The situation
Employees need answers from approved operating documents without exposing restricted records.
Retrieval-Augmented Generation
RAG retrieves relevant material and supplies it as context to a language model. A useful system must evaluate both the retrieval step and the answer produced from it.
- Prepare governed sources
- Engineer retrieval
- Evaluate the answer path
Is this right for you?
When this is the right work.
You want an assistant to answer from approved enterprise knowledge while respecting access boundaries and acknowledging missing evidence.
The first conversation establishes the environment, the constraint and who will accept the result. We agree a bounded starting point before expanding the engagement.
Scope
What DotConn delivers.
- 01
Prepare governed sources
Identify permitted content, access rules, freshness and deletion behavior.
- 02
Engineer retrieval
Design ingestion, chunking, indexing and keyword or vector retrieval against actual questions.
- 03
Evaluate the answer path
Test grounding, relevance, source use, abstention and the experience when evidence is absent.
Deliverables
What you receive.
Acceptance criteria identify the checks, records and handover needed for the agreed scope.
- Source and retrieval architecture
- Ingestion and answer-generation implementation
- Retrieval and end-to-end evaluation set
In practice
A practical example.
Our approach
We preserve source permissions, build retrieval and evaluate answers alongside cases that should be refused or escalated.
The useful result
The result is an inspectable knowledge workflow with known limitations.
Responsibility
Agree the responsibility.
DotConn
Assesses and delivers the technical work in the agreed scope, with reviewable changes and handover. Continuing operation requires a separately defined managed-service scope.
Your team
Provides authorized access, business priorities and accountable decision-makers; approves policy choices and accepts the agreed result.
Set this boundary early
Retrieval does not guarantee a correct answer. Source quality, permission enforcement and evaluation remain part of the implementation and operating scope.
FAQ
Questions worth resolving.
Technical reading
Will uploading all documents make the system accurate?
No. Content quality, access rules, retrieval behavior and representative evaluation questions matter more than document volume alone.
Pricing, timing and how to prepare are covered in working with DotConn.
Connected work
Services that work alongside this.
Vector Database Implementation
Vector retrieval searches representations of data for similarity. A vector-database implementation also needs the right metadata, filters, update behavior and a way to evaluate whether retrieved results are useful.
ExploreEnterprise Search Implementation
Enterprise search connects business content to a usable retrieval experience. The work spans source ingestion, document structure, permissions, query behavior and the way relevance is evaluated.
ExploreProduction LLMOps
LLMOps manages changes and operations for language-model applications. Prompts, model choices, retrieval configuration and tool permissions can all affect behavior and need a reviewable release path.
ExploreAgentic AI Workflows
An agentic workflow uses a model to help coordinate a sequence of tasks and tools. Production design must define what the workflow can access, which actions it may take and when a person must decide.
Explore