Managed retrieval-augmented generation
RAG as a service without surrendering control of quality
Launch and operate a dependable retrieval layer for your AI applications without assembling every connector, index, evaluation, and monitoring component from scratch.
Source-connected
Permission-aware
Measured before launch
01
What RAG as a service should provide
RAG as a service packages the recurring infrastructure required to connect applications to private knowledge. A serious service covers source ingestion, document lifecycle, indexing, retrieval APIs, permissions, citations, evaluation, monitoring, and support—not only an endpoint that returns generated text.
Morton Technologies designs managed RAG around your application and risk profile. You retain clarity about where information is stored, how retrieval is measured, which models are used, and how the service can evolve or be transferred.
Connectors and scheduled source synchronization
Parsing, chunking, metadata, versions, and deletion handling
Keyword, vector, hybrid, and reranked retrieval
Tenant, user, role, and document-level filtering
Cited generation or retrieval-only APIs
Evaluation datasets and regression testing
Quality, latency, usage, and cost monitoring
Operational support and documented exit options
02
Managed service or custom RAG build?
A managed service is useful when your team needs a supported retrieval capability, predictable operations, and a faster path to integration. A custom build may be the better fit when retrieval behavior, hosting, unusual data, extreme scale, or regulated controls are strongly differentiating.
We can assess both approaches. The decision should consider total operating cost, observability, data portability, permission complexity, evaluation access, vendor concentration, and the skills required after launch—not only prototype speed.
03
A service level built around quality
Define the retrieval contract
Specify sources, permissions, question types, evidence, latency, and failure behavior.
Prove the corpus
Ingest representative content and score retrieval and citations against reviewed questions.
Integrate the application
Connect identity, user experience, APIs, feedback, and operational logging.
Operate and improve
Monitor changes, failures, content gaps, cost, and evaluation regressions.
04
Questions to ask any RAG service provider
Can we inspect retrieved passages and ranking scores?
How are source updates, removals, and permission changes propagated?
Can we export our documents, metadata, evaluation set, and feedback?
Which logs may contain sensitive content and who can access them?
How are model, embedding, and indexing changes regression tested?
What happens when evidence is missing, conflicting, or low confidence?
Questions
Frequently asked questions
A practical next step
Bring us the workflow that is stuck—not a finished AI specification.
We will help clarify the opportunity, data, risks, and smallest useful way to prove value.