Answers from your own documents, with the source shown.
One question, one answer, one citation. Across folders, wikis, tickets, email and databases, with the permissions your systems already enforce. Retrieval done properly: chunking that respects structure, hybrid search, reranking, and context assembled for the model rather than dumped on it.
Zingaro AI builds retrieval-augmented generation and enterprise search systems: document ingestion and chunking, hybrid retrieval with reranking, permission-aware access, context engineering for accuracy, citations on every answer, and evaluation against real questions.
You get
- A search and answer system on your content
- Citations on every answer
- Permission-aware access
- An evaluation set of real questions
Built with
- Structure-aware chunking
- Hybrid retrieval
- Reranking
- Context engineering
- Permission filters
Knowledge systems, RAG and enterprise search, end to end.
Ingestion and structure
PDFs, scans, wikis, tickets, email and tables parsed with their structure kept, not flattened.
Retrieval that works
Hybrid search, reranking and query rewriting, tuned on your questions, measured on your answers.
Context engineering
The right passages, in the right order, with the right instructions. Most accuracy lives here.
Permissions
Who may see what carries through from your document systems to every answer.
Citations and abstention
Every answer shows its source. No source, no answer, a person instead.
Evaluation
Real questions, graded answers, run before every change to the index or the model.
From one painful job to a system in production.
Models and agents handle the volume. People handle the edges. You always know which did what.
- 01
Discover.
One or two weeks with your team. The jobs listed, sized and ranked. A number on the first one.
- 02
Build the pilot.
Fixed scope, fixed fee, 4 to 6 weeks. Real output on your real data, measured against the number.
- 03
Ship it.
Into production, inside your boundary if the rules require, with evals gating every release.
- 04
Run it, or hand it over.
We operate it with people on the queue and a weekly report, or your team takes it with the runbooks.
Your data does not have to leave the building.
On your servers
Air-gapped where required.
We install on machines you own, inside your network. Where the rules demand it, the system runs with no outbound connection and updates are carried in by hand. Your team keeps the keys.
Data stays inside your network
Your private cloud
We deploy into your account.
We deploy into your own cloud account, in your region, under your access controls. The data stays in your account. We get the access you grant, and nothing more.
Data stays inside your account
Ours
Managed, fastest to start.
We run it on infrastructure we operate. The right choice when the data is allowed to leave and you want the pilot running this month.
Managed by us, on our terms
In banking, insurance and healthcare, the rules decide where the data sits. We built for that first.
How each option worksWhat comes back, and how we measure it.
Deliverables
- A search and answer system on your content
- Citations on every answer
- Permission-aware access
- An evaluation set of real questions
Measured by
- Answered with a correct source
- Questions abstained and escalated
- Time to answer
- Coverage of the document set
Real figures come from your pilot. We do not publish invented ones.
What people ask about knowledge.
Is RAG still the right approach with long-context models?
Usually, for cost, freshness and permissions. Long context helps within a document; retrieval decides which documents. We combine them.
Can it run on-prem?
Yes. Index, models and application can all sit inside your boundary.
How do you stop wrong answers?
Grounding, citations, abstention rules and an evaluation set of real questions run on every change.
Bring us the knowledge job you keep postponing.
Twenty minutes is enough to say whether we can take it.
A pilot starts within 5 working days of agreed scope · Nothing upfront · No seat licences