H1ST-SUP-TKT-001
The same question shouldn't become a ticket every time
Most of what users ask, they could answer themselves, if the answer were findable and trustworthy. The agent lets users ask in natural language and returns grounded answers drawn from curated FAQs, troubleshooting guides, and platform documentation using retrieval-augmented generation, with each answer citing its source articles so users can verify it, and unresolved questions flowing into a ticket. Common issues resolve without waiting on support, cutting the volume that reaches your team.
The grind you know
Users keep raising the same questions because the answers are buried in scattered documents no one can actually find. So your support team spends its day on low-complexity tickets that self-service should have handled. The static FAQ page was supposed to help, but it goes stale and rarely matches how people actually phrase their problems.
Part of the Study Support Desk family
AI-triaged help desk for every user.
Most of what users ask, they could answer themselves, if the answer were findable and trustworthy. The agent lets users ask in natural language and returns grounded answers drawn from curated FAQs, troubleshooting guides, and platform documentation using retrieval-augmented generation, with each answer citing its source articles so users can verify it, and unresolved questions flowing into a ticket. Common issues resolve without waiting on support, cutting the volume that reaches your team.
Explore the Study Support Desk familyUsers pose a question in natural language describing their issue.
The agent searches the knowledge library and synthesizes a grounded, cited answer.
If the answer does not resolve the issue, the user opens a ticket pre-filled with the question and context.
Capabilities
Natural-language questions retrieve and synthesize answers grounded in the curated knowledge library.
Responses cite the underlying articles so users can verify and read further.
Step-by-step guides walk users through resolving common platform issues on their own.
When self-service falls short, the agent hands the question to the ticketing workflow with context attached.
In practice
Where the agent shows up in the day-to-day of a live trial — the moments the grind usually lives in.
A user asks how to reset a data-entry lock, a question support fields weekly. RAG search returns a grounded answer from the curated library with cited source articles, resolving it without a ticket.
A site user is unsure whether to trust the response. Because each answer cites the underlying article, they read further to confirm it rather than misinterpreting a black-box reply.
A question the library can't resolve comes in. The agent hands off to the ticketing workflow, pre-filling a ticket with the question and context, and the unanswered query feeds a content-gap report.
Proof
Figures shown are pre-launch targets based on internal benchmarks, not guaranteed outcomes.
Gemini / Anthropic LLM
LLM
A ticketing system for users to submit, track, and resolve platform issues across the study team.
Learn moreAI-based ticket prioritization and routing that assigns urgency and directs each issue to the right team.
Learn moreResponse and resolution time tracking with automatic escalation against defined service-level agreements.
Learn morePeace of mind
Every output is generated inside a validated, audit-ready platform, kept under human-in-the-loop control, and mapped to the regulatory and CDISC standards this agent supports.
Walk through it on your own workflow with a clinical-trials expert — no pressure, no obligation, and honest answers, including on the limits.
