Nursa Intelligence Assistant enables rapid posting of single or bulk shifts

SALT LAKE CITY, September 04, 2025–(BUSINESS WIRE)–Nursa, a nationwide platform that exists to put a nurse at the bedside of every patient in need, today announced the launch of an artificial intelligence assistant that enables healthcare facilities to rapidly generate shift listings within the Nursa platform. The first-of-its-kind smart scheduling tool helps organizations post single or bulk shifts within seconds so they can reach qualified, available clinicians immediately.

Active now within the Nursa platform, the Nursa Intelligence Assistant or “NIA,” allows post creation three ways: users can speak directly to NIA, describing their shift needs; they can take a photo of relevant shift information, even if it’s a handwritten scribble; and they can upload any spreadsheet or file used to track scheduling. From there, NIA fills in the details, letting users review and edit, and confirm pricing, before posting.

Carlee Scholl, staffing coordinator at Sullivan Park Care Center in Spokane, Wash., manages up to 150 shifts per month and recently began using NIA to schedule individual and bulk shifts. She described the experience as quick and accurate, with the AI assistant capturing all the details perfectly. “I just looked it over to make sure it was everything that I needed,” she said. “It was spot on.”

“Artificial Intelligence is opening up new opportunities to streamline cumbersome workflows so healthcare facilities can focus on the important business of delivering quality patient care,” said Curtis Anderson, CEO and founder of Nursa. “With NIA, facilities eliminate the repetitive typing and data entry of shift posting by generating one or thousands of shifts in just seconds. We’re redefining what fast and easy staffing feels like, and this is just the beginning.”

For more information on how Nursa helps healthcare facilities, hospitals and health systems solve staffing needs with qualified clinicians, visit nursa.com.

About Nursa

Nursa is a nationwide platform that exists to put a nurse at the bedside of every patient in need, removing the financial strain and operational gaps of traditional staffing agencies. Nursa’s technology enables hospitals, health systems, skilled nursing facilities and community organizations to easily secure reliable, qualified, nursing talent for per diem shifts and contract work. Founded in 2019 and headquartered in Salt Lake City, Nursa is trusted by a growing community of more than 3,400 facilities and 400,000 nurses nationwide and is accredited by The Joint Commission. For more information, visit nursa.com.

For many Hispanics the road to homeownership is filled with obstacles, including loan officers who don’t speak Spanish or aren’t familiar with buyers who may not fit the boxes of a traditional mortgage applicant.

Some mortgage experts are turning to artificial intelligence to bridge the gap. They want AI to help loan officers find the best lender for a potential homeowner’s specific situation, while explaining the process clearly and navigating residency, visa or income requirements.

This new use of a bilingual AI has the potential to better serve homebuyers in Hispanic and other underrepresented communities. And it’s launching as federal housing agencies have begun to switch to English-only services, part of President Donald Trump’s push to make it the official language of the United States. His executive order in August called the change a way to “reinforce shared national values, and create a more cohesive and efficient society.”

The number of limited-English households tripled over the past four decades, according to the Urban Institute, a nonprofit research organization based in Washington, D.C. The institute says these households struggle to navigate the mortgage process, making it difficult for them to own a home, which is a key factor in building generational wealth.

Bilingual AI helps demystify home loans

The nonprofit Hispanic Organization of Mortgage Experts launched an AI platform built on ChatGPT last week, which lets loan officers and mortgage professionals quickly search the requirements of more than 150 lenders, instead of having to contact them individually.

The system, called Wholesale Search, uses an internal database that gives customized options for each buyer. HOME also offers a training program for loan officers called Home Certified with self-paced classes on topics like income and credit analysis, compliance rules and intercultural communication.

Cubie Hernandez, the organization’s chief technology and learning officer, said the goal is to help families have confidence during the mortgage process while pushing the industry to modernize. “Education is the gateway to opportunity,” he said.

HOME founder Rogelio Goertzen said the platform is designed to handle complicated cases like borrowers without a Social Security number, having little to no credit history, or being in the U.S. on a visa.

Faster applications for buyers

Loan officer Danny Velazquez of GFL Capital said the platform has changed his work. Before, he had to contact 70 lenders one by one, wait for answers and sometimes learn later that they wouldn’t accept the buyer’s situation.

The AI tool lets him see requirements in one place, narrow the list and streamline the application. “I am just able to make the process faster and get them the house,” Velazquez said.

A homebuyer’s experience

One of Velazquez’s recent clients was Heriberto Blanco-Joya, 38, who bought his first home this year in Las Vegas. Spanish is Blanco-Joya’s first language, so he and his wife expected the process to be confusing.

Velazquez told him exactly what paperwork he needed, explained whether his credit score was enough to buy a home, and answered questions quickly.

“He provided me all the information I needed to buy,” Blanco-Joya said. “The process was pleasant and simple.”

From their first meeting to closing day took about six weeks.

Safeguards for accuracy

Mortgage experts and the platform’s creators acknowledge that artificial intelligence creates new risks. Families rely on accurate answers about loans, immigration status and credit requirements. If AI gives wrong information, the consequences could be serious.

Goertzen, the CEO of HOME, said his organization works to reduce errors by having the AI pull information directly from lenders and loan officers. The platform’s database is updated whenever new loan products appear, and users can flag any problems to the developers.

“When there are things that are incorrect, we are constantly correcting it,” Goertzen said. “AI is a great tool, but it doesn’t replace that human element of professionalism, and that is why we are constantly tweaking and making sure it is correct.”

Loan officers welcome AI support

Jay Rodriguez, a mortgage broker at Arbor Financial Group, said figuring out the nuances of different investors’ requirements can mean the difference between turning a family away and getting them approved.

Rodriguez said HOME’s AI platform is especially helpful for training new loan officers and for coaching teams on how to better serve their communities.

Another company is testing similar AI tools

Better Home & Finance Holding Company, an AI-powered mortgage lender, has created an AI platform called Tinman. It helps loan officers find lenders for borrowers who have non-traditional income or documents, which is common among small business owners.

They also built a voice-based assistant called Betsy that manages more than 127,000 borrower interactions each month. A Spanish-language version is in development.

“Financial literacy can be challenging for Hispanic borrowers or borrowers in other underserved populations,” said Leah Price, vice president of Tinman platform. “Tools like Betsy can interact and engage with customers in a way that feels supportive and not judgmental.”

The ability to understand and respond to specific references within a video, relating to both where and when events occur, represents a crucial next step for artificial intelligence. Honglu Zhou, Xiangyu Peng, Shrikant Kendre, and colleagues at Salesforce AI Research address this challenge with Strefer, a novel framework that empowers Video LLMs with advanced spatiotemporal reasoning capabilities. Strefer generates synthetic instruction data, effectively teaching these models to interpret fine-grained spatial and temporal references within dynamic video footage, without relying on expensive or time-consuming human annotation. This approach significantly improves a Video LLM’s ability to understand complex instructions involving specific objects, locations, and moments in time, paving the way for more versatile and perceptually grounded AI companions capable of interacting with the real world. The results demonstrate that models trained with Strefer-generated data outperform existing methods on tasks requiring precise spatial and temporal understanding, establishing a new benchmark for instruction-tuned video analysis.

Data Synthesis and VLM Evaluation Strategies

This research details a project focused on building more robust and accurate Video Language Models (VLMs) to improve their ability to understand and reason about video content, particularly in complex scenarios involving temporal reasoning, object localization, and nuanced descriptions. The core goal is to address limitations of existing VLMs, which often struggle with tasks requiring precise temporal understanding or grounding in specific video segments. The project relies heavily on generating synthetic data to target the weaknesses of existing VLMs, challenging the model in areas where it struggles. This is achieved through a process called Strefer, and the data covers a wide range of tasks categorized as open-ended question answering, multiple-choice question answering, temporal reasoning, object localization, and reasoning about actions and behaviors.

The data format varies, specifying how much of the video is used as input, and whether frames are extracted from a segment or the full video. Many tasks have mask-refer versions, where the question focuses on a specific region of interest in the video, forcing the model to ground its answers in the visual content. To improve the model’s ability to understand time, the research uses a technique that discretizes continuous time into segments, representing each segment with a temporal token added to the language model’s vocabulary. This allows it to process time-related information more effectively. Existing models struggle with understanding complex video content when queries rely on precise spatial locations or specific moments in time. Strefer addresses this limitation by systematically creating detailed, object-centric metadata from videos, including the location of subjects and objects as tracked over time, and their associated actions. This innovative approach leverages a modular system of pre-trained models, including Large Language Models and multimodal vision foundation models, to pseudo-annotate videos with temporally dense information.

By building upon this structured metadata, Strefer guides language models in generating high-quality instruction data specifically designed to train Video LLMs in understanding and responding to complex spatiotemporal references. Unlike existing datasets, Strefer automatically produces instruction-response pairs at scale, grounded in the dynamic, object-centric structures within videos. Current models struggle with detailed spatial and temporal reasoning, particularly when interpreting gestures or time-based cues in user queries. Strefer addresses this limitation by automatically generating synthetic training data that includes rich, detailed information about objects, their locations, and actions occurring at specific moments in time. By using a combination of existing AI models to annotate videos with this detailed metadata, Strefer creates a large dataset without the need for costly human annotation.

Experiments demonstrate that video models trained with this synthetically generated data outperform existing models on tasks requiring spatial and temporal disambiguation, showing enhanced reasoning abilities. The authors acknowledge that the framework relies on the accuracy of the underlying AI models used for annotation. Future work may focus on refining the annotation process and exploring the application of Strefer to more complex real-world scenarios.