AI Solutions · Example engagement
A grounded AI assistant for an operations team
Enterprise operations team · thousands of runbooks · strict access rules
Engineers lost time searching runbooks and wikis during incidents. A chat assistant had to answer only from approved sources and respect existing permissions.
The challenge
Where things stood.
- Knowledge spread across wikis, tickets and documents of mixed quality.
- Some documents restricted to specific teams.
- No way to tell whether an AI answer was grounded or invented.
Our approach
How we would solve it.
- 01
Govern the sources
Select approved content, keep source permissions and define freshness and deletion behaviour.
- 02
Engineer retrieval
Chunking, hybrid keyword and vector search, and metadata filters tuned against real incident questions.
- 03
Evaluate before launch
An evaluation set covering correct answers, refusals and missing evidence, run on every change.
- 04
Operate it like a product
Prompt and model versioning, quality and cost monitoring, and a feedback loop from users.
Deliverables
What the client receives.
Services involved:
- Source and retrieval architecture
- Assistant with citations and permission-aware retrieval
- Retrieval and end-to-end evaluation set
- LLMOps monitoring and release process
Outcomes
What success looks like.
- Every answer cites the source it used
- Restricted content never appears for the wrong user
- Quality is measured on each release, not assumed
- Cost per answer is visible and governed
Example engagement: an illustrative scenario showing how DotConn approaches this kind of work. Client details and figures are not shown.
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