Enterprise Knowledge Search
Arrive needed to work across fragmented post-merger HR, CRM, project, and messaging systems without waiting on a long ETL program, because information silos were slowing RFP work, onboarding, and day-to-day collaboration.
Reported impact
Arrive built its AI retrieval layer in two weeks, cut RFP research from hours to minutes, improved onboarding and cross-team collaboration, and created a scalable alternative to separate AI add-ons across each legacy system.
Arrive used Gemini Enterprise and Gemini Enterprise Agent Platform to build Aria, an AI retrieval and orchestration layer that sits across siloed internal systems created by multiple acquisitions. Instead of migrating all data first, the company configured Aria to query live backend sources across HR systems, CRM platforms, project tools, messaging systems, and other internal repositories, then return grounded answers through a single conversational interface. Gemini handles the natural-language reasoning, Agent Search points requests toward verified internal sources, and prompt engineering bridges inconsistent schemas so the model can interpret disparate data structures correctly. The team monitors performance with automated logging and employee feedback as new companies and systems are brought into the environment. This design let Arrive deliver a production virtual agent in two weeks and turn hours of manual RFP and onboarding research into minutes.
Employees ask Aria natural-language questions, Gemini interprets the request, the agent layer queries live HR, CRM, project, and messaging systems, Agent Search grounds the response in verified internal sources, and the answer is returned through a single conversational interface while logs and feedback tune performance over time.
Enabled development of an AI retrieval layer in two weeks
We used Gemini Enterprise Agent Platform, which provided the foundational platform, and combined it with Gemini's advanced natural language capabilities.
a similar process would have taken up to four hours
the team used Agent Search to point the model towards verified internal data sources
using Gemini Enterprise to build an AI-driven orchestration layer that sits across siloed systems and surfaces answers in real time.
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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