Challenge
In service and project companies, working context usually stays scattered across chats, meetings, emails, Google Drive, tasks, Git and financial spreadsheets. When an AI agent joins the work, it sees only the current dialogue and again asks to explain the project, client, decisions and history. Sloy solves this as a corporate memory layer: it pulls the working trail from sources, links it to projects, clients, employees and money, and turns heavy documents and transcripts into short machine-readable representations.
Solution
Context is stored in corporate Git: README files, monthly summaries, decisions, risks, agreements and links to source materials. The agent reads the short project memory and returns to the originals only to verify a disputed fragment. Sloy already has routing scenarios built in for incoming emails, files, meetings and Plaud recordings: the LLM suggests a project based on participants and text, a person confirms when confidence is low, and similar sources are then routed automatically.
What changed
Result - the agent can answer questions about project status, client commitments, payments, margin, risks, and changes after vacation without manually collecting context from the manager.
