Key takeaways
- Keyword search compares characters. Semantic search compares meaning
- In business, 60% of internal searches fail (source: Coveo) because every team uses different vocabulary
- Semantic search understands that "supply chain" and "logistics flow" refer to the same thing, and retrieves relevant documents regardless of the terms used
Let’s start with a confession. Before building Archesia, we used keyword search too. And we grumbled about it like everyone else.
The turning point came from a consulting firm we were working with. Over 3,000 deliverables produced in ten years. Reports, analyses, proposals. A treasure trove of collective expertise. And nobody used it, because the internal search engine returned nothing unless you knew the exact file name.
3,000 documents. Years of work. Inaccessible.
Why keyword search fails
Keyword search is not inherently bad technology. It works on Google, where billions of pages guarantee matches. In a business setting, conditions are radically different: a few thousand documents, produced by dozens of people who each use their own vocabulary.
Industry synonyms
“Operational restructuring”, “process reorganisation”, “transformation programme”. One concept, three phrases. A keyword engine does not connect them.
Cross-cutting relevance
A report on “climate risk in the banking sector” is relevant to searches about climate, banking, and risk management. But its title may not contain any of those words.
The complexity of real questions
“Which topics do we have the most expertise in?” No keyword engine can answer that. The question requires understanding the meaning of hundreds of documents.
How semantic search works
The principle is simple, even though the technology behind it is complex.
At import
Each document is analysed by an AI model that extracts its meaning. “CO2 emissions rose by 12%” and “air pollution is worsening” are recognised as covering the same subject, even though no words are shared.
At search
Your question is analysed the same way. The system matches the meaning of your question against the meaning of every indexed passage.
At display
The tool synthesises an answer instead of handing you a list of files. Every claim cites its source: which document, which page, which passage.
Why the AI model matters A model trained on French-language data will understand “operational restructuring” better than a generic English-language model. That is why we chose Mistral for Archesia. Not out of patriotism. Because our users work in French, on French documents. And because it means their data never pass through American servers subject to the Cloud Act.
Three real situations
The consultant facing a tender
They type “energy” into SharePoint. 200 results, including electricity bills. With semantic search, they ask: “Our engagements in the energy sector over the last 3 years, with methodologies and results.” A structured summary of 23 relevant engagements. The difference between a generic proposal and one that demonstrates expertise.
The project manager taking over an account
The previous lead has left. Manually reconstructing context: one week. With semantic search: “Full history of the Martin account, campaigns, feedback, key decisions.” A few minutes. For an agency with staff turnover, this is collective memory that no longer gets lost.
The business owner who needs to decide
They are considering whether to renew a supplier. A vague sense of past problems, nothing precise. “Incidents with supplier X since 2023.” Delays, non-conformities, penalties. All sourced. For an SMB, this means a decision based on facts instead of a hunch.
How to spot a genuine semantic engine
Many tools claim to be “semantic” now that AI is fashionable. Three tests, five minutes, on your own documents.
- Synonyms. Search for “environmental impact” when your documents say “carbon footprint”. If nothing comes up, it is a keyword engine in disguise.
- Synthesis. Ask “What trends emerge from our healthcare engagements?” If the tool lists PDFs without summarising, it does not understand your documents. It indexes them, that is all.
- Sources. Every claim must cite its source document with the exact passage. Without traceability, an answer is unusable in a professional context.
To go further: our complete guide to smart DMS explains what actually changes, and this article on fake smart DMS platforms details how to recognise them.
Frequently asked questions
Does semantic search replace keyword search?
It complements it. A good semantic engine also handles exact matches when relevant (an invoice number, a client name). Semantic search kicks in when exact terms are not enough, which is the case for the majority of business searches.
Does it work with all document types?
Yes. PDF, Word, Excel, PowerPoint, scans, document photos. Scanned files go through a text recognition step before being semantically analysed.
Is voice search reliable?
Yes. When you speak, you naturally ask richer questions than when typing ("What did we do for Dupont last year?" instead of "Dupont 2025"). Semantic search is precisely designed to understand this kind of complex query.
What is the difference from a standard chatbot?
A standard chatbot answers from its general knowledge. Semantic search answers from your documents, with exact sources. It is not a generic opinion. It is an answer grounded in your document base, verifiable passage by passage.
Do I need to reorganise my documents before switching to semantic search?
No. That is precisely the point. Semantic search works regardless of how your files are organised. Even if your folders are messy or your files are poorly named, the tool understands the content and retrieves it.