Retrieval pipeline
Connections that bring in approved documents, build the search index, and carry later edits and deletions through to it.
LLM and knowledge systems
We build internal search and assistants that answer questions from your own documents. Each answer cites its sources and respects document permissions.
ScopePrototype through production release
Delivery timingDays for a focused prototype, weeks for a well-defined release when access and decisions are ready
How we check itWe test that search finds the right sources, answers match them, and the system declines questions it should not answer.
Answers people can check
People need to see where an answer came from before they rely on it. The system we build finds the right passages first and answers only from them. It stays current as documents change and says when information is missing or conflicting.
Good fit
- Organizations whose knowledge is spread across documents and tools
- Support, legal, finance, and operations teams that answer questions from documents
- Software products that need answers based on specific source material
Not the right fit
- Document collections no one owns or keeps current
- Search problems that better tags or filters would solve without AI
- Uses where answers can’t show the documents they came from
What you get
Answers with citations
A search and answer interface where each answer links to its sources and people see only what they are allowed to open.
Quality and upkeep
Test questions with known answers, checks for outdated or missing documents, and a way for users to report wrong answers.
How it works
Start with the documents
We go through the collection with its owners: who maintains each source, its formats, access rules, conflicting versions, and how often it changes.
Build retrieval first
We test whether search finds the right passages before judging the answers the model writes from them.
Evaluate and integrate
We check source links, questions the documents cannot answer, and restricted documents before connecting search to your application.
Scope, cost, and ownership
What we need from you
- A document collection with clear access rules
- Document owners, plus typical questions and the sources that should answer them
What affects cost
- Number of sources, file formats, and how often they change
- Permission rules and connections to each source
- Required answer quality and the number of test questions
Technical scope
- Document parsing
- Hybrid retrieval (keyword and vector search)
- Permission filtering
- Source citations in the interface
- Passage selection for the model’s
- Answer quality testing
Support and maintenance
Your document owners keep the source material current. The maintenance plan covers failed imports, missing citations, access changes, and testing when a model changes.
Common questions
Do we need a vector database?
Not always. Keyword search is enough for some collections; others need vector search or both. We test retrieval with your documents and questions before choosing.
How do documents stay current?
Through a scheduled import for each source. We agree how often each one runs and how edits and deletions reach the search index. Failed imports trigger an alert so outdated material doesn’t go unnoticed.
How do you keep internal AI search within document permissions?
We filter by permission before the model sees anything. The search layer checks which documents each user may open and passes only those passages to the model. We test access changes and deleted documents, not just the first import. The application has to enforce these restrictions, because the model cannot.