Turning fragmented enterprise knowledge into a governed AI search experience
An enterprise retrieval augmented generation platform that connects approved documents, reports, databases and business content to a governed search and question answering experience. The solution retrieves relevant source content first, then uses a language model to produce answers grounded in that information.
Enterprise knowledge existed across too many systems for people to find it quickly.
Employees often had to search different document repositories, reports, databases and applications to answer one business question. Even when information existed, finding the correct and current source could take longer than producing the analysis itself.
What we found
- Business information was distributed across documents, reports, databases and application repositories.
- Different teams maintained their own knowledge sources and terminology.
- Employees relied on manual searches and subject matter experts for routine questions.
- Search results often returned documents without enough business context to identify the right answer.
- Access permissions had to be respected when bringing multiple information sources into one experience.
- Generic language models could generate fluent responses but could not be trusted as the sole source of enterprise facts.
- There was no consistent way to measure retrieval quality, answer quality and user adoption.
What the business needed
- A single experience for asking questions across approved enterprise information.
- Retrieval that could understand business language rather than relying only on keyword matching.
- Answers grounded in retrieved company content.
- Source references so users could verify important information.
- Access controls carried through from the underlying enterprise sources.
- A scalable architecture that could use Snowflake, Databricks or Fabric based on the data domain.
- Evaluation, monitoring and feedback mechanisms to improve the system over time.
We built a governed retrieval layer between enterprise information and the language model.
The model was not treated as the enterprise knowledge base. Relevant information was retrieved from approved sources and supplied as context before the response was generated.
Enterprise RAG platform
The platform brought structured and unstructured information into a common retrieval experience while preserving source level controls.
- Connected approved documents, reports, structured datasets and application content to the knowledge layer.
- Parsed and prepared content using document extraction, chunking, metadata enrichment and embedding generation.
- Created a semantic retrieval layer using vector search and metadata filters.
- Applied user identity and authorization rules before returning enterprise content.
- Used retrieval augmented generation to provide relevant context to the language model.
- Added response instructions to keep answers tied to retrieved information and reduce unsupported responses.
- Returned source references so users could trace answers back to approved enterprise content.
- Captured retrieval, response and user feedback metrics for continuous evaluation.
One retrieval experience can sit across the organization's modern data platforms.
The design allows different data domains to remain on their appropriate platform while exposing approved information through a common AI access layer.
A six stage approach to move from disconnected knowledge to governed enterprise AI search
The implementation started with a defined knowledge domain and expanded as retrieval quality, security and user value were demonstrated.
Assess
Identify high value knowledge sources, user groups, security requirements and priority questions.
Prepare
Clean, parse, classify and enrich documents and structured data before indexing.
Index
Create embeddings, metadata and vector indexes with source level access information.
Retrieve
Design semantic search, ranking and filtering to return the most relevant enterprise context.
Generate
Connect retrieval to an approved language model and apply grounding and response controls.
Operate
Monitor quality, latency, usage, cost and user feedback and improve the retrieval and response pipeline.
The value is measured by how quickly people can find trusted information and act on it.
Faster information retrieval
Semantic retrieval can reduce the time users spend manually searching across multiple enterprise repositories.
Lower repeated research effort
Routine questions can be answered through a common knowledge experience instead of repeated manual investigation.
Faster first response
Users can reach relevant information faster when retrieval and response generation are combined in one workflow.
Controlled source access
Enterprise content can be exposed through the AI layer only after defined identity and authorization checks.
Lower analyst interruption
Routine information requests can be handled through the knowledge experience before being escalated to specialist teams.
Faster knowledge onboarding
New users can access structured enterprise knowledge without relying entirely on informal team guidance.
Enterprise knowledge becomes easier to access without giving up control over the underlying data.
The platform creates a practical foundation for internal AI applications while keeping enterprise information, identity and governance at the center of the design.
Faster Decisions
Employees spend less time locating information and more time evaluating the business decision that depends on it.
Lower Research Effort
Repeated questions and routine information requests can be handled through one governed experience.
Better Knowledge Access
Information held by different functions can become searchable through a common enterprise interface.
Controlled Enterprise AI
Security, permissions, source references, evaluation and monitoring are built into the AI workflow rather than added later.
Turn fragmented enterprise knowledge into a trusted AI experience.
Connect approved data and documents across Snowflake, Databricks and Fabric with semantic retrieval, governed access and grounded AI responses.
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