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Semantic Search in PDF

AI
Ask in natural language, get verbatim passages with page refs and relevance notes.
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What you'll get: search-by-meaning across one or many PDFs — finds concepts even when keywords don't match. Each hit cited to the source page. For another use, try AI · Chat with PDF.

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Private & secure
AI tools process your PDF in memory, send only the extracted text to the model, and discard everything immediately. No file is persisted on our servers.
No watermarks
Clean output, no branding, no daily limits.
Pay only for what you use
Purchased credits never expire. Cancel anytime.

Why people use Semantic Search in PDF

Ctrl-F finds exact matches but misses paraphrases. Semantic Search in PDF accepts a natural-language question, retrieves relevant passages from the PDF (regardless of phrasing), and returns them verbatim with page references and relevance notes. Useful when you know what you’re looking for but don’t know the exact words the source used.

Find a clause in a long contract
Search “what happens if the partnership ends?” in a 60-page partnership deed and get the dissolution clauses verbatim with page numbers. Faster than skimming for “dissolution” / “termination” / “exit” manually.
Locate a fact in a research paper
Search “what was the sample size?” or “how was bias controlled?” in a 30-page paper. Returns the relevant paragraph regardless of how the paper phrased it.
Lookup in a textbook / handbook
“How do I revoke a power of attorney?” in a 200-page legal handbook. Returns the relevant section with surrounding context, faster than scanning the index.
Spec / docs question-answering
When working with technical specs you don’t know cover-to-cover, semantic search lets you ask the doc directly. Different from chat-with-pdf because output is verbatim passages, not generated responses.
Across-doc passage retrieval
When you have multiple PDFs and a question, run the search on each and aggregate. Returns where each doc addresses your question, useful for the “synthesize across sources” workflow.

How Semantic Search in PDF works

Three steps. Sign in to run, zero retention — files never persisted on our servers.

  1. STEP 1
    Drop the PDF + ask the question
    PDF up to 100 MB. Question in natural language — “what does X mean”, “how do I do Y”, “when did Z happen”.
  2. STEP 2
    We embed + retrieve
    Server-side text extraction + chunking + vector embedding. Question embedded; retrieved passages ranked by semantic similarity (not keyword match). Top 3–5 passages returned with relevance scores.
  3. STEP 3
    Get verbatim passages with cites
    Markdown output with: question echoed back / 3–5 verbatim passages with page references / relevance score / context note. Verbatim means we DIDN’T rewrite — you see exactly what the source said.

What makes pdfcraft ai different

  • Citations on every claim
    Every AI summary, answer, and translation links back to the page it came from. You can verify the source before relying on the output. Hallucination is bounded, not blind.
  • Pay only for what you use
    No subscription. Purchased credits never expire. Cancel anytime. Cost shown before you run — exact credit amount appears after upload.
  • Multi-provider routing, no vendor lock-in
    We balance multiple AI providers per call to optimise cost and quality. If one has an outage, the next call automatically tries another. You're not bound to a single vendor's pricing or downtime.
  • Zero retention by design
    Your PDF lives in memory only as long as the AI call needs it, then it's discarded — nothing is persisted to our servers. We don't fine-tune models on your data. Verifiable in your dashboard's audit log.
  • Honest about model limits
    When a doc exceeds the model's context window we surface that the output was truncated (we render result.wasTruncated in every AI tool's UI). When a number can't be verified from the source we flag it. No silent failures.

Frequently asked questions

How is this different from chat-with-pdf?
Chat generates an ANSWER (synthesized from the doc, model-paraphrased). Semantic Search returns PASSAGES (verbatim from the doc). Use chat when you want a direct answer; use search when you want to read the source material yourself with the LLM’s help finding the right pages.
What’s the difference from Ctrl-F?
Ctrl-F is exact lexical match — misses paraphrases. Semantic search matches meaning regardless of wording. Search “dog” and Ctrl-F won’t find “canine”; semantic search will.
What if my question has no answer in the doc?
The tool surfaces the closest-matching passages with low relevance scores and a note like “this passage discusses related but not the exact topic.” Better than “no results” because near-misses are often informative.
Can I search multiple PDFs at once?
Single PDF per call. For multi-PDF semantic search across a corpus, AI Chat (multi-doc mode is on the roadmap) is the closer fit. Today’s workaround: run the search on each PDF separately, aggregate the top results.
What’s the credit cost?
3 credits per search. Cost is fixed regardless of doc size or query count (one query per call today; multi-query batching on the roadmap).
Privacy?
PDF + query sent to inference provider for embedding + retrieval, not stored. For maximum confidentiality, redact PII via Redact PDF before running search; the structural search works on the redacted version.

Want a synthesized answer instead?

AI Chat reads your PDF and answers questions in natural language with page citations. Pairs with semantic search: search to find passages, chat to discuss them.

Try AI Chat

Related PDF tools

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