How it works
The expert leads.
Arkia carries them through.
ArkiaMate is infrastructure for the digital memory of expert teachers. An expert trains a digital copy of themselves, and Arkia is the avatar shell that channels it, one expert at a time through the same form.
How it works
One loop. Built for depth.
Alexia trains the memory once. Arkia carries it. You practice on your schedule while the engine learns what to teach next, built for advanced learners who want real depth, not streaks and badges.
Expert trains
An expert records lessons and corrections once. They become the corpus.
Arkia channels
The persona model and cloned voice carry their method through the shell.
Student practices
Speak or type at any hour and get their corrections in context.
Telemetry feeds back
Every attempt updates the mastery map and sharpens what comes next.
↺ telemetry returns to the memory, so the next answer lands better
Calibrate in five minutes
A short adaptive assessment finds where your English actually sits, so Alexia’s method starts at the right point instead of a generic placement quiz.
Practice with Alexia
Speak or type. Her voice and judgment respond through Arkia in real time: corrections, examples, and follow-ups the way she would give them.
Watch your map grow
Every graded turn updates mastery per skill. Friction surfaces, wins compound, and the next lesson is chosen rather than guessed.
The technology
Six pillars under the hood.
We built ArkiaMate so the answers you receive carry the expert’s thinking, with each part of the stack working to keep the experience theirs rather than a generic model wearing a custom voice.
Persona model
The expert’s teaching style, examples and judgment are encoded into a system that stays in character, so their corrections, phrasing and sequencing come through rather than a generic voice.
Cloned voice
The expert’s actual voice is synthesized in real time through ElevenLabs and streamed as they answer, with the audio driving Arkia’s lip movement while they speak.
Adaptive engine
Bayesian Knowledge Tracing infers what you have mastered and what you have not, so practice meets you exactly where you are instead of running a fixed sequence.
Longitudinal telemetry
Every session refines a personal mastery map, surfaces the patterns where you keep slipping, and compounds your wins, so you can see the curve over time.
Voice and chat in one
You speak when it feels natural and type when it does not, switching in a tap. Avatar mode is for presence while chat mode is for review, and they never run at once.
Ready for many experts
The system was built so that any deep expert can have a digital memory. Today the field is English, and the architecture is ready for more.
The measure that matters
Time to mastery, cut by at least half.
We hold ourselves to one number. The time it takes you to reach mastery on a standardized post-test should fall by at least fifty percent against a linear course taught by the same expert. We measure it because that is the honest test of whether adaptation and correction in context are doing real work, and the whole system is instrumented to track it per skill.
Why it gets better
The advantage compounds with every cohort.
What makes ArkiaMate defensible is the data it accumulates over time. Each cohort generates telemetry on where learners trip and what unblocks them, and that telemetry sharpens both the routing and the persona, so the next cohort moves through the same material faster. A competitor can copy an interface in a quarter, yet they cannot copy years of an expert’s corrections and a routing model trained on them.
The longer the system teaches, the better it teaches, which is the kind of lead that widens on its own once it exists.
Built to carry many
One shell, ready for many experts.
We sell the product with one expert because that is how we validate a real outcome with a real learner, and we built the data model for many from the first day. Arkia is a single avatar shell that can channel any expert, while each expert keeps their own voice, corpus and method behind it.
Today one expert is the lit node, and the rest of the architecture is already in place. Adding the next expert is a new row in the data model rather than a rewrite, because every student, session and retrieval is scoped by expertId.
Start today
Ready to practice with Alexia?
Her method, on your schedule. Seven days free, no card required to start.