70%
faster lookup
Median time to find an answer, measured in agent screen recordings.
Sample project · Paperkite
A knowledge assistant for 2,000 support agents
Sample project: an illustration of how we approach this kind of work, not a real client engagement.
Industry
AI · Retrieval-based support assistant
Timeline
10 weeks
Team
1–2 senior engineers
Stack
- TypeScript
- OpenAI-compatible LLMs
- pgvector
- Next.js
- Azure
01 · The challenge
What was getting in the way
Paperkite’s support agents searched six tools to answer one customer question: a wiki, two folders of policy PDFs, a ticket archive, release notes and a chat history. New hires needed months before they could find answers quickly.
An earlier chatbot experiment had been shelved because it answered confidently from outdated policies and could not show where an answer came from.
02 · Our solution
What we built and how
We indexed policies, resolved tickets and release notes with team-level access rules, and built a retrieval pipeline that returns an answer only when supporting sources are found. Every answer carries citations that open the original document at the right paragraph.
Documents carry owners and review dates, so stale content is flagged and ranked lower. An evaluation set of 400 real questions ran on every change, which let the team improve answer quality without guessing.
03 · Results
What changed after Paperkite went live.
Sample figures for this illustrative case study, measured against the period before launch.
2,000
agents using it
Weekly active agents within three months of rollout.
93%
cited answers
Answers backed by at least one source the agent could open.
−35%
onboarding time
Time for a new agent to reach the average handling-time target.
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