Keyword search misses meaning
Searching "leave carry forward" won't find a policy that says "unused annual holiday" unless the words match.
AI & Automation · Search
AI enterprise search that answers with sources you can open
We build search over your policies, manuals, contracts, tickets and records that understands what people mean, respects who is allowed to see what, and backs every generated answer with links to the passages it came from.
What is AI Search?
AI enterprise search is search across an organization's documents and systems that uses embeddings to match the meaning of a query rather than only its keywords, often combined with retrieval-augmented generation (RAG) to compose a direct answer from the retrieved passages. Each answer should cite the sources it used so readers can verify it. Search quality depends far more on how content is prepared, chunked, permissioned and kept current than on the language model that writes the answer.
Staff spend time hunting for the latest version of a policy, a clause in a contract or the fix for a recurring fault, because keyword search only finds exact words and knowledge is split across drives, wikis, email and the ERP. Simply pointing a chatbot at all of it creates new problems: outdated documents get quoted, confidential files leak to the wrong people, and answers arrive without any way to check them.
Why staff still can't find what they need
Knowledge in many places
Shared drives, wikis, PDFs, helpdesk tickets and ERP records each hold part of the answer.
Permissions get ignored
A search index that doesn't respect source access rights can expose salary files or board papers to everyone.
Stale and duplicate documents
Old versions sit beside current ones, and retrieval will happily use whichever matches best.
Our AI Search service
Content audit and connectors
Identifying authoritative sources, removing or flagging outdated material, and building connectors that sync documents and records on a schedule.
Chunking and indexing
Splitting documents into meaningful sections with titles and metadata, generating embeddings and storing them in a vector index alongside a keyword index.
Hybrid retrieval and ranking
Combining semantic and keyword search, with re-ranking and metadata filters such as department, document type or date.
Cited answer generation
A language model composes answers only from retrieved passages, cites each source and says when the content doesn't contain an answer.
Permission-aware results
Access rights from source systems carried into the index, so users only retrieve content they are allowed to open.
Retrieval evaluation
A test set of real questions with known source passages, used to measure retrieval and answer quality before and after every change.
Included capabilities
- Semantic and keyword hybrid search
- Cited answers with links to passages
- "Not found" responses instead of guesses
- Source-level access control
- Metadata filters by team, type and date
- Scheduled sync from source systems
- Version awareness for documents
- Feedback on answers and results
- Retrieval quality test suite
What AI Search changes for your business
Less time searching
People find relevant passages by meaning, even when they don't know the document's wording.
Verifiable answers
Citations let readers open the source and confirm the answer for themselves.
Access rules preserved
Search respects the same permissions as the original systems.
Content gaps made visible
Questions that return nothing show where documentation is missing or unclear.
Where it makes the biggest difference
Policy and procedure lookup
Employees ask about HR, finance or safety procedures and get a short answer with links to the exact section of the current policy.
Technical documentation search
Engineers and field staff search manuals, specifications and past fault reports by describing the problem in their own words.
Contract and legal research
Teams locate clauses across many contracts and see each match in context, with the review left to qualified staff.
Customer-facing help center
Site search that finds the right help article from a customer's description, as the retrieval layer behind a chatbot or support tool.
Seven steps from question list to maintained search
Retrieval is measured and tuned before answer generation is switched on, because a model can only be as good as the passages it receives.
Gather questions
Collect real questions staff or customers ask, and identify where the correct answers live.
Audit sources
Decide which sources are authoritative, how they are permissioned and how often they change.
Build the index
Connect sources, chunk and embed content, and set up hybrid retrieval with metadata.
Evaluate retrieval
Measure whether the right passages are found for each test question, and tune chunking and ranking.
Add answer generation
Introduce cited answers once retrieval is reliable, and test for unsupported statements.
Pilot
Release to one department, gather feedback on bad results and fill content gaps at the source.
Maintain
Monitor sync jobs, feedback and test results as content and models change.
What it connects to
AI Search
- Google Drive and Microsoft SharePoint
- Confluence and internal wikis
- Odoo Documents and Knowledge records
- Helpdesk ticket history
- PDF and scanned document archives
- Vector databases and pgvector
- Single sign-on identity providers
Built with
RAG or fine-tuning
| Criterion | Retrieval-augmented generation (RAG) | Fine-tuning |
|---|---|---|
| How knowledge is added | Documents retrieved at question time | Model weights trained on examples |
| Keeping content current | Re-index changed documents | Retrain the model |
| Citations | Yes, from retrieved passages | Not inherently |
| Access control | Enforced at retrieval | Difficult once knowledge is in the model |
| Best for | Answering from changing documents | Teaching a consistent style or task format |
Security, quality and scale
Permission sync
Document-level access is mirrored from source systems and checked at query time.
Data residency options
Index and models can run in your cloud account or on your servers if content must stay in-house.
Prompt injection awareness
Retrieved text is treated as data, with safeguards so instructions inside documents don't override system rules.
Query and access logs
Searches and answers are logged under retention rules for audit and quality review.
What working with us looks like
Retrieval first, generation second
We prove the system finds the right passages before adding a model that writes answers.
Measured relevance
We build an evaluation set from your real questions, so improvements are demonstrated rather than claimed.
Odoo and business systems
We can index ERP records and Odoo Documents alongside files, which most generic tools don't reach.
Permissions taken seriously
Access control is designed into the index from day one, not patched later.
What it costs
Fixed-price packages with the scope written down, paid stage by stage. Your project goes live once it's fully paid.
Related services, technologies and industries
Sources and further reading
Official documentation and guidance if you want to read more about this topic.
AI Search: questions we hear most
What is RAG and why does it matter for search?
Retrieval-augmented generation (RAG) means the system first retrieves relevant passages from your content and then asks a language model to answer using only those passages. It keeps answers tied to your current documents and makes citations possible, which a model answering from its general training cannot do.
Can AI search still give wrong answers?
Yes. If retrieval returns the wrong passage or the model misreads it, the answer can be wrong, which is why we show citations, allow "not found" responses and evaluate with a test set of real questions. Users should check the cited source for anything consequential.
Will employees see documents they shouldn't?
Not if permissions are designed correctly. We carry access rights from source systems into the index and filter results at query time, so a user retrieves only what they could already open.
Should we fine-tune a model on our documents instead?
For answering questions from documents that change, RAG is usually the better choice because it stays current, cites sources and enforces permissions. Fine-tuning is more useful for teaching a model a specific style or output format.
Which vector database do you use?
It depends on scale and your existing stack. For many business deployments, PostgreSQL with the pgvector extension is sufficient and keeps everything in one database; dedicated vector databases are considered when volume or features require them.
What affects the cost of AI search?
The main drivers are the number and complexity of sources to connect, content volume to index, permission requirements, query volume and the models used for embeddings and answers. We estimate after the source audit.
Next step
List the questions your team keeps asking
Share a dozen real questions and where you think the answers are stored. We'll check whether AI search can find them reliably and what needs cleaning up first.