The answer exists. It is in a contract, a drawing, a policy or an email thread, in a folder someone set up years ago. People ask a colleague instead, or do the work again, because finding it takes longer than redoing it.
Internal search fails for reasons that are specific to companies, and each has a known remedy.
01Why it is harder than web search
- The documents are spread across shared drives, mail, an ERP, a document system and people's laptops.
- File names say little: "final_v3_revised" tells you nothing about what is inside.
- Much of it is scanned, so there is no text to search at all.
- The words in the question are not the words in the document.
- Not everyone is allowed to see everything, and a search that ignores that cannot be switched on.
02What search by meaning changes
Older search matches words. Search by meaning matches what a passage is about, so "when does the lease end" finds a clause headed "Term and expiry". Combined with a model that reads the passage, the person gets an answer with the clause beside it, not a list of forty files.
03What a useful system must do
- Reach every place documents live, without moving them.
- Read scans and tables as well as clean text.
- Keep each person's existing permissions: if they cannot open the file, they do not see the answer.
- Show the source for every answer.
- Say when it does not know.
- Stay current as documents change.
04Beyond finding: obligations and dates
Once documents can be read, they can be watched. Renewal dates, notice periods, warranties and commitments sit in contracts that nobody rereads. A system that extracts them can warn before a date passes, which is often worth more than the search itself.
05Where to begin
With one collection that people complain about and one group of users. Contracts for the legal and purchasing teams, or drawings and manuals for maintenance. Collect twenty real questions with known answers, and judge the system on those.
In short
- Internal search fails because documents are scattered, badly named, often scanned and worded differently from the question.
- Search by meaning plus a model that reads the passage returns an answer with its source.
- Permissions must be honoured or the system cannot be used.
- Start with one collection and a set of real questions to judge it by.
Questions
Do we have to move our documents into a new system?
No. A good search layer indexes documents where they already are.
Is it safe to let AI read confidential documents?
It is when the index and the model run on your own servers and each person only gets answers from documents they may already open.
How is this different from the search in our document system?
That search matches words inside one system. This searches by meaning across all of them and answers the question that was asked.
What does Quantum Beetle offer?
MNEMOS is built to answer questions across contracts, drawings, policies and reports with the source shown, and to track obligations and deadlines.
Sounds like your problem?
Tell us about it. We'll say honestly whether the swarm can help, and what it would take.
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