CASE STUDY / AI / EXPERIMENTAL
Knowledge & AI infrastructure.
Exploring local models and retrieval as an auditable knowledge-engineering workflow.
Personal exploration · not presented as a production deployment
ARCHITECTURE / CONCEPTUAL FLOW
01 / ENGINEERING NOTES
Problem & context
Operational knowledge is spread across documents and systems; retrieval needs provenance and access boundaries to be trustworthy.
Exploration of local LLMs, vector search and knowledge ingestion with integrations such as Confluence, Jira and ServiceNow.
02 / ENGINEERING NOTES
Constraints
Document freshness, authorization, hallucination, retrieval relevance, latency and local compute limits.
03 / ENGINEERING NOTES
Engineering decisions
Keep source identifiers with chunks so responses can be traced back.
Separate ingestion permissions from query-time tool access.
04 / ENGINEERING NOTES
Reliability & data flow
Treat empty retrieval, stale documents and tool errors as explicit outcomes, not plausible answers.
Approved source → extraction and chunking → embeddings → vector index → retrieval → cited response.
05 / ENGINEERING NOTES
Operational model
Evaluate retrieval quality, monitor ingestion failures and keep sensitive sources behind explicit access checks.
06 / ENGINEERING NOTES
Trade-offs
Local inference improves control but can increase latency and constrain model choice; retrieval adds maintenance costs.
07 / ENGINEERING NOTES
Outcome & next steps
A hands-on architecture exploration, not a claim of a deployed enterprise AI platform.
Build a repeatable evaluation set and test permission-aware retrieval before broader use.
08 / ENGINEERING NOTES
Key lesson
A useful answer needs a source, a confidence boundary and a path to correction.