Give the response a useful connection to your knowledge
A business holds information in many forms: guidance, product records, policies, reference material and the knowledge people use to make decisions. An AI response needs a considered way to work with the relevant parts.
Retrieval-augmented generation, or RAG, connects an information-retrieval step with generation. Selected source material provides context for a response rather than leaving the model to rely only on its training.
We develop those connections for internal and customer-facing experiences, with attention to source quality, permissions, relevance and how people can inspect the result.
Begin with the questions the knowledge needs to answer
We work with your team to understand what people need to find out and which sources should govern the answer. What is current? Who owns it? Where are the gaps or conflicting versions?
Knowledge Base Development, Content Strategy and Content Operations already provide homes for that work. Preparing knowledge for AI is connected to those responsibilities, not a replacement for them.
More documents do not automatically create better understanding. Useful context includes the product, version, date, audience and conditions that make information applicable.
A source is useful when it answers the right question in the right context.
Retrieve what the person is allowed to use
The implementation needs to find relevant material while respecting access rules before it is supplied to the model.
We consider how content is divided for retrieval without losing necessary context, which metadata helps identify the right material and how search or ranking approaches should be evaluated. Keyword, vector and combined retrieval are options to examine, not requirements imposed on every project.
Search Development and Integration supplies the broader retrieval discipline. Connecting retrieval with generation is the particular concern of this page; it is not the same task as training or fine-tuning a model.
Follow an answer back to its basis
When we help a team answer questions about care for an equipment component, we examine how the system identifies the correct product and revision.
Does the retrieved guidance apply to this component? Is a newer instruction available? What happens if two sources conflict? Can the person inspect the relevant passage and understand what it supports?
We shape the response behavior around those questions. Missing or conflicting information should remain visible rather than be smoothed into an invented agreement. References help people check an answer; they do not by themselves establish that it is correct.
Your team's subject knowledge helps us evaluate both the retrieved material and the explanation made from it.
Keep changing information connected
Published content, permissions and records change. We plan how updates, unpublishing and deletion reach the retrieval system and how those changes are checked.
Some questions need a current query to an operational system rather than an indexed document. Prices, inventory or order status may require an authorized live connection. AI Integration helps connect that information without confusing access to a record with authority to change it.
Source text is treated as information, not an instruction that can override the application's rules or expand its permissions.
Test the retrieval and the response
AI Evaluation and Testing examines whether the right material is found and whether the generated answer uses it appropriately. Those are related but distinct questions.
We include ambiguous requests, outdated material, restricted information and cases where the source does not support an answer. Response time, operating cost and the effort required to review outputs also affect usefulness.
Our process follows Discover → Define → Explore → Design / Develop / Market → Learn → Advance → Discover. Responsible AI Implementation connects the system with ownership and controls; AI Assistant Development gives the capability a usable role where an assistant is the right expression.
Bring us the questions people ask and the knowledge they need to work with. We will help shape the connection between finding information and using it well.