
Your therapy session
Your usual weekly or bi-weekly therapy sessions can now be hosted on Feelpath's video platform, without leaving your existing EHR.
A Learning Platform for Emotions
Feelpath helps therapists support clients' emotional growth between appointments. Clients get personalized reflection, practice, and review built from your therapy conversations.

Feelpath is the #1 way therapists can support their clients' emotional development between sessions
How it works
Host your next sessions on Feelpath so your clients can get an upgraded learning experience.

Your usual weekly or bi-weekly therapy sessions can now be hosted on Feelpath's video platform, without leaving your existing EHR.
After each session, Feelpath prepares note drafts, session insights, emotion tools, and personalized learning material for you and your client.
Emotion labeling rate
+93%
Emotion words per 1,000 words
Clients keep learning between sessions and return with more to build on.

Hi, I'm Nick. I built Feelpath to give therapists a better way to support their clients' emotional development without adding more work.
Until my late 20s, I was unknowingly living with alexithymia. Alexithymia means difficulty identifying and describing emotions. Thankfully, therapy helped me learn how to notice, name, and work with what I was feeling.
However, even with an excellent therapist, the learning process was slow, tedious, and confusing. For me, feelings often showed up as stress, body tension, shutdown, or confusion before I had clear words for them.
That is why I wanted to build Feelpath. My hope is that this kind of emotion learning platform can support your therapy sessions in the background, never interrupt your workflow, and give clients a clearer view of the progress they're making as they build emotional clarity.
Nick Venturino
Founder, Feelpath
Alexithymia (difficulty identifying and describing emotions) is common: around 10% of the general population meet criteria for alexithymia, and estimates range from 50–85% among many autistic and ADHD populations.[1,2]
It is strongly linked to interpersonal distress, insecure attachment patterns, and developmental factors such as early language delays and a reduced sense of agency.[3]
The measurement gap
The Alexithymia Language Index (ALI) tests whether session-derived language measures can help close this gap.[4,5]
Core features of alexithymia
Hard to know what I'm feeling.
Feelings are present but vague or blurry.
Hard to put feelings into words.
Emotions are felt but not easily described.
Focus on facts more than inner life.
Attention goes to events, not inner reactions.
Body sensations and emotions blur together.
It is hard to tell physical cues from emotional states.
Emotions show up differently across people and neurotypes. This table highlights a few commonly reported patterns of difficulty / strain across domains. When emotional awareness is harder (alexithymia), regulation, executive functioning, and social-emotional inference can get harder too—especially under stress.
| Profile | AlexithymiaLow emotional awareness | Emotion dysregulationDysregulation under stress | Social-emotional skill gaps†Difficulty with empathy & perspective-taking | Executive dysfunctionDifficulty with planning, initiation & follow-through |
|---|---|---|---|---|
| ADHD | ◐ | ● | ◐ | ● |
| Autism (ASD) | ● | ● | ◐ | ◐ |
| AuDHD | ● | ● | ◐ | ● |
| CEN* | ● | ● | ◐ | ◐ |
| HSP* | ◐ | ◐ | ◐ | ◐ |
| Trauma / CPTSD | ● | ● | ◐ | ◐ |
| Anxiety / Depression & Perfectionism | ◐ | ● | ◐ | ◐ |
Table notes: Cells with a corner marker in the table above contain cell-specific details. Hover to preview, or click/tap anywhere in the cell.
Selected peer-reviewed sources supporting the main patterns summarized in this table, including the idea that alexithymia (low emotional awareness) can cascade into harder regulation, social-emotional inference, and downstream functioning—especially under stress.
† In this table, “difficulty reading emotion / empathy” refers to how easy it is to read cues and infer what someone might be feeling/meaning (emotion recognition and perspective-taking), not how much someone cares. This domain is also harder to measure and comparatively under-researched, so the notes are meant to add nuance and reduce overgeneralization.
* CEN (Childhood Emotional Neglect) is an experience; HSP (Highly Sensitive Person) is a temperament trait. They’re included because support needs often overlap with neurodivergent profiles.
A quick, high-level comparison of the widely used self-report scales VS. our conversation-derived ALI approach.
Toronto Alexithymia Scale
Perth Alexithymia Questionnaire
Alexithymia Language Index (Feelpath)
Example composite scores show alexithymia indices trending down from last month to this month across six score dimensions.
Insightful analytics
Our example analytics panel shows how language patterns can be summarized into clear signals for identifying, describing, and relating to feelings.
Client psychoeducation
We offer clients additional support outside of the session through personalized psychoeducation tools like our smart emotion wheels, dynamically updated and created based on the session conversation.
Click different dots to see a visual explanation of what the client might be feeling underneath.

We're building on top of a working HIPAA-focused platform, analytics, and clinician/client UIs so that labs can plug in their own study questions rather than starting from scratch.
Feature preview
Study readiness examplesWorking platform & privacy safeguards
Example of how sessions move through encryption, de-identification, and analysis before results return to clinicians.

A quick overview of how ALI is meant to be used today in research and clinical settings.
ALI surfaces candidate markers and indices to support research and clinical reflection. It is not intended to diagnose or prescribe treatment, and should not be used as a standalone decision-maker.
Outputs are designed to be read, questioned, and integrated by human clinicians and supervisors. The therapeutic relationship, case formulation, and clinical judgment stay central.
ALI focuses on alexithymia-related patterns over time, not on real-time risk assessment or crisis services. It should be paired with your existing protocols for safety and escalation.
Real words from people using these tools to better understand their emotional life.
“The way I can use the computer to zero in on specific feelings is groundbreaking. I find this makes healing and growth more effective and powerful.”
Matt K.
Feelpath participant
“I love getting insights on how I've been expressing myself emotionally. I can't wait to see how the product develops to make me more aware and embodied as my emotional self.”
Jessica R.
Feelpath participant
“The emotion wheels, which highlight frequently used words in my transcripts, have been instrumental in increasing self-awareness and aiding reflection.”
Michael C.
Feelpath participant
“I deeply appreciate the emotion wheels, especially those focusing on positive self-regard and shame. Their insightful design helps me navigate complex emotions with clarity and understanding.”
Sarah T.
Feelpath participant
“I've found myself turning to these wheels more frequently, as they provide invaluable guidance in my journey of self-awareness and emotional exploration.”
Kristen V.
Feelpath participant
“Grateful for the profound impact this software has had on my understanding of myself and others.”
Robert M.
Feelpath participant
ALI is built on the same privacy protections as the rest of Feelpath: HIPAA-focused, consent-first, and designed so that individuals and clinicians can see and control how their information is used.
Consent-first design
Clear language about how session language is saved and analyzed, with controls to turn AI features on or off and to adjust retention and sharing.
BAAs + HIPAA-aligned safeguards
Encryption in transit and at rest, BAAs with clinicians, and logged access help support the kind of privacy expected in clinical and research settings.
Care for what people share
People share real, vulnerable things during therapy. At Feelpath, we take this seriously. We keep the words shared in session, and the insights built from them, private, protect them carefully, and keep them in service of the work of therapy.

ALI is at the measurement and validation stage. Our goal is to build and test language-based measures of alexithymia with you.
Our Validation Goals
How You Can Help
Any diagnostic or regulatory path would be a separate, later stage with its own governance. Today, the focus is on careful measurement, validation, and fit with real clinical work.
A few of the questions research partners and clinicians most often ask about ALI. For more detail on privacy, data handling, and product usage, you can visit our full FAQ.
No. ALI is designed for research and clinical insight about alexithymia-related patterns in session language. It is not intended to diagnose at this stage and does not replace clinical judgment or therapy.
We are seeking PIs, labs, and individual clinicians who want to study alexithymia-related patterns in language, compare ALI-style indices with existing measures, and help shape how results are visualized and used in practice.
ALI is designed to sit alongside your existing therapy, research, and documentation workflows. With consent, session language is analyzed to produce indices, visuals, and psychoeducation prompts that you can review between or during sessions.
We are building a simple way to derive psychometric-style measures directly from therapy and research sessions, and to pair those measures with personalized psychoeducation for clients who struggle with alexithymia-related traits.
Academic labs and individual clinicians or therapists who are interested in digital language-based measures, alexithymia and affective science, or psychotherapy process and outcome research.
One example is a study where ALI features are turned on for one group and off for another, with outcomes compared every 6–8 weeks. Another is a protocol where participants complete a series of ALI-enabled sessions plus established measures such as PAQ or TAS at baseline and follow-up, so we can examine how ALI composites relate to and predict change on existing scales.
In a research collaboration we aim to provide clearly documented composite and component-aligned scores, links back to the labeled excerpts that support those scores, and a data dictionary or codebook. For appropriate studies we also discuss options for providing de-identified text features or transcripts under a data use agreement.
Feelpath is currently self-funded. We are open to discussing financial support, in-kind engineering and analytics work, or other resources for well-scoped collaborative studies.
ALI follows the same HIPAA-aligned privacy and security practices as the rest of Feelpath: clear consent language about how session language is saved and analyzed, encryption in transit and at rest, access controls, and options for de-identified exports in research settings.
We are looking for thoughtful partners who care about alexithymia, language, and careful measurement. If this sparks ideas, we would love to talk.