amaiko vs Omnora: Living Memory vs the AI-Interviewed Wiki (2026)

What Omnora gets right

Let’s grant the premise first, because it’s a real one. Knowledge trapped in a few people’s heads — “Kopfmonopole” — is a genuine business risk. Handovers drop things, onboarding is slow, and when a key person leaves, a chunk of how the company actually works walks out with them. Omnora is right about the problem.

And its training-production engine is legitimately capable. AI-led expert interviews, document-to-course conversion, AI-dubbed videos in 140 languages, automatic quizzes, 120+ avatars — if your goal is to mass-produce polished e-learning material, Omnora does that well, and more than 650 companies use it for exactly that. We’re not going to pretend the problem is fake or the machine is bad.

The disagreement is narrower and more important: it’s about the method for keeping knowledge. And there, Omnora is solving a 2026 problem with a blueprint from 2010.

It’s a wiki with a chatbot receptionist

Strip away the AI veneer and the deliverable is the oldest one in the book: documents. Articles, courses, PDFs, videos — a library that someone has to build, review, publish, and then maintain forever. The clever part is that Omnora moved the data entry from a keyboard to an interview. But the thing you end up owning is still a wiki. And everyone reading this already knows how wikis end.

Nobody wants to be interviewed by a bot

The entire model depends on your experts volunteering to sit down and be questioned by an AI. Think about who those experts are: your most senior, most overloaded people — the ones whose knowledge is worth capturing precisely because they’re too busy to write it down. Asking them to schedule AI interviews is asking for the one resource they have least of.

So the interviews that actually happen are the convenient ones, on the topics that were never really at risk. The knowledge you most needed — the stuff in the head of the person retiring in March — stays exactly where it was: undocumented, and walking out the door on their last day. A capture tool that only captures what people have time to volunteer isn’t insurance against knowledge loss. It’s a training-content generator.

The crucial detail never comes up

Here’s the deeper problem, and no amount of interview polish fixes it. The knowledge that actually matters is tacit — the judgment call, the exception you only make under a specific condition, the reason you don’t do the obvious thing. It’s tacit because the expert doesn’t consciously know they know it; it only surfaces in the moment of doing the work.

An interview asks “how do you do X?” and gets back the tidy, official version — the one that would go in a manual. The real version, the messy and correct one, never comes up, because nobody thinks to ask and the expert doesn’t think to say. You end up with a beautifully produced course that documents the process everyone already knew, and misses the ten hard-won details that were the entire point.

amaiko doesn’t interview anyone. It observes the work as it happens in Teams — the actual decisions, the actual exceptions, the actual “we do it this way because last time we didn’t” — which is the only place tacit knowledge is ever visible.

A snapshot rots

Say the interview goes perfectly and the knowledge is captured cleanly. You now own a document. Documents have a half-life. The process changes, the tool gets replaced, the policy updates — and the article doesn’t, because updating it is a job that is nobody’s priority. Six months later your beautiful AI-generated knowledge base is quietly wrong, and wrong documentation is worse than none, because people trust it.

This is the slow death every wiki dies, and putting an AI in the author’s chair doesn’t change the ending — it just produces more pages, faster, for the same graveyard. amaiko’s memory has no maintenance step, because it isn’t a library. It’s fed continuously by the work, so it moves when the company moves. Nothing to review, nothing to re-publish, nothing to quietly go stale.

amaiko’s opposite bet

amaiko inverts the whole model. No interviews, no wiki, no L&D project to staff and run. It builds a persistent, self-learning company memory from what’s already happening in Microsoft Teams — decisions, context, who knows what, why things are done the way they are. When someone leaves, their context stays behind. New hires inherit a living memory instead of a stack of possibly-outdated courses; the measurable effect for amaiko teams is 35% less time spent searching and onboarding up to 57% faster. See how that works.

And because it’s Teams-native, there is nothing to deploy and nobody to interview. You add amaiko to Teams and it starts learning your business from the work itself — the exact opposite of a project you kick off, staff, and hope people find time for.

What it costs

Omnora is quote-based. It’s sold as a corporate-learning platform, so budgeting starts with a sales call and, for a knowledge-capture rollout, usually an implementation phase on top of per-seat fees — you’re funding a program, not just a license.

amaiko starts at €29.91 per user per month, billed annually — with memory, proactive intelligence and the multi-agent network included, not stacked on top. There’s no capture project to fund before you see value; the value starts accruing the moment it’s in your Teams.

Who should choose what

Honest segmentation — and Omnora earns a real “choose them” case.

Choose Omnora if your actual goal is a formal training operation: you need to mass-produce e-learning, track mandatory-course completion, issue certificates, run quizzes, and localize training videos across languages. That’s a legitimate need, and Omnora is genuinely built for it — it is a learning platform first and a knowledge-preservation tool second. If you were going to buy an LMS anyway, the AI authoring is a real upgrade on writing courses by hand.

Choose amaiko if what you want is for your company’s knowledge to stay in the company without running a capture project to make it happen — a memory that builds itself from the work, lives where your team already works, and acts on what it knows instead of filing it away. If you’re surveying the field, our roundup of Omnora alternatives covers the neighbours. And if you’d rather see living memory than read about it: book a demo — it takes one Teams chat to show you.