Expeed Software

Profile set up by Edverise AI

Set Up By Edverise AI

Edverise AI built this profile by analyzing thousands of data points across the web. If you own this business, become an Edverise partner to manage and update your profile.

Become an Edverise Partner

Overview

$5,000+

Min Project Size

$25 - $49/hr

Avg Hourly Rate

50 - 249

Employees

2008

Year Founded

ABOUT EXPEED SOFTWARE

Custom Software Development Company

Founded in 2008, Expeed Software is an IT consulting firm that assists companies of various sizes in updating, integrating, and enhancing their applications and processes to deliver exceptional experiences. We specialize in software application development (both web and mobile), data analytics (including AI and machine learning), and strategies for digital transformation. With a team of over 100 employees operating from several office locations, we bring flexibility to every project. Our solutions leverage the reliable products and platforms of our technology partners, such as Qlik and Microsoft, enabling us to implement top-tier technology solutions for our clients.

Languages Spoken

English

HIGHLIGHTS

$5,000+

Min Project Size

$25 - $49/hr

Avg Hourly Rate

50 - 249

Employees

2008

Year Founded

Services

Focus

Industries

Clients

Services Provided

12% Mobile App Development (iOS & Android)

12% Backend Development

10% Custom Web Application Development

3% IT Staff Augmentation

8% AI Strategy & Roadmap Consulting

14% BI & Big Data Consulting & SI

8% Machine Learning Model Development

33% Operational Process Consulting

About the Team

OUR STORY

Founded in 2008 by CEO Rao Chejarla, Expeed Software is dedicated to empowering businesses through technology to achieve sustainable growth. With over three decades of technical expertise, Rao leads the company’s global operations, mentoring startups and collaborating with clients ranging from emerging companies to Fortune 500 enterprises. Expeed’s leadership team emphasizes transparency, high performance, and integrity, striving to deliver impactful results for customers, employees, and communities alike. Guided by a vision to architect the intelligent enterprise, the company pioneers strategic AI integration and seamless digital platforms, continually pushing industry boundaries. This commitment has established Expeed as a trusted global technology leader, focused on innovation and meaningful partnerships.

Portfolio

Automated Review Assignment System Enables QA Team

Unknown

Situation/Problem: Our radiology group was expanding, and the existing method for assigning peer reviews of exams for the Quality Assurance (QA) team was not sustainable. Previously, radiologists selected which exams to review, while the QA team manually filled in gaps in coverage related to modality, interpreter, and site, creating a logistical challenge for the team. As the practice grew to encompass numerous sites and handle thousands of exams daily, this system resulted in uneven workloads, with some radiologists opting for significantly simpler exams, leading the QA team to rely more on those radiologists known for their prompt and efficient responses. Solution: Expeed developed a system to establish rules for assigning exam reviews, which could be applied at various levels. For example, a rule might apply to the entire practice or to an individual radiologist. Specific categories included sites and types of personnel, such as new hires. These categories were dynamically linked to the company’s database, so changes, like the hiring of a new radiologist, would automatically update the available options. Additionally, an amount could be specified for radiologists to whom a rule applied, either as a percentage or a fixed number. Outcome: The QA team could keep pace with the ongoing growth, consistently delivering the contracted reviews to medical providers with whom service level agreements were in place. Additionally, radiologists felt that the workload for each review was fairly acknowledged, rather than being treated as uniform. Ultimately, the time burden on the QA team was greatly reduced, allowing them to concentrate on more critical tasks instead of randomly assigning exam accession numbers for review.

More Accurate & Timely Billing Coding

Unknown

**Situation:** Medical billing plays a crucial role in the functioning of healthcare practices by ensuring the revenue cycle operates smoothly. It is essential to use the correct medical billing codes along with all required supporting documentation to accurately bill health insurance providers and secure proper reimbursements. For instance, healthcare providers need to supply certain details, such as the medical justification for a procedure, to ensure that insurers process payments correctly. **Problem:** Occasionally, healthcare providers do not provide adequate information regarding the procedures performed, which can hinder billing coders. To address these gaps, requests for information (RFIs) must be made, requiring physicians to respond, which diverts valuable time from patient care. Additionally, missing information can lead to reduced payments from insurance companies, ultimately raising overall healthcare costs. **Solution:** Our Director of Data Science oversaw the development of a real-time natural language processing (NLP) algorithm that ingests and analyzes dictation reports while ensuring the protection of PHI and compliance with HIPAA. This algorithm extracts key information needed by medical coders pertaining to the described procedures. If essential details were missing—such as those related to the type of exam (e.g., X-ray, MRI)—the physician would receive prompts to include specific information before finalizing the report. **Outcome:** When this solution was put into action, the billing department managed approximately 300,000 dictations each month and was over a month behind schedule. The testing of the billing solution on submitted dictations verified the completeness of information and determined any additional information needed by insurance providers, processing 30,000 dictations in just 10 minutes.

NLP to Identify Urgent Treatment

Unknown

Situation/Problem: Our client faced challenges with its healthcare personnel being overwhelmed, which hindered their ability to focus on patients requiring immediate care. Solution: Amid the COVID-19 pandemic, Expeed Software seized the chance to assist! We utilized natural language processing techniques on approximately 5,000 medical dictations to create a classification model that assessed the urgency of patients' treatment needs. Key aspects of model development included: Outcome: The AI solution we developed supported healthcare providers by pinpointing urgent care needs, reducing the additional burden on the already strained staff. The model achieved over 95% accuracy, effectively identifying urgent cases in most situations and enhancing our client's capacity for swift and precise triage.

5 more projects

Projects & Clients

Project Size & Rate

Minimum Project Size

$5,000+

Average Hourly Rate

$25 - $49/hr

Pricing Snapshot

Min Project Size

$5,000+

Avg Hourly Rate

$25 - $49/hr

Cost Rating

N/A

What Clients Have Said

"Expeed Software delivers quality projects within budget and timeline expectations based on verified client feedback."

Based on verified reviews and internal benchmark data

PROJECT BUDGETS

No budget data yet — reviewers either haven't shared a project budget or kept theirs confidential.

Select a service

Mobile App Development (iOS & Android)

Backend Development

Custom Web Application Development

IT Staff Augmentation

AI Strategy & Roadmap Consulting

BI & Big Data Consulting & SI

Machine Learning Model Development

Operational Process Consulting

Industry Recognitions

Microsoft Partner Solutions Designations

View source

Locations

Headquarters

100 West Old Wilson Bridge Road

Columbus, Ohio, United States 43085

+16145160789

Reviews For Expeed Software

Submit A Review

No Reviews Yet

Be the first to share your experience working with Expeed Software.

Write a Review