We sat down to record an episode about model independence. Our own podcast agent, Esther, had other ideas. She had been reading our recent meeting transcripts, noticed we kept circling a bigger theme, and suggested we talk about that instead. The fact that an agent spotted the pattern, drew a conclusion, and brought it back to us is, when you think about it, the whole point of this episode.

Arjé Cahn, our CPO, and Ian Zein, our CEO, get into what it actually means to become a self-improving organization. Not bolting AI onto your workflows, but building loops where AI proposes, people steer, and the work gets sharper every cycle. Along the way:

* Jason, our go-to-market agent, and what it is like to work next to a colleague who is an agent

* why our meeting transcripts have quietly become more valuable than our documents

* the loop we use to turn a conversation into a stronger document, and back again

* a maturity model from scattered AI, to governed AI, to your first real loops, and where model independence fits in

* why governed AI is the enabler of all of this, not a compliance checkbox

* the sameness problem, including the speaker whose AI slides made the audience walk out

* and the line that ties it together: good AI is raised, not installed. The same, it turns out, is true of a company.

If you take one thing away, make it this: pick your first loop, the one workflow that matters, and keep a human who owns it.

Chapters

00:00 The topic Esther chose for us

02:54 Jason, our go-to-market agent

04:55 Transcripts, not documents

07:46 When does a conversation become the truth

10:47 The loop that makes a document stronger

12:46 A maturity model for the self-improving organization

14:04 Why governed AI is the enabler, not compliance

16:43 Islands, sameness, and putting the humans back in

20:59 Good AI is raised, not installed

A Dutch AFM-regulated investment platform processes around 400 deals a year. Each screening memo used to take a full day of manual work - pulling together databases, PowerPoints, financial documents into a templated summary. We built them something that does it in 15 minutes. But the productivity win turned out to be somewhere we didn’t expect.

In this episode, co-founders Arjé Cahn (CPO) and Ian Zein (CEO) of Aimable unpack what happened, and what made this engagement different from another “use ChatGPT to summarise stuff” project. The conversation moves from concrete output (a Word document in the customer’s exact template, with an Excel sheet underneath) to a broader question that keeps showing up across Aimable’s customers: how do you make AI deliver *finished work* instead of just talking about work? And what kind of company do you have to be to do that?

Along the way they cover why an LLM can’t be trusted to calculate but a skill wrapping Python around it can, why a black marker doesn’t work as redaction, what they learned from Ian’s own accountant chasing a missing Coolblue invoice, and where this is heading once agents start talking directly to other agents.

0:00 The 15-minute investment memo

0:30 Inside the customer engagement

2:30 Finished work vs ChatGPT experiments

3:50 One-off prompts vs reusable skills

5:00 When the numbers have to be right

7:00 An accountant’s email - and a much smaller skill

9:30 Why funds don’t just drop pitch decks into AI

10:00 A different kind of software company

11:30 The black-marker problem (smart redaction)

13:30 The compounding platform

14:00 Portal visions and agents that play by the same rules

About Trusted Intelligence

Trusted Intelligence is a weekly conversation between Aimable’s founders about what they actually run into while building a safe AI platform for organisations that need their AI to deliver real, grounded work - not chat.

About Aimable

Aimable is the leading European AI platform that lets organisations get real work done with AI, on their own data, in their own format, with their own rules - and the audit trail to prove it. Standard platform, customisable skills, EU-hosted by default.

🔗 www.aimable.ai

We gave our AI agents real jobs. At home and at work. This is what happened.

Arjé’s kids now negotiate their chores with an AI called Rosie over WhatsApp. One insisted on being addressed as “The Untouchable Alpha Lord of Chill.” Ian’s agent Zosia sends him a daily briefing at 9AM. His contractors thought she was a real person.

Then we brought it to work. Aimee, our company AI agent, reads our Slack channels, analyzes meeting transcripts, and delivers a briefing at 5AM that connects dots we missed ourselves. She does the work of at least three people. And 99% of companies aren’t doing anything like this yet.

But it’s not all smooth. These agents can’t stop talking, even when you tell them to be silent. Aimee still reports every morning: “I will NOT say anything about Arjé’s to-do list.” That’s funny at home. At work, with customer data involved, it’s a real problem. And that’s where safety gates come in.

In this episode we share what actually works, what fails, and what mid-market companies should be thinking about before their employees start running agents on their own.

Trusted Intelligence is a podcast by Aimable, the AI productivity platform for organisations with sensitive data. We build in the open. This is what that looks like.

00:00 Meet Zosia and Rosie: our personal AI agents

03:15 Rosie manages the family chores via WhatsApp

07:15 One Mac Mini, two agents, strictly separated

07:45 Aimee: our company agent that never sleeps

10:25 Human + AI collaboration: more than an RSS feed

14:05 “99% of companies aren’t doing this”

15:45 When contractors think your AI is a real person

16:20 Why AI agents can’t shut up (and why that matters)

18:50 From personal to family to work: the progression

20:05 The cost of agents that run 24/7

Stop Apologizing for AI: Build Strong Core Documents and Iterate with TranscriptsTwo founders discuss why people still apologize for using AI—often to avoid seeming lazy or to preempt mistakes in generic AI output—and note visible tells like default slide layouts and em dashes, which they no longer try to hide. They explain how to set an “AI for everything” expectation inside a company, including onboarding a non-technical sales hire by giving him core documents and an AI that can generate needed assets instead of preparing slide decks manually. They argue AI produces “garbage” when given no business context and that prompting is a skill, but a simple approach is to create concise, aligned value proposition and brand voice documents through an iterative process: record round-table discussions, transcribe locally (e.g., iPhone Voice Memos plus Whisper), and feed transcripts back to AI to refine. They emphasize owning AI outputs and integrating iteration inside the AI workflow.00:00 Why Apologize for AI00:51 Owning AI Artifacts01:39 Setting Team Expectations03:28 Why AI Outputs Feel Generic04:14 Build Solid Core Docs05:34 Roundtable Recording Method07:46 Simple Tools and Privacy09:52 Brand Voice Playbook11:29 Iterate and Constrain Models14:14 Concise Docs Win16:06 Own the Output17:25 Wrap Up and Next Time