AI-Powered Enterprise Search
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A knowledge-management client
A 12,000-person organization had knowledge spread across 40+ systems - wikis, ticketing, drives, code, chat archives - with no unified search. New hires lost their first month rediscovering things that already existed. I built a semantic search platform that connectors-pull from each source, embeds the content into a vector store, applies row-level access controls per system, and answers natural-language questions with cited results. The hard problems were not retrieval quality - they were permissions, freshness, and trust.
This is a representative architecture study based on real project patterns. Specific metrics and client details have been generalized to protect confidentiality.
Results
What changed, in numbers
The metrics the engagement is measured by.
85%
Search Time
reduction in time-to-find
40+
Systems Connected
enterprise systems indexed
94%
Query Success
of searches find relevant results
15K+
Adoption
daily active users
Challenge
What was broken
Knowledge silos at scale. Asking 'have we ever solved X' could take a week and three Slack channels. Off-the-shelf enterprise search couldn't honor the per-system access controls, so it either over-shared (a compliance fire) or under-shared (useless). Permissions changed daily, content changed hourly, and people would stop trusting the tool the first time it returned a stale or unauthorized result.
Solution
The shape of the fix
A semantic search platform that indexes 40+ enterprise systems, honors per-system permissions at query time, blends lexical and vector retrieval, and returns AI-generated answers with citations - so users can verify before they trust.
Approach
How I tackled it
The concrete moves that took the project from broken to shipped.
Built source-specific connectors that respected each system's native ACLs at query time, not at index time
Used semantic chunking with embedding models tuned for the corpus, not generic web embeddings
Mixed lexical and vector retrieval with a learned re-ranker so exact-match queries still worked
Streamed near-real-time updates so a wiki edit was searchable within minutes
Added per-result citations so users could verify before they trusted
Personalized rankings based on team and recent-work signals without leaking access boundaries
Outcomes
What shipped, and what it changed
Measured results from the engagement, told as a story rather than a scoreboard.
Reduced average time-to-find on tracked queries by 85%
Indexed 40+ enterprise systems with continuous near-real-time updates
Reached 94% query-success rate on a held-out evaluation set
15,000 daily active users within six months of internal rollout
Cut 'has anyone done this before' Slack threads by an estimated 60%
Stack
Technologies used
Linked entries open the technology page with related studies, playbooks, and notes.
Services
How I helped
The specific services involved in this engagement. Each links to a deeper breakdown.
Lessons
What I would tell the next team
The takeaways I carry into every similar engagement.
Permissions at query time is the only correct answer in the enterprise. Index-time ACLs go stale and become incidents
Citations are the difference between a search tool and a chatbot. Users will tolerate wrong answers if they can verify
The freshness budget matters more than the ranking algorithm. Stale results train users to leave
Related
Other studies you might recognize
Engagements with overlapping problem shapes, industries, or stacks.
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