/03 · Case study
Turning Company Knowledge Into Answers People Can Actually Find
AI knowledge retrieval · Semantic search · Hybrid retrieval
- Evidence shown
- Interface reconstructed from the delivered workflow
- Data
- Synthetic sample data, no client information
- Impact reporting
- Technical outcomes only where business impact was not measured
Open full size ↗01 / Challenge
The challenge
Most businesses do not have a shortage of information. They have a problem finding the right information when it matters.
Traditional search depends heavily on terminology. A user might ask ‘I can’t get into my account’ while the relevant article says ‘How to reset forgotten login credentials’. Different words, same problem. The system needed to understand meaning without losing exact terminology where it mattered.
02 / Build
What we built
We extended an existing knowledge platform with semantic retrieval using OpenAI embeddings and Azure Cosmos DB vector search.
Stored knowledge could be compared with a new question by semantic similarity. Conventional keyword signals remained available for product names, internal terms and specific policies. The system also introduced question suggestions and foundations for capturing search interactions.
03 / Workflow
How it works
- 01Natural-language question
- 02Question converted into an embedding
- 03Semantic and conventional search
- 04Existing knowledge evaluated
- 05Candidate answers ranked
- 06Relevant knowledge returned
04 / Evidence
Outcomes
Exact wording became natural language, semantic ranking and relevant existing knowledge.
The system introduced semantic retrieval across a structured knowledge base, matching questions by meaning rather than exact wording alone.
The architecture supported hybrid ranking, with semantic and conventional search signals contributing to relevance.
The embedding layer represented queries with 1,536-dimensional vectors and returned multiple ranked candidates instead of one brittle match.
Foundations were added for measuring queries, selected results and result position. Reduced support cost or employee hours were not formally measured.
05 / Value
Why it matters
A company can spend years accumulating knowledge and still pay employees to rediscover it.
Every folder search, repeated question and recreated answer makes institutional knowledge less valuable. AI earns its place here by changing how people access information the company already owns.
06 / Pattern
Where else it applies
Authentication and permissions can limit each user to the knowledge appropriate for their role.
Could this work here?
Is your knowledge problem actually a retrieval problem?
If important information already exists but employees still have to hunt for it or ask somebody else, the access layer may be the real bottleneck.
Talk to us about your knowledge workflow