Evidence tier Source Confirmed · 2 captures on record
What the publisher says
As described on AWS Marketplace.
Enterprise knowledge is trapped.
AI can't work cleanly on top of scattered context. Every enterprise accumulates the same hidden architecture problem. Institutional knowledge ends up spread across CRM, ERP, ticketing, wikis, shared drives, calendar history, and the heads of the people who know how everything actually fits together.
Show the rest of the publisher’s description (6 more lines)
When someone needs an answer, they open tabs, ping colleagues, search inboxes, and stitch the situation back together by hand. That is why decisions lag and why many AI programs stall before they become useful.
You cannot automate what the organization cannot query coherently.
**Hive** solves that at the architecture level. Not by adding one more system of record. By creating a semantic layer that understands your entities, your decisions, and the relationships between them in real time.
The problem with most AI assistants is not the interface. It is the lack of grounded context.
Most chat tools are impressive until the work becomes specific. They do not know your organization, they forget between sessions, and they stop at text generation.
**Sayya** is built for the opposite environment: one where answers need context, workflows need memory, and actions need control.
Highlights
Highlighted by the publisher on AWS Marketplace.
Why teams buy Hive Decision substrate: Unify the history, ownership, dependencies, and trade-offs behind a decision before anyone has to reconstruct it manually. AI foundation; Give agents and automations a grounded way to query the business so projects stop failing at the knowledge layer. Operational memory; Keep what the company has learned attached to the entities and workflows that need it instead of losing it in inboxes and turnover.
Why teams buy Sayya Conversation surface: Let operators, executives, and frontline teams work through natural language without forcing them to navigate every underlying system. Learning loop; Preserve user-specific preferences, corrections, and playbooks so the assistant compounds instead of resetting every session. Action path; Move from insight to execution with auditable write-back into CRM, calendars, tasks, documents, and monitored workflows.
Agent build and provenance
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Plans and pricing as listed
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Refund terms
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Sources
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4 linksLinked repositories
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