Private knowledge · Permission-aware retrieval · Cited answers
RAG systems that turn approved documents into verifiable answers
Morton Technologies builds retrieval-augmented generation systems for organizations that need to search, summarize, and work from private knowledge. We connect document ingestion, access controls, retrieval, citations, evaluation, and user feedback into one supportable application.
When RAG is useful
Help people find and apply information spread across private documents
RAG is appropriate when answers depend on material that is private, frequently updated, too large for a single prompt, or different for each user or account. Instead of relying only on what a model learned during training, the application retrieves relevant approved sources and supplies them as context for the response.
That pattern is useful for policies, procedures, manuals, contracts, project files, support material, professional knowledge, and product documentation. It is not a replacement for a transactional database, deterministic calculation, or formal approval process.
Strong candidate characteristics
- Users repeatedly search the same document collections
- Answers need direct links to supporting sources
- Access differs by role, customer, matter, or project
- Documents change and stale answers create risk
- Current search returns files but not usable answers
- Subject-matter experts spend time answering routine questions
RAG architecture
The retrieval pipeline matters as much as the model
A useful knowledge system needs reliable content processing, access-aware retrieval, answer constraints, and measurable quality. We design each layer around the documents, users, and decisions involved.
Document processing
Collect files from approved sources; extract text and metadata; handle OCR where needed; identify document versions; and report processing failures.
Chunking and metadata
Divide content along meaningful boundaries and preserve titles, sections, dates, owners, accounts, document types, and access attributes.
Hybrid search
Combine semantic retrieval, exact matching, metadata filters, structured queries, and reranking based on how users actually ask questions.
Grounded answers
Constrain responses to retrieved material, request structured outputs when useful, expose citations, and decline when evidence is insufficient.
Permissions and isolation
Apply identity, tenant, role, project, matter, or document-level access before retrieval so unauthorized content never becomes model context.
Evaluation and feedback
Test representative questions, source relevance, answer support, citation accuracy, refusal behavior, latency, and cost as the system evolves.
Quality and governance
Measure whether the answer is supported—not whether it sounds convincing
Fluent output is not proof of accuracy. We create evaluation examples from real questions and documents, then assess whether the correct sources were retrieved and whether the response is supported by them.
- Retrieval relevance and coverage
- Answer faithfulness to retrieved content
- Citation correctness and source visibility
- Appropriate refusal when evidence is missing
- Permission and tenant-isolation tests
- Regression checks after content, prompt, or model changes
- User feedback and corrected-answer workflows
Knowledge workflows
Common applications for private RAG
Internal policy assistant
Answer employee questions from approved policies and procedures while citing the current document and section.
Project or matter knowledge
Search documents, correspondence, notes, and deliverables within the user’s authorized project, customer, or matter.
Product and support knowledge
Help staff or customers navigate manuals, implementation guidance, troubleshooting content, and account-specific records.
RAG versus broader AI integration
Use the right service for the problem
This page focuses on document retrieval and knowledge systems. If the requirement includes extracting fields, classifying submissions, drafting inside an existing application, recommending next steps, or executing controlled workflow actions, review our broader AI integration service.
A practical starting engagement
- Select one document collection and user group.
- Gather representative questions and expected sources.
- Confirm permissions, freshness, and document ownership.
- Prototype retrieval and cited-answer behavior.
- Evaluate quality before expanding scope or automation.
Start with your documents and questions
Can users find the right source quickly enough today?
We can evaluate the document collection, permissions, query patterns, retrieval approach, quality criteria, and smallest useful RAG implementation.
Discuss a Private Knowledge System