Dimitris Goudis recently shared something that caught my attention: "You will chat with an AI assistant who knows your clients from your conversations - not from Google searches. I will show you live how." That single idea reframes what many teams think AI is for.

He also pointed out a painful reality most of us accept as normal: every day we generate contracts, emails, Slack threads, meeting transcripts, SOPs, and docs that "sit unused." In other words, our most valuable business context is trapped in scattered tools, buried in threads, and lost in handoffs.

I want to expand on what Dimitris is really describing here: not a chatbot, not a generic search bar, but a practical AI knowledge base built from your actual work so you can ask it the questions your business is already paying for.

The real asset you already own: your operational memory

Dimitris Goudis made a point about documents that really resonated with me: most companies treat their written trail as exhaust. But that trail is the only place where the truth of your business lives:

What clients actually asked for (not what the proposal template says)
What you promised, when you promised it, and what changed
What objections keep showing up in sales calls
What internal decisions were made in meetings and never documented properly
What "this is how we do it" means in practice across different teams

When that context is spread across inboxes, drives, Slack, Notion, CRM notes, and call recordings, your team pays a tax every day: repeating questions, re-learning history, re-litigating decisions, and missing patterns.

"Turn all of it into one searchable mind."

That line from Dimitris captures the goal perfectly. The point is not to store more documents. The point is to make your collective memory queryable.

What it means to have an AI assistant that "knows your clients"

There is a huge difference between:

An AI that knows the internet
An AI that knows your business

Dimitris Goudis is clearly advocating for the second. When he says the assistant knows your clients "from your conversations," he is talking about first-party context: the emails you exchanged, the meeting notes you wrote, the requirements in the contract, the deliverables in the SOW, and the follow-ups in Slack.

That matters because most client questions are contextual. A generic model can explain best practices, but it cannot tell you:

What you promised Client A in the Q4 kickoff
Whether that promise was revised in the last steering call
Which stakeholder rejected the previous approach and why

An AI knowledge base grounded in your documents can answer those questions because the evidence exists in your artifacts.

The 5 questions Dimitris listed are the perfect starting point

Dimitris Goudis suggested asking your AI things like:

"What did I promise in meetings that I haven't delivered yet?"
"Which client problems keep showing up?"
"What patterns am I missing across all my conversations?"
"What should my team know right now?"

These are not toy prompts. They are operational prompts. And they reveal the real value of a searchable mind: it helps you manage commitments, detect recurring pain, and reduce the gap between what was said and what got done.

1) Find undelivered promises before they become escalations

Most delivery risk is not technical. It is conversational. A promise gets made casually in a call, then it lives only in a transcript or someone’s notes.

A knowledge base can surface:

All statements that sound like commitments ("we will," "I will," "by Friday")
The owner, the date, and the related client
Whether there is follow-up evidence that it was completed

Used weekly, this becomes a lightweight risk radar.

2) Turn recurring client problems into product and process improvements

"Which client problems keep showing up?" is how you graduate from reactive support to strategic improvement.

If you consistently see themes like onboarding confusion, reporting delays, or unclear roles, you can:

Update SOPs and templates
Add a proactive onboarding module
Improve product documentation or UX
Train the team on the top 5 recurring misunderstandings

The insight is not in any single ticket or call. It is in the pattern across dozens of interactions.

3) Spot patterns you are missing across conversations

Humans are not great at cross-referencing. We remember the latest issue, the loudest stakeholder, or the biggest fire.

An AI knowledge base can help you ask:

Which objections appear before deals stall?
Which features are promised most often in sales but hardest to deliver?
What phrases indicate churn risk?

This is where "client intelligence" becomes real: it is not spying, it is making sense of what your clients repeatedly tell you.

4) Answer "What should my team know right now?" with evidence

Status updates are expensive because they are mostly reconstruction. Someone has to remember what matters, then re-type it.

If your system can pull a concise brief with citations (links back to the source email, note, or doc), you reduce:

Meeting load
Slack interruptions
Context loss during handoffs

And you raise the quality of decisions because the team can trace conclusions back to the underlying facts.

What "build it in minutes" actually implies

Dimitris Goudis wrote that building this takes minutes, not months, and that in a live session you can build it "from your actual documents, step by step." The important implication is scope.

Most teams fail at knowledge management because they start with an impossible goal: "organize everything." A faster path is:

Start with one business area (for example: one client account, one sales pipeline stage, or one delivery team)
Ingest a small, representative set of documents
Define the questions you want answered
Improve retrieval and structure based on those questions

In other words, you do not build a library. You build an answering system.

A practical blueprint for your first AI knowledge base

If I were following Dimitris Goudis’s approach, I would do this in a tight loop:

Step 1: Choose a single outcome

Pick one outcome that matters this month:

Reduce missed follow-ups
Speed up onboarding
Improve proposal accuracy
Lower support escalations

Step 2: Gather the minimum useful sources

Do not start with everything. Start with what contains the most truth:

Meeting notes and transcripts for the last 30-60 days
Key client emails and decision threads
The latest SOW or contract
Current SOPs and templates

Step 3: Normalize and label lightly

You do not need perfect taxonomy. You need enough structure to help retrieval:

Client name
Date
Document type (call, email, contract, SOP)
Owner or participants

Step 4: Ask the questions Dimitris suggested, and iterate

Run prompts like the ones Dimitris listed and check:

Are answers specific?
Are they grounded in your documents?
Can you click back to sources?

If the answers are vague, you do not need "more AI." You need better source coverage, cleaner documents, or clearer questions.

The bigger idea: from documents to a digital twin of your work

Dimitris Goudis’s framing hints at something larger than search. When an assistant can reliably answer questions about commitments, patterns, and priorities, your business starts to look like a system with memory.

That is the competitive advantage: not fancy prompts, but faster sense-making. The teams that win are the ones that can:

Recall context instantly
Learn from patterns continuously
Hand off work without losing the plot

An AI assistant that knows your clients is really an assistant that knows your history.

If your history is scattered, your decisions are slower and riskier. If your history is organized and queryable, you move with clarity.

This blog post expands on a viral LinkedIn post by Dimitris Goudis, I turn meetings, docs, and workflows into AI systems that think like you do | Founder @ 3nuggets | Knowledge Bases • Digital Twins • AI Agents. View the original LinkedIn post →