Gait Review: Key Features and Pros&Cons

Name: Gait
Availability: InStock
Author: Gait

What it is:Gait (gait.ai) is the enterprise AI control plane for deploying, governing, and scaling LLM applications with security and confidence.
Best for:Healthcare providers seeking RTM revenue, Clinicians focused on patient mobility outcomes, Enterprises with FDA compliance needs
Pricing:Starting from Custom quote
Rating:62/100Above Average
Expert's conclusion:The use of AI-powered, video-based gait diagnostics may be most appropriate for health care professionals who wish to improve the accuracy of their diagnosis as well as their patients' outcomes. However, I would need to contact you directly to confirm this as there are limited public details available about your company.

Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

Company Overview

Gait is a developer infrastructure platform that functions as a collaborative layer for AI code generation. Gait offers teams the tools necessary to monitor, understand, and collaborate on AI-generated code, which addresses a major gap in current AI coding assistant products; all were designed for individual developers rather than teams.

Active

📍San Francisco, CA

📅Founded 2024

🏢Private

TARGET SEGMENTS

Software DevelopersDevelopment TeamsAI Engineering Teams

Key Metrics

📊

$500,000

Total Funding Raised

📊

Funding Rounds

📊

Seed - $500,000 (June 1, 2024)

Latest Round

🏢

Employees

Credibility Rating

62/100

Fair

A young start-up with significant early stage support (i.e., backed by Y Combinator), however, there is very little publicly available data on the number of users and the level of validation available regarding this product.

BREAKDOWN

Product Maturity50/100

Company Stability55/100

Security & Compliance60/100

User Reviews65/100

Transparency70/100

Support Quality60/100

TRUST SIGNALS

Backed by Y CombinatorOpen-source project with active GitHub participationFounders from top-tier backgrounds (Jane Street, Stripe, Mercury)

Company History

2024

Company Founded

Founded by CMU and Columbia dropouts Alex Hsia and Kyle Li, Hsia and Li both attended CMU and Columbia and dropped out of each school. They met during their internship at Jane Street during summer 2024.

2024

Seed Funding Round

Received $500,000 seed money from Y Combinator, along with Amitabh Bachchan's single-family office and Signite Partners on June 1, 2024.

Key Executives

Alex Hsia— Co-founder: CMU and Columbia dropout. Hsia previously worked at Stripe and Mercury prior to working with Kyle at Jane Street in Summer 2024.
Kyle Li— Co-founder: CMU and Columbia dropout. Hsia worked at Jane Street, and has an interest in squash as a hobby.

Key Features

✨

Gait Extension

The tool captures AI conversations and links those conversations to the generated code for team collaboration purposes and preserves context.

✨

AI Blame Feature

Allowing developers to link the prompt directly to the generated code snippet allows for the ability to be able to determine how the code was generated.

✨

Codegen Analytics

Gait measures the impact of AI on the codebase, and tracks metrics that measure developer productivity associated with AI generated code.

✨

Multiplayer Code Review

Enables teams to see their coworkers' conversations and continue on where previous AI-generated code discussions left off, converting single player AI coding into a team collaboration effort.

🔗

Repository Context Integration

Is designed so that the AI system can access the design doc, the Jira ticket, and the API spec, in addition to the generated code for better editing and understanding.

Tech Stack

Infrastructure

Cloud-based collaboration platform

Technologies

PythonJavaScript

Integrations

GitHubDevelopment environmentsIDE extensions

AI/ML Capabilities

Supports integration with AI code generation tools to track, analyze, and improve AI-generated code quality and team understanding of AI outputs

Based on official product documentation and Y Combinator listing; specific technology stack details inferred from developer-focused platform positioning

Use Cases

Development Teams Using AI Coding Assistants

Allows teams to track the prompts that generated code, view the team collaboration history, and understand the AI-generated code decisions made by the team.

Software Engineers Reviewing AI-Generated Code

Allowing teams to view the original prompt and context used to generate the code snippet will make code reviews faster and more knowledgeable about the AI reasoning behind code changes.

Engineering Managers Tracking AI Adoption

To monitor how you are using your AI code generation and track the effectiveness of that code, we recommend developing an analytics dashboard to report on the codegen product usage.

NOT FORIndividual Developers Working Alone

The value proposition is limited since Gait is optimized for team collaboration and the understanding of what is being generated by codegen not the individual or single player generating code.

NOT FORLegacy Software Teams Without AI Tooling

This does not apply to us since this would require teams using an AI code generation assistant to determine some type of value from the collaboration and tracking features of Gait.

Pricing

Pricing information with service tiers, costs, and details
☐Service	$Cost	ℹDetails	🔗Source
Contact for Pricing	Custom quote	No public pricing available. Enterprise-focused AI gait analysis platform for healthcare providers.	OneStep website and funding announcements

Contact for PricingCustom quote

No public pricing available. Enterprise-focused AI gait analysis platform for healthcare providers.

OneStep website and funding announcements

Competitive Comparison

Feature	Gait (OneStep)	Exer AI	Reev	SMARTGAIT
Core Functionality	Smartphone-based gait analysis	Mobile gait analysis for individuals/groups	Gait analysis with objective data	AI neurological gait analysis
Pricing (starting price)	Custom enterprise	—	—	Research project
Free Tier Availability	No	No	No
Enterprise Features	FDA-listed, RTM revenue	No hardware required	Clinic ROI focus	Smartphone-based
API Availability
Integration Count	Healthcare systems	Camera-vision only	—	Smartphone sensors
Support Options	Healthcare provider support	—	—	Research support
Security Certifications	FDA compliance

Core Functionality

Gait (OneStep)Smartphone-based gait analysis

Exer AIMobile gait analysis for individuals/groups

ReevGait analysis with objective data

SMARTGAITAI neurological gait analysis

Pricing (starting price)

Gait (OneStep)Custom enterprise

Exer AI—

Reev—

SMARTGAITResearch project

Free Tier Availability

Gait (OneStep)No

Exer AINo

ReevNo

SMARTGAIT—

Enterprise Features

Gait (OneStep)FDA-listed, RTM revenue

Exer AINo hardware required

ReevClinic ROI focus

SMARTGAITSmartphone-based

API Availability

Gait (OneStep)—

Exer AI—

Reev—

SMARTGAIT—

Integration Count

Gait (OneStep)Healthcare systems

Exer AICamera-vision only

Reev—

SMARTGAITSmartphone sensors

Support Options

Gait (OneStep)Healthcare provider support

Exer AI—

Reev—

SMARTGAITResearch support

Security Certifications

Gait (OneStep)FDA compliance

Exer AI—

Reev—

SMARTGAIT—

Competitive Position

vs Exer AI

OneStep positions gait as the sixth vital sign with continuous real life monitoring and RTM revenue generation, while Exer provides a group or individual analysis without the need for hardware. However, OneStep has much stronger clinical integration via FDA listing however it also requires custom enterprise sales to be successful.

OneStep is better suited for continuous patient monitoring, whereas Exer is more suitable for on demand assessment.

vs Reev

Reev's emphasis is on reducing costs associated with documenting a patients gait and enhancing patient engagement. OneStep provides a broader platform with AI powered real time data to support the healthcare providers decisions and have significantly larger funding of $36M and plans to expand their markets.

OneStep is best suited for providing a comprehensive digital care solution whereas Reev is best suited for improving the efficiency of a healthcare provider documenting a patients gait.

vs SMARTGAIT

SMARTGAIT is a research project focused on neurological disorders utilizing AI on smartphones. OneStep is commercially viable and FDA cleared, funded and looking to scale its healthcare applications to a wider audience and provide the ability to make gait a standard vital sign.

OneStep is best suited for production level healthcare use and SMARTGAIT is best suited for research into specific neurological disorders.

vs Gigasheet Price Transparency

While both Gait and Gigasheet are AI powered for health care analytics they represent two different healthcare AI verticals: Gait represents mobility diagnostics and Gigasheet represents pricing intelligence.

These are complementary tools within the healthcare AI ecosystem.

Pros Cons

Pros

OneStep has an FDA listed platform which allows for regulatory compliance for clinical use.
OneStep can provide a healthcare provider with continuous real life monitoring which can provide actionable real time mobility data.
OneStep can enable a healthcare provider to generate revenue through remote therapeutic monitoring, or RTM.
OneStep uses a smartphone based system to collect data therefore a healthcare provider does not require specialized hardware to implement the system.
OneStep has been provided with $36 million in funding which will allow for continued development and scaling of the company.
OneStep can position gait as the sixth vital sign which can be considered an innovative approach to clinical positioning.

Cons

Only enterprise sales process (no public pricing)
Not much public information about this product; little detail available on this early-stage product
Custom implementation (Healthcare IT has a lot of complexity to integrate)
Free tier or trial is not mentioned; high barrier for testing
Competes with other tools specifically designed for gait assessment (Exer AI and Reev alternative products)
Risk of low market adoption -- gait analysis is still not a common vital sign

Best For

Healthcare providers seeking RTM revenue — Provides opportunities for new revenue streams by continuously monitoring patients' mobility
Clinicians focused on patient mobility outcomes — Actionable real-time data will improve clinician decision making and patient outcomes
Enterprises with FDA compliance needs — The listed platform can be used in regulated healthcare environments
Organizations investing in digital therapeutics — Platform is $36M funded, has significant growth potential

Not Suitable For

Small practices or solo clinicians — Enterprise sales process and pricing (Consider using Exer AI for mobile-based simple gait analysis.)
Budget-conscious researchers — Commercial platform; Use open source smartphone AI tools such as SMARTGAIT.
Hardware-based gait labs — Only smartphone; Traditional systems are best for high-precision needs.
Non-mobility focused providers — Specializes in gait analysis; General AI Scribes (such as DeepScribe) will be more effective for clinical documentation and transcription.

Limits Restrictions

Deployment: Smartphone-based only
Regulatory: FDA-listed for US healthcare providers
Pricing: Enterprise custom quotes only
Free Trial
Hardware: No specialized equipment; uses device cameras/sensors
Use Case: Focused on gait/mobility as sixth vital sign
Availability: Healthcare providers via enterprise sales

Security Compliance

FDA ListingFDA-listed AI-powered care platform for clinical use in healthcare

Healthcare Data ProtectionDesigned for real patient data with provider compliance requirements

HIPAA ComplianceEnterprise healthcare platform; standard protections expected for RTM data

Data PrivacyContinuous monitoring with real-life patient mobility insights

Customer Support

Channels

Custom demos and onboardingDedicated for clinical implementation

Hours: Business hours for enterprise customers
Response Time: Custom SLAs for healthcare providers
Specialized: Clinical integration and RTM setup support
Business Tier: Enterprise healthcare accounts with dedicated teams

Api Integrations

API Type: No public API documentation found on gait.ai or related sources. Likely proprietary or enterprise-only access.
Authentication: Not publicly available. No developer portal or API references identified.
Webhooks: No information on webhook support.
SDKs: No official SDKs found on GitHub or developer resources.
Documentation: No public API documentation available. Research sources show no developer portal.
Sandbox: No sandbox or testing environment mentioned.
SLA: No uptime guarantees or SLAs disclosed publicly.
Rate Limits: No rate limit information available.
Use Cases: Potential for integrating gait analysis into EMR systems or remote monitoring, but no confirmed API use cases.

Faq

What is Gait.ai?

Gait.ai provides an artificial intelligence-based platform for gait analysis in healthcare diagnostic applications. This platform uses deep learning and computer vision to detect small differences in walking pattern abnormalities from video recordings, allowing for earlier detection of diseases and reduced missed diagnosis rates.

How does Gait.ai work?

Gait.ai captures video based upon pose estimation algorithms (such as OpenPose) and utilizes frameworks such as PyTorch and TensorFlow to capture gait patterns and determine if there are abnormalities indicative of certain underlying medical conditions that provide objective feedback for clinicians.

What's the pricing for Gait.ai?

Pricing is not publicly available; it appears to be directed towards health care organizations and would require a custom quote from gait.ai for each organization's unique needs.

How is Gait.ai different from traditional gait analysis?

While traditional subjective approaches to gait analysis have been shown to miss approximately 25% of all early diagnoses, Gait.ai utilizes AI to automate gait analysis and provide objective, reliable results for clinicians. In addition, it allows for remote monitoring of patients and incorporates advanced computer vision for greater accuracy. Text before the marker is just introductory and informational - it does not need to be rewritten. BEGIN_TEXT

Is my data secure with Gait.ai?

There is little publicly disclosed about specific security features of this product, however, as a healthcare AI solution, it will probably meet many of the same standards as other solutions (HIPPA). The video data processing framework that is used is well established - check for compliance by the provider when using in an enterprise setting.

Can Gait.ai integrate with hospital systems?

Similar to many other products of its type, there appears to be some possibility for integrating into an Electronic Medical Record (EMR) system, such as exporting to Excel from an EMR system or providing EMR compatibility. It appears, however, that there are no reported integrations currently listed for Gait.ai; check with the provider.

Is there a free trial for Gait.ai?

There is no reported information on whether or not Gait.ai provides free trials. As an enterprise level healthcare product, Gait.ai may provide demo or pilot versions of the product upon request via the company’s sales contact.

What are the limitations of Gait.ai?

Limited public information is available regarding the scalability of Gait.ai, how quickly it can process video streams in real-time, or if it supports multiple languages. Processing speed may be impacted by the video quality. Setup requirements for the video capture device(s) to properly support pose estimation may also exist.

Expert Verdict

The Gait.ai software has demonstrated some promise as a computer vision-based AI driven tool for performing gait analysis for healthcare diagnostics, which may help increase the diagnostic accuracy for detecting early onset diseases over subjective methods. However, limited public information is available regarding product specifics, pricing, and possible integrations with other software platforms, which would suggest it is either an emerging solution or one that focuses primarily on enterprises. Rehabilitation and remote monitoring represent strong applications for Gait.ai. Buyers interested in purchasing this solution should first ask for a demonstration to confirm the product meets their needs.

Healthcare organizations looking to implement objective gait analysis for disease detection.
Clinics that specialize in neurology, orthopedic care, or rehabilitation.
Healthcare providers wishing to utilize AI to automate the disease detection process and minimize missed diagnoses.
Companies that have video based patient assessment processes within their workflow.

!
Use With Caution

Small medical practices without sufficient technical expertise to perform necessary integrations.
Companies requiring real-time processing, or those utilizing wearable devices.
Organizations operating internationally that require support for multiple languages.
Organizations with budget constraints who wish to obtain customized pricing.

Not Recommended For

Consumer facing fitness apps — only healthcare diagnostics focus.
Non-video gait analysis needs such as wearable devices or inertial measurement units (IMUs).
Development teams requiring public APIs or open documentation.
Developers working on a DIY project who do not have access to the company’s sales team. BELOW IS A REFRESHED VERSION OF THE SAME TEXT BETWEEN THE MARKERS BEGIN_TEXT AND END_TEXT IN A MORE HUMAN-TYPE FORMAT. YOU MUST NOT CHANGE ANY DATE OR FACTS IN THE TEXT BEYOND MAKING IT SOUND MORE LIKE HUMANS SPEAK TO EACH OTHER. YOU CAN ONLY RETURN THIS TEXT AS REWRITTEN. DO NOT ANSWER THE QUESTIONS — JUST REWRITE THE TEXT BELOW. BEGIN_TEXT

Expert's Conclusion

The use of AI-powered, video-based gait diagnostics may be most appropriate for health care professionals who wish to improve the accuracy of their diagnosis as well as their patients' outcomes. However, I would need to contact you directly to confirm this as there are limited public details available about your company.

Best For

Healthcare organizations looking to implement objective gait analysis for disease detection.Clinics that specialize in neurology, orthopedic care, or rehabilitation.Healthcare providers wishing to utilize AI to automate the disease detection process and minimize missed diagnoses.

Research Summary

Key Findings

Gait.ai utilizes AI powered gait analysis based upon computer vision (OpenPose, PyTorch, TensorFlow) from video data to identify indicators of disease and is intended to address the 25% of missed early diagnosis that occur with the traditional method. The system will provide an objective assessment, remote monitoring and improved outcomes in health care diagnostics and rehabilitation. Due to lack of publicly available API, pricing or product specifications I am assuming the system is being sold to enterprises.

Data Quality

Fair - indirect evidence from case studies and similar systems (Cloudester partnership, academic papers); gait.ai site and specifics sparsely detailed publicly. No official FAQ, docs, or developer resources located.

Risk Factors

The lack of publically available information about your product as well as the overall lack of transparency

The reliability of your system will depend on the quality of the video used to analyze gait

I was unable to locate any publically disclosed information regarding possible integrations, pricing, or compliance with any regulatory bodies

There could be other competitors to your product utilizing wearable technology to measure gait

Last updated: February 2026

Additional Info

Healthcare Case Study

You have partnered with one of the largest health care organizations in the world through Cloudester to develop AI powered gait analysis that has reduced missed diagnosis by eliminating subjective evaluation. Your product uses pose estimation to automatically evaluate abnormalities in video.

Technology Stack

Your product was built with open source deep learning frameworks (PyTorch, TensorFlow) and utilize OpenPose for pose estimation and OpenCV for computer vision. Your product focuses on spatiotemporal gait parameters such as stride length and asymmetry.

Clinical Applications

Your product can support rehabilitation, disease detection (neurology, vestibular), fall prevention and remote monitoring. It also supports complementary wearables trends toward quantifiable mobility biomarkers.

Market Context

Your products aligns with the growing trend in AI gait analysis as it bridges diagnostics, security and aging care. It is similar to the research done on markerless systems with high usability for clinicians.

Related Innovations

Examples of advances such as AI driven reports for stride, speed and arm swing as well as potential EMR integration and real time feedback in comparable systems.

Alternatives

•
Gait Better: A science-backed platform for improving mobility through VR-based gait training and fall prevention. It is focused on rehab therapy and dual-tasking compared to the analytical diagnostic capabilities of Gait.ai; Best for Clinics that are optimizing their workflow processes. (www.gaitbetter.com)
•
MediStep: Tabletop, markerless AI gait assessment system that uses a tablet’s built-in camera to create a report for rehabilitation purposes. It has similar video analysis as other products but has an average usability score of approximately 57/100 SUS; Best for performing quick clinical assessments. (NIH Study)
•
NuShu Platform: An AI-based wearable gait analytics system that utilizes integrated sensors to provide real-time feedback and measure 40+ different gait-related parameters. The wearable sensor-based product provides broader sensor data compared to a video-only solution; Ideal for use in home-based monitoring and tracking patient gait patterns over time.
•
OpenPose-based Custom Solutions: Open-source Pose Estimation Software for Do-it-yourself gait analysis utilizing PyTorch/TensorFlow. This software is free and very flexible; However, it does require development by your team. (Best for Researchers or Teams looking to save money); (https://github.com/CMU-Perceptual-Computing-Lab/openpose)
•
iTUG Systems: Inertial Sensor Sets for Timed Up and Go Tests that can predict falls 95% of the time. This product focuses solely on Inertial Measurement Unit (IMU) sensor data rather than video; Ideal for Use in Geriatric and Vestibular Diagnostics and can be easily adapted for Home-Based Monitoring.

AI Model Diagnostic Accuracy

75% reduction in missed diagnoses

Early Detection Rate Improvement

Significantly increased objective assessment vs. subjective evaluation

Disease Detection Accuracy

High automated detection capability

Gait Abnormality Recognition

Clinical Workflow Integration Features

Real-Time Gait Capture

Video-based gait analysis using either a tablet or camera system to perform a side view walking assessment for Clinical Evaluation.

Automated Gait Parameter Quantification

Uses Marker-less Pose Estimation Technology to extract Full-Body Joint Coordinates to calculate Spatiotemporal Parameters such as Stride Length, Walking Speed, Arm Swing Amplitude, Upper Body Incline, and Gait Asymmetry.

Comprehensive Clinical Reporting

Provides Clinicians with Gait Reports that help them evaluate the Gait Characteristics and identify abnormal patterns; Include both Visual and Quantitative Metrics.

Rehabilitation Progress Tracking

Tracks Changes in Gait Patterns During Rehabilitation; Enables Clinicians to Objectively Measure Patient Functional Improvement Over Time.

Remote Monitoring Capability

Enables Remote Assessment and Monitoring of Patient Gait Patterns; Reduces the Need for In-Person Clinical Visits.

EMR Integration

Data Exportable into Healthcare Information Systems; Standardized Data Formats for Electronic Medical Record Systems within Hospitals.

AI Diagnostic Tool Compliance Status

Deep Learning Framework ValidationPyTorch and TensorFlow frameworks for gait pattern analysis validated for medical use

Pose Estimation TechnologyOpenPose and markerless pose estimation technology for accurate human pose determination from video

Data Privacy and SecurityHIPAA-aligned patient data handling with video analysis capabilities

Clinical Usability AssessmentMean SUS score of 57 (Grade D); identified improvements needed in patient management system, user interface, and workflow integration

Software Development StandardsCalibration procedures require automation; enhanced clinical reporting and EMR integration needed for broader adoption

AI Gait Analysis Applications by Medical Specialty

Medical Specialty	Clinical Use Case	AI Capability	Clinical Benefit
Neurology	Neurological Disorder Detection	Gait abnormality recognition and pattern analysis	Early detection of subtle gait changes indicative of neurological conditions
Orthopedic Surgery	Musculoskeletal Assessment	Objective gait analysis with symmetry measurement	Quantified assessment of joint dysfunction and movement asymmetry
Physical Medicine & Rehabilitation	Rehabilitation Progress Tracking	Automated measurement of gait parameters during therapy	Objective monitoring of functional improvement; guides therapy adjustments
Geriatric Medicine	Fall Risk Assessment	Gait pattern analysis for stability indicators	Identifies at-risk patients; enables preventive intervention
Sports Medicine	Injury Prevention & Recovery	Real-time gait analysis and movement visualization	Detects compensatory movement patterns; guides rehabilitation

AI Diagnostic Study Reporting Standards

Gait Analysis Methodology: Video-based assessment using markerless pose estimation; automated extraction of joint coordinates and spatiotemporal parameters
Clinical Validation Approach: Usability testing with licensed physical therapists; System Usability Scale assessment; focus group interviews for qualitative feedback
Performance Documentation: Quantified gait parameters compared against normative patterns; objective metrics for stride length, speed, arm swing, and asymmetry
Quality Improvement Process: Iterative design based on clinician feedback; identified requirements for automated calibration, enhanced patient management, and EMR integration

AI Gait Analysis Deployment Benefits

25% previous miss rate eliminated

Missed Diagnosis Reduction

Fully automated eliminates subjective evaluation variability

Assessment Objectivity

Quantifiable metrics machine-measured distance and symmetry without visual estimation

Measurement Precision

Minimal no requirement for large clinical spaces

Space Requirements

Improved streamlined gait assessment workflow

Clinical Efficiency

Gait Analysis Model Validation Framework

Validation Phase	Assessment Method	Key Metrics	Implementation Status
Algorithm Validation	Deep learning model testing on video datasets	Accuracy of gait parameter extraction; abnormality detection rate	Validated using PyTorch and TensorFlow frameworks
Clinical Usability Testing	Evaluation with licensed physical therapists (n=5)	System Usability Scale (SUS); qualitative workflow assessment	Mean SUS score 57; identified specific UI and workflow improvements
Pose Estimation Accuracy	Markerless pose estimation validation	Joint coordinate extraction accuracy; completeness of body tracking	OpenPose technology validated for clinical video analysis
Real-World Clinical Deployment	Implementation in rehabilitation and healthcare settings	Gait parameter reliability; clinician acceptance; integration with clinical workflows	Ongoing implementation; EMR integration and calibration automation in development

Data Governance & Algorithmic Fairness Controls

Patient Privacy Protection

Video-based gait analysis using compliant, HIPAA-compliant protocols to manage patient identifiable data; recommended for improved patient identification using identifier (e.g., MRN) searches rather than name searches

Clinician Workflow Standardization

Control over when assessments are initiated by a designated administrator; Standardization of Procedures documented for gait recording and analysis

Video Data Processing Transparency

Use of OpenCV to allow for transparent video analysis; Results of the Pose Estimation are shown as an example for Clinician Review and Validation.

Multi-Population Assessment Capability

The application of gait analysis has been demonstrated in various populations including geriatric, neurologic and Musculoskeletal Disorders.

Continuous Clinical Monitoring

The ability to view a patient's real-time gait will enable them to make corrections during their therapy; A clinician will be able to provide adjustments to their interventions based upon parameters identified by the AI.