Simple AI Tasks Deliver Real Results – Stop Overengineering

01 /The Concept

Ethermind is another startup aiming to "transform professional education with AI" – though its approach is more grounded than that phrase usually implies.

The core intervention is simple: give course instructors AI-powered teaching assistants that handle routine tasks and answer student questions around the clock.

Those assistants are AI personas built by Ethermind, currently trained in management, cloud services, and DevOps. They're available 24/7 to field student questions without pulling in a live instructor.

The platform ships with a preloaded curriculum from CertNexus and Microsoft, so the AI personas come ready to answer questions on those topics out of the box.

The real flexibility, though, is in extensibility. Organizations can train Ethermind's AI personas on any subject matter by uploading their own course materials and configuring persona behavior through a no-code interface. The training program manager doesn't need to be a subject-matter expert to set this up.

An internal marketplace brings 4,400 experts onto the platform – available to help build new courses, configure AI personas for specific content, or serve as guest instructors. A second marketplace lists existing courses: if a company builds a course and configures an AI persona around it, they can license that course to other companies and collect royalties.

One underappreciated capability: AI personas remain available to students after course completion, when they're applying what they learned on the job. Post-course support without live instructor involvement is a meaningful operational advantage.

Ethermind targets professional training and certification companies, as well as corporate learning and development teams. Founded in Bulgaria last year, it already has 6 enterprise clients who have run more than 2,000 students through the platform, drawing on 1,000+ uploaded learning materials.

The startup has raised its first round – £500K (~$650K).

02 /Why It Matters

AI personas and digital twins are having a moment, with training and coaching being one of the most active application areas.

The most common use case so far is sales training. The pattern: salespeople practice client interactions not with real customers but with AI personas modeled on real or composite buyer types. You upload conversation transcripts, the persona learns the patterns, and trainees practice against it. Hyperbound ([covered here](/review/za-takoe-obuchenie-kompanii-tochno-zaplatjat)) is a well-known example – a Y Combinator alum using this approach specifically for sales rep training.

The same technology applies to hiring. Take2 ([related review](/review/s-tem-zhe-samym-na-drugoj-rynok)) uses AI personas to pre-screen sales candidates, routing only those who clear the AI negotiation threshold to live interviews. It raised $3 million in April.

A more unusual application comes from Lakmoos ([covered previously](/review/mgnovenno-vmesto-polugoda)), which lets product teams create AI personas representing their users – built from support transcripts and communication logs. Developers can then ask those personas questions about product decisions before building anything. The startup's own research found the AI responses statistically align with real user feedback. Lakmoos is based in the Czech Republic and raised its first €300K in March.

Personal AI ([covered here](/review/vzorvat-rynok-obrazovanija)) takes the concept in a more personal direction – letting users build a digital twin of themselves capable of handling messages on their behalf. Consumer applications like celebrity or character personas exist too, though the commercial models there are still being figured out. Personal AI has raised $13.7 million.

03 /Opportunities

AI personas are distinct from "digital employees." Superficially similar, but the underlying architecture tends to be simpler: an AI bot operating on a fixed algorithm, trainable on domain-specific text, with configurable parameters for specific use cases.

The simplicity is a feature, not a limitation. For a wide range of routine tasks – answering questions grounded in a specific knowledge base, handling repetitive interactions – that's exactly what you need. And for 80% of use cases, nothing more is required.

The opportunity is building persona platforms for specific verticals. Each domain may require a purpose-built algorithm, but once that foundation exists, end users can create and configure their own personas – uploading relevant materials and tuning the behavior – without engineering involvement.

The real questions worth working through: which specific operations in which specific domains can be automated with AI personas today? What algorithm logic does that domain need? What kind of text should be fed into the persona? And which behavioral parameters should be user-configurable to make the persona genuinely flexible? Answer those, and you have a product spec.