The problem we are solving, and the proposal

A1Executive summary

Our employees and customers already use AI. But the general-purpose assistants we can buy know the public internet, not [company]. They have never seen our policies, products, procedures, contracts or support history. When they are asked about them, they still produce a confident answer, and it is often wrong.

We propose to adopt Trilivy.AI, a platform that grounds AI in our own documents and data. It ingests our knowledge once, keeps it governed, and serves it to a chatbot, to mobile apps, to the AI assistants we already use and to our own systems through an API. Every answer cites the document it came from.

We would start with one narrow, measurable use case, [usecase], and expand only as results justify it. Trilivy.AI is licensed perpetually and delivered with its full source code, so we own what we build and are not locked in.

A2The problem

Our knowledge is scattered, and generic AI cannot reach it

The knowledge our people need to do their work is spread across [knowledge_locations]. Finding the right, current answer takes time. Finding it means asking a colleague, who is then interrupted. When that colleague leaves the company, the knowledge often leaves with them.

Giving everyone a general AI assistant does not fix this. The assistant does not know which of our documents is authoritative or current, or who is allowed to see what. An answer that sounds right is not proof that it came from the right policy.

Most AI projects fail on data and governance, not on the model

Independent research is consistent on why enterprise AI projects stall:

These figures describe different populations and should not be combined into one "AI failure rate". Together, they say that a successful demo does not prove production readiness, and that knowledge readiness has to be part of the plan from day one.

What this costs us today

• Time lost searching for and re-answering known information: [hours_lost_text]
• Repeated questions to [repeated_q_team]: [repeated_q_volume]
• Time for new hires to reach productivity: [onboarding_weeks]
• AI pilots or tools already bought that do not share knowledge with each other: [existing_ai]

On our own numbers, that recoverable time is worth about – per month. Part B turns it into a full financial case.

A3What Trilivy.AI is

"AI knows the world. Trilivy makes it understand your company."

Trilivy.AI is a platform-first, RAG-grounded AI system. RAG (retrieval-augmented generation) means that before the AI answers, the platform retrieves the relevant, permitted passages from our own knowledge and gives them to the model. The model reasons over our content and cites it, instead of answering from memory.

Three layers, kept separate on purpose

The principle is to build the knowledge foundation once, keep it independent, and let models and agents compete to use it. A better model next year is a configuration change, not a rebuild.

What we receive

The core we are buying is the ingestion, retrieval and API layer. The add-ons come with it, and we switch them on as needed.

Key facts

A4What Trilivy.AI is not

We should be clear about the limits so nobody is surprised later. The vendor states them itself:

• It is not a turnkey, plug-and-forget product. It is an accelerator: a working foundation we own, run and extend.
• An AI Data Expert is mandatory, with no exception. This is a data-literate person on our side who owns the knowledge: knows where it lives, decides what is authoritative, gets it handed over and keeps it current. Without that person, the platform waits for data that never arrives. Our AI Data Expert: [ade].
• Someone must stand up and run the platform: either an external certified system integrator or our own IT team. Our choice: [impl_mode].
• Mission-critical and compliance-heavy use needs our own review first. That means security review, load testing and industry-specific validation ([regulation]) before broad production use.
• It does not replace our existing AI vendors. It makes them better by giving them governed access to our knowledge.

A5Why a platform, not another chatbot

We could buy a separate AI tool for each department. Each would ingest its own copy of our documents, apply its own permissions and give its own answers. Each pilot could succeed on its own while the overall architecture got worse.

A6How it fits [company]

We start narrow: one task, one audience, one bounded set of approved documents, one named owner. Twelve proven starting points: