Adaptive Neural Dialogue Assistant

"We don't change the person. We teach the technology to understand them."

Some words only exist in one language: the one a person speaks themselves.

ANDA AI learns how one specific person communicates, not how people communicate in general. So even the substitute working their first shift understands right away.

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READY TO LISTEN
heard: "ba, ba." 87%
I want water
Text recognition found no match. Our acoustic pattern recognized the tone of voice anyway, 87% confident.
The problem

Communication gets lost between shifts.

Many people around the world speak in their own way: sounds, shortened words and tone of voice that only people who know them well learn to read. That works fine with steady staff. But turnover, substitutes and night shifts mean that knowledge is rarely written down anywhere.

The result is missed needs, like thirst, pain or wanting to be left alone. And a person who has to explain themselves over and over to someone who doesn't understand, instead of being understood right away.

The solution

The app learns from staff. Once per phrase, not once per shift.

01

Listen

Staff record a single utterance, or run a full conversation. The app listens continuously and detects speech and silence on its own.

02

Interpret

Whisper transcribes, and our acoustic layer compares the tone of voice against previously learned patterns. It works even when the text isn't enough.

03

Correct if needed

Wrong interpretation? Staff type what the person actually meant. It takes a few seconds, once.

04

Everyone understands

The correction is saved to the account. Whoever is on shift, the same utterance is understood instantly next time.

Why it's hard

Not just transcription.

Most voice tools try to guess what was said, as text. That isn't enough for atypical speech, where the same utterance can transcribe completely differently depending on pace and clarity.

ANDA AI also listens to how something is said: a purpose-built acoustic matching layer compares the sound pattern against the person's earlier recordings, independent of whatever the transcription thinks it heard.

Verified test case
Learned phrase "I want water"
Said faster, unclear → transcribed differently
Text matching no match
Acoustic matching 87% — correct
The app answered correctly even though the transcription completely missed the utterance. That's the core value of the product.
Business model

Built for the operation, not just one person.

One account per home

Multiple residents at the same home, each with their own learned patterns that never get mixed up.

Named staff logins

Every staff member signs in themselves. Full traceability on who corrected what, and access is revoked instantly when someone leaves.

Subscription per home

Priced for operations, not consumers. Recurring revenue per customer rather than a one-off purchase.

Where we are today

Built, and in real daily use.

Core product is live

The listen–interpret–correct flow, conversation mode, and acoustic matching all work and are used daily.

Named staff accounts

Multiple staff per home, each with their own login and shared data.

Next: billing & GDPR formalization

So more homes can become paying customers safely.

Next: more languages, more homes

The same method works regardless of language, and the foundation is built to scale.

Next steps

We're looking for early investors who understand care.

Book a walkthrough of the app as it stands today, or reach out directly. We're happy to share more about where we're headed.