Capability guide
Agentic workflows, RAG and vector retrieval solve different parts of an AI system. Start with the task, permitted knowledge and human decisions, then evaluate whether the resulting behavior is useful enough for the intended risk.
Build around the workflow and its boundaries. The right starting point depends on what already exists, which decisions are open and the people who will operate or use the result.
Build multi-step agents that orchestrate APIs, enterprise tools and business logic, with explicit permissions and human decision boundaries.
Ground language models in enterprise knowledge through secure retrieval-augmented generation pipelines.
Design architecture, indexing, hybrid search tuning and scaling for enterprise retrieval.
A focused assessment can establish the unknowns. Implementation delivers an agreed change. Ongoing services and embedded specialists require their own responsibility and coverage terms.
Choose the topic you need to inspect. Each page explains the specific work, a practical example, deliverables and its operating boundaries.
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.
A repeated business workflow spans knowledge, APIs and decisions, and a single chat response cannot complete it usefully.
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.
You want an assistant to answer from approved enterprise knowledge while respecting access boundaries and acknowledging missing evidence.
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.
A prototype retrieval index needs a production architecture, or similarity results are not aligned with business queries and permissions.
The exact outputs follow the engagement. This capability group can include workflow boundaries, retrieval architecture, evaluation dataset.
Agree the acceptance evidence, repository or record location and next operating owner before the work begins. Exceptions and unfinished dependencies remain visible in the handover.
Coordinates the agreed assessment, implementation or service activities, directly or with the required specialist support. The scope defines the decisions, checks and records to be delivered.
Provides business priorities, authorized access and accountable reviewers. Customer-directed individual staffing remains distinct from a DotConn-coordinated team or managed service.
AI evaluation reduces uncertainty; it does not guarantee correct answers. Production application AI is distinct from workplace AI access and governance in Managed TechOps.
No. A deterministic workflow or existing search platform may fit. Choose components because they serve the task, not because they belong in a fashionable stack.
Yes. Start with a bounded requirement, then connect adjacent work only when the dependencies or intended outcome justify it. Your enquiry can name the specific capability and current constraint.