dense PDFs with lots of text hit the token limit faster than image-heavy slides. One client’s legal contract: 387 pages, stopped at page 142. Split large files into smaller chunks or use a tool made for bulk document processing like NotebookLM Ultra or enterprise search platforms.

How do I know if my RAG system is hallucinating?

Check citations. If the tool says “According to page 12” but page 12 says something different (or doesn’t exist), that’s a hallucination. Run test queries where you know the ground truth. Track how often answers conflict with source docs. Research shows hallucination rates between 18-65% depending on retrieval quality and domain (as of 2025 studies) – legal and medical applications see higher failure rates because precision matters more. One misconception: more data = less hallucination. Not true. Stale or noisy data makes it worse.