Nora Fink
Co-founder · Research & ProductAI researcher and mother of a child with dyslexia. Nora leads the scientific framework, item design and transparent model development.
Dyslexia99 combines six targeted screening areas covering reading, speech sounds, processing speed, handwriting, and sentence comprehension. Directly after, you get a detailed profile with strengths, flagged patterns, and actionable next steps.
Screening only. Not a medical, psychological, or educational diagnosis.
17 ORCID-indexed research works as well as additional public datasets, research records, and peer-review activity for PLOS ONE.
arXiv infrastructure
Verified researcher profilePublications, preprints, datasets and researcher profiles — directly verifiable on the respective platforms.
Every task is short and clearly explained. Content changes with age, while the overall flow stays familiar.
Check the camera, microphone and room noise before the timed tasks begin.
Photograph a recent, uncorrected piece of writing with at least five sentences. We describe visible patterns; we do not diagnose from handwriting.
After one practice item, classify 20 age-appropriate letter strings as real or invented.
Short picture tasks cover rhyme, syllables, first and last sounds, deletion, substitution and segmentation.
A no-audio, RAN-inspired task measures fast visual search, sequencing, confusable symbols and short-term order memory.
Read an age-appropriate text. The system looks at pace, word accuracy, pauses, repetitions and self-corrections.
After one practice item, answer 10 short, age-appropriate true-or-false sentences.
Nora's son Lukas was visibly struggling with reading. Yet his dyslexia remained unrecognized across three well-intentioned screenings. The family received isolated scores and general feedback — but no clear picture of where the difficulties lay or what would actually help next.
This is precisely why Dyslexia99 was built: a screening that goes beyond a single total score to make individual reading component skills visible.
Our goal is to help families understand earlier which patterns stand out, where strengths lie, and when further support makes sense.
Technology, research and lived family experience come together in one focused product.
AI researcher and mother of a child with dyslexia. Nora leads the scientific framework, item design and transparent model development.
Business builder focused on a clear consumer offer, responsible growth and turning a useful screening into a sustainable learning platform.
Digital-marketing specialist focused on understandable communication, parent trust and a simple path from concern to action.
Product-minded engineer responsible for reliable assessment flows, privacy-aware systems and a smooth experience across devices.
Families do not need another generic checklist. They need to see what happened in the actual tasks and what the pattern may mean.
The product stays simple on the surface while collecting the detail needed for a responsible interpretation.
The core screen is designed to stay close to 10 minutes, with one practice item before each task type.
The child or adult performs real reading-related tasks instead of only answering whether certain problems sound familiar.
See performance by module, task quality and uncertainty. No single number is presented as a diagnosis.
Routes for ages 7–9, 10–12, 13–17 and adults use the same structure with different words, texts and difficulty.
The report explains sensible next steps and prepares a learner profile for the upcoming adaptive reading app.
The website uses no advertising trackers. App consent, research permission and marketing permission are kept separate.
Dyslexia is not captured by one trick question. Dyslexia99 combines complementary tasks so that speed, accuracy and error patterns can be considered together.
The first release uses transparent task rules plus model-assisted interpretation. Until a validated statistical model is available, the report avoids claiming an exact clinical probability.
Parents need a safe place to manage profiles and reports, but the child-facing test should remain distraction-free.
The public website has no database, no forms and no advertising cookies. The screening app will collect only what is needed for the screen and the choices a parent actively makes.
A screening is only useful when the person can understand what to do and comfortably complete it.
The screen gives clear setup instructions and marks results as uncertain when the conditions are not good enough.
Reading patterns differ by language, age and learning history. A single global score would hide those differences.
Dyslexia99 is built to support an earlier, better-informed next step — not to replace qualified diagnosis or create false certainty.
Dyslexia99 combines established approaches from word recognition, phonological processing, reading fluency, sentence comprehension, and supplementary handwriting analysis into one guided screen. The specific combination and evaluation are continuously refined with real usage data.
Lexical decisions with real words and pseudowords are used in Stanford's validated ROAR-Word assessment.
Stanford ROAR-WordSound matching and phoneme deletion are established early-reading measures and core parts of ROAR-Phoneme.
Stanford ROAR-PhonemeLarge reviews and meta-analyses find a meaningful relationship between rapid naming and reading performance.
Araújo et al., 2015Fast, accurate sentence understanding adds information beyond single-word reading alone.
Stanford ROAR-SentenceWriting can reveal useful observable patterns, but handwriting alone is not specific enough to diagnose dyslexia.
Research contextOur team publishes on machine learning, handwriting analysis, reading patterns, eye tracking, and explainable screening methods. These publications form the technological and scientific foundation upon which Dyslexia99 is developed.
Reported performance values relate to the datasets and study designs used in the respective research records.
Computer-vision framework for automated, explainable handwriting error analysis.
View publicationPipeline approach combining feature extraction with classical machine learning for risk scoring.
View publicationEvaluates Machine Learning models on gamified interaction data from children.
View publicationData augmentation pipeline to improve detection robustly across small clinical datasets.
View publicationUnsupervised classification of eye movement patterns during reading tasks.
View publicationOpen synthetic dataset for training and benchmarking handwriting error detection models.
View publicationParents consistently describe the same three situations.
Parents often describe children falling further behind while they wait for a school referral or clearer feedback.
Cost, insurance coverage, location and long waiting lists can delay a comprehensive professional evaluation.
Families want to understand the pattern behind slow or inaccurate reading and what a sensible next step looks like.
Condensed themes from public parent and dyslexia communities; not customer testimonials.
Clear answers about what the product can — and cannot — tell you.
No. It is an educational reading and dyslexia-risk screening. It cannot diagnose dyslexia or replace an assessment by a qualified professional.
The first product starts at age 7 and routes users into age groups 7–9, 10–12, 13–17 and adult. The structure stays consistent; words, texts and difficulty change.
The timed core is designed to remain close to 10 minutes. Account setup, consent, camera setup and choosing a writing sample happen before the core timer.
A recent, uncorrected text of at least five sentences can add useful context about spelling and visible writing patterns. The app reports observable features and does not diagnose from handwriting.
A technology check, writing sample, word and pseudoword decisions, seven areas of phonological awareness, a visual-speed and sequencing task, one minute of read-aloud, and sentence understanding.
No. It is RAN-inspired and requires no spoken response. It measures visual search, sequencing and confusable symbols, with a short order-memory component. The report labels it accurately.
The known text is compared with the recording to estimate reading pace, word accuracy, substitutions, omissions, pauses, repetitions and self-corrections. Poor audio quality can make a result unscorable.
The finished product must be validated on independent data before any strong accuracy claim is made. The first release shows task results, uncertainty and a model-assisted screening interpretation rather than a clinical probability.
German, US English, UK English and Spanish for Spain. The public website is available in English, German and Spanish.
Not as separate website languages at launch. The interface can remain English, while pronunciation, item suitability and norms should be checked separately before region-specific scoring claims are made.
No. Word frequency, pseudowords, speech sounds and reading fluency differ by language. Each language and age route needs its own item bank, audio and validation.
Yes, but results must be interpreted in light of the person's main language of instruction and reading experience. Limited exposure to the test language can look like a reading difficulty.
One complete screening, a parent dashboard, detailed module results and a downloadable PDF report. Market currency is shown before purchase.
Age, grade/year or education level, sex with an option not to say, language information, a required choice of diagnosed, suspected or not known, and the consents needed for the screening. Family history is not required.
No. Marketing permission is separate and optional. It is not required to purchase or complete the screen.
It keeps learner profiles, reports, consent settings and deletion controls in one place. It also allows a secure connection to the future reading app.
Use the report as a structured starting point. Depending on the pattern and the person's day-to-day difficulties, the next step may be targeted reading support, a conversation with school, or a comprehensive assessment by a qualified professional.
The PDF can support a discussion, but schools and authorities decide what evidence they accept. Dyslexia99 does not promise eligibility, accommodations or a formal diagnosis.
A secure learner profile can set the starting level and priorities for adaptive practice. The planned reading app will use AI for personalised support while keeping the learning sequence structured and reviewable.
The parent area is designed to provide access, export and deletion controls. Exact retention periods and processing partners will be shown before the screening launches.
Check six reading-related areas, get results immediately upon completion, and download a detailed report with concrete next steps.
Screening only — not a diagnosis.