Semantic search can help when you remember what a document was about but not its filename. Instead of requiring identical wording, it compares the meaning of a query with indexed text. In a recovery workflow, that can help identify a document saved under an unfamiliar name.
Recovery Studio provides semantic-text and image-organization workflows for supported inputs. Check the support matrix for the particular operation you need. Text retrieval, OCR, category suggestions and scene-based photo search are distinct capabilities; the phrase “AI search” does not by itself establish all four.
Match the search method to the clue you remember
| Your clue | Appropriate operation | What to verify |
|---|---|---|
| “The receipt for the computer purchase” | Semantic text retrieval | Requires indexed readable text or OCR output |
| A particular invoice number | Exact identifier verification | Semantic ranking is not a guaranteed exact match |
| “This looks like a document or a landscape” | Image classification | Category suggestions can be uncertain |
| “A red bicycle beside a lake” in a text-free photo | Visual semantic retrieval | Explicit support for querying image content, beyond category suggestions |
A photograph of a receipt may be searchable because OCR reads its words. A photograph of a landscape without text does not acquire a searchable description merely because it was classified. If you need scene-based photo retrieval, verify that a supported visual-search path indexes and queries the images themselves.
Build the index from known copies
Select recovered copies on separate storage, install any required model assets and index supported inputs. Check text formats, OCR image formats and document containers individually. TXT and Markdown contain text directly; a scanned image needs OCR, while a PDF or Office container may require its own supported extraction or conversion path.
Review indexing results before interpreting search results. Unsupported inputs, empty OCR output and processing errors leave gaps. The index does not silently become a complete search of every drive or every file on the computer; it covers the copies explicitly indexed.
Check how much content the selected index reads from each file. Long documents need a coverage check rather than an assumption that every paragraph was indexed. A query about text outside the indexed portion may return no relevant result even though the source file contains it.
Treat ranking as a clue
For an illustrative example, a query about “payment for a laptop” might retrieve an indexed receipt that uses “computer purchase.” That is the kind of relationship semantic retrieval is intended to help with. It is not a measured accuracy promise for every receipt or query.
Read the excerpt, then open the recovered source to verify the relevant details. A similarity score is not a probability that the document is correct, a confidence level in recovery integrity or proof that all matching files were found. Result thresholds and display limits can also omit candidates.
Use specific context when possible and compare more than the first result. For exact numbers, names or legal wording, verify the original text directly rather than interpreting semantic closeness as exact agreement.
Preserve the difference between evidence and suggestions
Recovered bytes, OCR text, semantic matches and suggested image categories are different outputs. Keep the original recovery copy so you can tell which information was present in the source and which came from a model's interpretation.
The local index contains excerpts and paths that may themselves be sensitive. Supported inference runs locally after setup, while model installation and account/payment functions have separate network activity. Read the privacy guide before indexing sensitive material.
For an identifier-focused example, use the invoice OCR guide. For photographs whose main problem is missing or damaged image content, start with photo recovery. Use the support matrix to match the input, search method and host platform before selecting a workflow.