Your own server, provisioned and running — with everything your company knows on it.

Nothing to procure. Nothing to install. Nothing to wait for. Log in and go — today.

Not a slice of a shared machine. Not a tenant in someone else's database. Not a migration project with a date next quarter. A dedicated server — 8 CPU cores, 32 GB of memory, 400 GB of disk — live, and waiting for your documents. Here is what fits on it, what it will do all day, and the two screens you use to fill it.

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Knowledge stored

800,000

pages of searchable text — about 2 million indexed passages, held in memory for instant recall.

Conversations

7,000/hr

answered questions per hour, sustained. Roughly two every second, around the clock.

Machine calls

20,000/hr

REST and MCP requests per hour — your applications and AI assistants querying directly.

Formats it reads

file types across 20 loaders — documents, spreadsheets, mailboxes, databases, websites, source code.

What those numbers actually mean

36%

Give 1,000 people this server — every one of them a heavy AI user, having 24 conversations a day — and it runs at just over a third. That is 24,000 conversations a day, and the machine is barely leaning on it.

Conversations

2,400 /hr of 7,000

34% used

Machine calls

7,200 /hr of 20,000

36% used

People waiting at once

~6 of 40 slots

15% used

Every one of those thousand people could ask nearly three times as often before the busiest hour reaches a ceiling. The headline figure is the worst of the three above, not the flattering one — machine calls bind first, at 2.8× headroom. Assumes those conversations spread across a ten-hour working day, and that each one also triggers three background calls from your agents, integrations and scheduled automations. Note what does not move: the library is the size of what your company knows, not of its payroll — hiring another thousand people adds traffic, never pages.

The Jumpstart: two screens from empty to answering

A server with capacity is not the same as a server that is useful. The reason Trilivy.AI is working the week you buy it — rather than the quarter after — is that the whole path from raw content to a cited answer is two screens, both already installed and waiting the first time you log in.

Step 01

Ingestion Center

Turns what you already own into a governed, searchable knowledge base. You point it at content; it reads, splits, understands and indexes.

40 formats, 20 loaders — PDF, Office, HTML, Markdown, CSV, XML, JSON
Sources that never arrive as a file — live databases, websites, mailboxes (EML, MBOX, PST), source code and SQL schemas
Audio and video transcribed and indexed with everything else
Automatic routing — prose, spreadsheets, code and Q&A pairs land in separate stores, so a question about code never wades through narrative
Governed — metadata on every passage, so answers can be scoped by audience and permission

Done when a question comes back answered with a citation.

Step 02

Retrieve Center

Puts that knowledge to work. One foundation, two doors — and the only question is who is consuming it: a person, or a machine?

Conversational — a chat assistant with its own persona, its own permitted scope of knowledge and its own appearance. Customer-facing and internal assistants are separate deployments, never the same voice with different settings.
Programmatic — REST for your own applications, and MCP connectors so the AI assistants and agents you already pay for query your knowledge as a native tool:

Microsoft CopilotM365 · Copilot Studio Salesforce Agentforceservice & sales agents ServiceNowNow Assist · AI Agents SAP JouleS/4HANA · SuccessFactors Oracle FusionAI Agents · EBS Google GeminiVertex AI Agent Builder AWS BedrockAgents & Knowledge Bases Anthropic ClaudeDesktop · Code · API OpenAI ChatGPTEnterprise · Assistants UiPath · n8n · Zapieragentic automation

MCP is an open standard, not a partnership — support and maturity vary by vendor and version. Anything that cannot speak it can still call plain REST, which is everything.

Both doors read the same governed tables, so improving the knowledge improves every consumer at once.

Done when a real consumer — person or machine — has received a real answer.