A professional services firm approached Digital is Simple when the volume of internal documentation, process guides, and institutional knowledge had grown too large for employees to navigate efficiently. Teams were spending significant time searching across disparate systems for answers that already existed somewhere in the organization. Our team assessed the knowledge landscape, identified a Generative AI copilot as the right fit, and guided the client through the architecture, data strategy, and deployment approach needed to put that knowledge to work.
IT Services / SaaS
Generative AI Development
We began by auditing the client’s existing documentation repositories, identifying where knowledge lived, how employees typically searched for it, and where the biggest gaps in retrieval were occurring.
Based on our assessment, we recommended a retrieval-augmented generation (RAG) architecture built on the client’s own content, rather than a generic chatbot that would lack the organisational context of employees needed. This fell squarely within our Generative AI development services, which we scoped to fit the client’s existing tooling.
We outlined what content needed to be indexed, how access permissions should be respected within the copilot’s responses, and what data hygiene steps were required before ingestion could begin.
We handed the client a structured plan covering model selection, embedding pipeline setup, interface integration, and a phased rollout, giving them a clear path from scattered documentation to a working internal copilot.
The client’s documentation was distributed across shared drives, a legacy intranet, email threads, and departmental wikis with no consistent structure. A core part of our consultancy was helping the client understand what would need to be normalised before any AI layer could reliably surface it.
Different teams had access to different categories of documentation. We advised how the retrieval layer could respect those permissions, so the copilot did not inadvertently surface restricted content to unauthorised users, a requirement that shaped the entire architecture recommendation.
Knowledge management touches nearly every department, and stakeholder expectations varied significantly. We helped the client establish a focused initial scope that would deliver visible value quickly, while leaving a clear path to expand coverage over time. Experts at our AI development company helped the client treat this as a strategic capability rather than a one-off tool deployment.
