Can AI search all my office documents?
Yes, if the files are readable and the search respects who may see what.

The documents are split into passages, and each passage is converted by an embedding model into a list of numbers that represents its meaning. Those lists go into an index. A question is converted the same way and the nearest passages are retrieved. A language model can then write an answer from those passages, which is the method known as retrieval-augmented generation.

Two open embedding models show the range. Check the licence and the language coverage before choosing.

BEIR, a benchmark of 18 datasets used to test 10 retrieval systems, found that BM25 keyword scoring is a strong baseline. Re-ranking and late-interaction models did best on average but cost more computing, and dense retrievers often did worse than other approaches on data they had not seen. The BGE-M3 card recommends a pipeline of hybrid retrieval followed by re-ranking, naming embedding retrieval combined with BM25 as the classic example, and Open WebUI, a self-hosted chat front end, lists hybrid BM25 and vector search with re-ranking among its document features.

For an office this means a search for an invoice number should still find that exact number, and a search for what is our refund policy should find the policy even if it never uses the word refund.

• Permissions. OWASP lists unauthorised access and data leakage as a risk of retrieval systems, particularly where groups share one index. Its mitigation is permission-aware indexes with strict separation between groups. ANVE’s roles today are administrator and user, with teams, finer roles and single sign-on not on ANVE yet, and it has not published how document-level permissions work. Get that in writing before a paid pilot, and before you load files of mixed sensitivity.
• Scans. Scanned pages have no text until OCR runs. Tesseract works best at 300 dpi or more and has Hindi data; vision models can also read page images. Check results on your worst scans.
• Hidden text. OWASP warns that documents can carry hidden instructions or text and recommends validating documents before they enter the index.
• Stale files. An index that still holds last year’s policy will serve it confidently. Re-index on a schedule and show each result’s date.

• Pick one team and one folder tree to start, rather than the whole file server.
• Convert what needs OCR, and keep each original file path and page number with its passages.
• Use a multilingual embedding model if your files mix English, Hindi and Hinglish, and test queries in both scripts.
• Write 30 real questions with known answers. Measure how often the right passage appears in the top five results.
• Show the source passage beside every answer, with the file name and date.
• Fix permissions before the first user logs in, then re-index on a schedule.

ANVE’s documents function answers questions over the office’s own documents and shows the source passage with each answer. Private GPTs are assistants built on the office’s own material, and vision models read scanned documents with OCR. ANVE serves many named users from one device on the office network.

These run today in ANVE’s development build. No sealed unit exists yet.

No sealed ANVE unit exists; every image of the appliance on this site is a labelled render. The software runs today on a development machine. No customers yet, no certifications, no delivery date, and the final price is not announced.

Reservations are ₹999 in India or US$12 elsewhere, refundable in full until you confirm an order in writing (terms). Offices that cannot use cloud AI can apply to be one of ten design partners.