A source-grounded AI workspace for expert practitioners. Use three purpose-built chat modes, access dozens of models in one plan, and download artifacts that compound across every turn.
No credit card. Your first session is ready in 30 seconds.
Search got solved. Embeddings got solved. The model knows how to find the passage.
What still takes hours is turning a stack of sources into something a colleague, a regulator, or a client will accept: a memo with citations they can click, a spreadsheet that survives an audit, a deck that doesn't get rewritten by hand.
Every "chat with PDFs" demo answers the first half. The hard half is what you ship.
Reviewed on
A chat answers a question and moves on. A workspace retains context and builds on it.
Every output stays addressable across turns: a spreadsheet from turn 3 becomes a PDF in turn 7, a ZIP bundle in turn 9, an input to fresh analysis in turn 11. Your hour compounds instead of resetting.
This is not a UI distinction. It is a different machine: a multi-step agentic engine where tools like PDF, XLSX, HTML, charts, diagrams, and bundles are first-class outputs that can be generated, referenced, and combined into new work at any point.
Dustin's team at Rally Point Resources had 350 business agreements to review: forty to fifty pages each, all looking for the same handful of fields. The normal play was three people, three months, page by page.
He found docAnalyzer through a Google search for "PDF AI analyzer." Ran a couple of documents on the free tier. Put a small balance on the account. Then committed.
The project closed in four working weeks.
"It would have taken probably three months to get that work done. And we did get it done in four working weeks. We definitely got this done much faster than we would have otherwise."
"The OCR function works really well. Using Adobe Acrobat from an OCR standpoint is always a pain. I didn't have any issues with docAnalyzer at all."
Dustin, Founder & President, Rally Point Resources
Why docAnalyzer
Need a PDF, spreadsheet, chart, or diagram? The system can generate it as part of the conversation and make it downloadable. Those outputs don't disappear. You can reuse them later, convert them into new formats, or combine them into bundles, without starting the work over.
Claude 
GPT switched 
Gemini busy → next Gemini 30+ models across a dozen providers, all in one interface. Switch mid-thread without restarting the agent or losing context. If one model is busy, your conversation keeps moving on the next one.
Ask docAnalyzer helps you plan the next step. Focus chat runs deep analysis on a defined set of sources. Co-work chat lets you refine and shape the output in a structured canvas editor. All three modes share the same underlying engine, with knowledge carried between them. No switching contexts. No tab hopping.
“I use docAnalyzer every damn day, to compare résumés to job postings, review contracts, ask questions about terms of service, summarize, and a lot more. I've tried loads of other similar software and this one is absolutely my favorite.”
Aaron Freed, CEO recruitingguy.com (Product Hunt)
“With ChatGPT you can upload a single, limited-size file. With docAnalyzer you can upload a multitude of large, dense files and interact with them all at once, making it vastly more powerful for real studying or professional work.”
Andrew, Medical Professional (Product Hunt)
“I especially appreciate the ability to click on citations taken from the documents I uploaded. I can see where the LLM took text from, check the context, and validate that it didn't originate from a hallucination.”
Duayne G. (Capterra)
AI hallucinates. I can't trust it for serious work.
docAnalyzer doesn't guess in one shot. It runs multiple rounds against your sources: retrieve, read, refine, re-check against the material before it answers. You set how strict that grounding is. A context-adherence control tightens it for high-accuracy, source-bound work, or loosens it when you want broader synthesis. Every cited claim is traceable: the reference opens the exact page or section it came from, so you verify in seconds.
My data, and my client's data, cannot be used to train models.
Your content is never used to train any model. Each workspace is isolated at the tenant level, and artifacts are session-scoped, never pooled across users. What you upload, analyze, and produce stays inside your workspace. Every claim tied to your data links back to its exact source, so the trail is auditable.
It won't survive my real workload: 500-page PDFs, 50-doc datasets, mixed formats.
docAnalyzer is not limited by the LLM context window. Your library can be many times larger than what one model reads at once, across hundreds of long documents and mixed formats. It works in rounds: pull passages, read them, run sharper searches, and keep going until the answer holds up against the sources, with citations landing on the exact page. For batch jobs, dedicated workflows give each document its own pass and return a structured result per source. Long sessions stay coherent because old detail is pruned in a predictable, reproducible way, not by asking another model to summarize what happened.
Everything you need to know about the platform.
PDFs, Word docs, PowerPoint, OpenDocument (ODT/ODP), RTF, EPUB, Excel spreadsheets, markdown, plain text, and HTML. CSV and JSON are available with experimental mode on. You can also pull in web pages by URL. Scanned pages are run through OCR automatically so the text is searchable. Standalone image, audio, and video files aren't supported yet, though images inside your documents are read automatically.
Every cited claim in an answer is a clickable reference, and clicking opens the document viewer at the exact page or section. Citation discipline is built into the engine and reinforced by an explicit context-adherence control you can set per conversation: turn it up to keep the model strictly inside your sources; turn it down to let it draw on general knowledge.
No. Customer content is never used to train models. Documents, notes, chats, and artifacts are isolated per tenant and session-scoped: they aren't pooled across users.
Yes. 30+ models from a dozen providers, all in one interface. Switching models mid-thread doesn't restart the agent or lose the active work. If a model is busy or unavailable, the conversation keeps moving on the next model in line.
There's no 'fits in the model' limit on dataset size. docAnalyzer searches your library in rounds: pulls passages, reads them, searches again, until it has enough to answer. The working set the model sees is sized to the question, not to the corpus. Real sessions run across hundreds of long PDFs at a time, with full per-page citations. Per-tier limits on per-file size, page count, total storage, and per-day prompt usage are listed on the pricing page. Long sessions stay coherent through deterministic history compaction, not by re-summarizing on the fly.
The product UI is in English for now. Your documents and your conversations don't have to be. You can upload documents in dozens of languages, ask questions in any of them, and the chat will reply in the language you write in, or in whichever language you ask for. A single library can mix languages: twenty papers across five languages work the same as twenty papers in one. Ask in English about an Arabic source and the answer cites back into the Arabic original. Scanned documents are OCR'd in 40+ languages out of the box (see the OCR docs for the full list).
The free tier covers everyday document Q&A and the core workspace. Paid tiers unlock larger limits, premium models, batch workflows beyond included credits, and team workspaces. See the pricing page for the current tier breakdown.
Sign up, drop in a document, ask the question you'd normally spend the morning answering. See if the artifact you get back is what you'd actually ship.
