Request for Startups 2025

HR operations & reporting

1. Payroll processing — There are 157,230 payroll professionals in the US making $26.29 an hour on average. That’s $8.6 billion spent on payroll management by US companies each year. Assuming that running payroll takes at least 5 hours per month, that equates to $250M in annual spend; but with the added complexities of international employees and commissions, it could take even longer. An agent that can cut the time these professionals spend on routine administrative work could save these companies hundreds of millions.

2. Leave management — Large companies have complex leave processes that include various types of leave, accrual rules, and eligibility criteria, all of which lead to increased administrative burdens. The average HR professional manages 85.6 leaves a year, which, even with leave management software, can still require lots of compliance and communication work. An AI agent dedicated to collecting and parsing requests and policies alongside an HR professional could dramatically cut administrative costs.

3. Buddy punching prevention — Buddy punching — the act of having an employee punch a timecard for another employee that’s not present — costs businesses up to $373M a year. Running regular audits and monitoring time tracking systems for anomalies is cumbersome, but with an AI agent involved, employers may be able to detect buddy punching and minimize these costs or eliminate them altogether.

4. HR compliance and operations — Workplace regulations are constantly changing, meaning HR professionals have to spend time updating company policies to comply. An AI agent for HR compliance could use natural language processing to analyze new laws and regulations, then automatically notify HR teams of critical updates and suggest policy changes. Once approved, the agent would also be able to automate things like policy creation, employee handbook updates, and compliance reporting.

There’s additional opportunity to create an AI agent for global workforce compliance that helps multinational companies manage complex international labor laws and regulations. This agent would maintain an up-to-date database of employment laws across different countries and regions, providing real-time guidance on hiring practices, work hour regulations, benefits requirements, and termination procedures. It would also assist in creating compliant employment contracts and policies tailored to each jurisdiction, helping companies avoid costly legal pitfalls when operating across borders. 

Retirement

1. 401(k) TPA management — Third party administrators (TPAs) manage many day-to-day aspects of employee retirement plans, including designing plan docs, preparing statements, and auditing plans. TPAs influence 30% of 401(k) assets, which equates $850B across hundreds of thousands of plans with millions of active participants. Lots of TPA processes require compiling data, updating reports, and filling out compliance forms, all of which can be streamlined with an AI agent. Unlocking capacity of existing TPA professionals would allow them to take on more business and offer additional services that they currently do not have bandwidth for.

2. Advisor assistant — There are 272,190 financial advisors in the US that offer services to individuals but have the opportunity to work with SMBs by helping to manage their retirement plans. Designing plans and creating compliant documentation can be streamlined with an AI agent augmenting templates and pitches. Additionally, this agent can help address common client questions proactively, allowing the advisor to spend more time building client relationships. 

3. Recordkeeping system manager — As of 2022, there were 686K 401(k) plans in the US with nearly 80M active participants. Recordkeepers are responsible for running set up, compliance checks, and contribution management for each of these plans, and often have to work in complex legacy systems. Having an AI agent that directly integrates with systems like FIS Relius, SS&C, and Schwab Retirement Technologies can automate key workflows and allow recordkeepers to focus on driving the best sponsor experience.

4. Personalized retirement planning — Most Americans set-and-forget their 401(k) contributions, defaulting to whatever investments and allocations their plan’s 3(16) fiduciary elects. Many plans have hundreds or thousands of participants, making it impossible to personalize saving and investment strategies for every employee. A personalized retirement planning AI agent could analyze individual employees’ financial data, career trajectory, and retirement goals to create tailored retirement benefit packages to optimize 401(k) contributions, suggest investment allocations, and provide detailed retirement readiness assessments. This could help employees make informed decisions about their own long-term financial planning, setting them up for greater success in the future.  

5. Multi-generational retirement planning — The personalized retirement planning agent could help employees make informed decisions, while a multi-generational planning agent could inform 3(16) fiduciaries’ investment strategies for different age groups within an organization. The agent might analyze demographic data, financial trends, and individual employee preferences to create customized retirement plans for each generation (e.g., Baby Boomers, Gen X, Millennials, Gen Z). By providing personalized education and engagement strategies, this agent could help retirement sponsors boost participation and optimize savings across all of an organization’s age groups.

Health benefits administration

1. Benefits optimizer — About two-thirds of employers are looking to switch health insurance carriers over the next four years, likely seeking to reduce costs and improve the member experience. Choosing a new benefits plan is a time-intensive process that involves analyzing dozens of carriers, weighing plan options, and understanding the diverse needs of the organization’s workforce. An AI-driven benefits optimization agent could analyze employee health data, usage patterns, and industry trends to recommend the most cost-effective and comprehensive health benefit packages for both employers and employees, significantly cutting down the time HR professionals spend conducting research. 

Once a coverage plan is in place, the agent could also help both employers and employees to maximize the benefits. For employers, this might look like continuously monitoring claims data, identifying potential cost-saving opportunities, and suggesting preventive care programs to improve overall employee health outcomes. For employees, could provide personalized recommendations for maximizing their health benefits based on their individual needs and circumstances.

2. Predictive health benefits utilization agent — Similar to an optimization agent, AI could be used to predict an organization’s health care needs and costs in the future, helping businesses to prepare for what’s to come. This agent could analyze historical claims data, demographic information, and health trends to predict future utilization patterns. With this information, the agent could recommend adjustments to benefit plans, suggest targeted wellness programs, and help employers budget more accurately for health care expenses.

3. Benefits enrollment agent — HR professionals are expected to help employees make the most of their benefits. This is especially relevant for younger generations — 81% of Zillennials (those aged 18–43) say HR departments should provide more help during open enrollment. An AI agent could help to streamline the administrative work of open enrollment through form filing assistance, real-time data validation, and auto-generated reports on enrollment participation, freeing up time for HR professionals to consult with employees and help them make the best elections. An AI agent might even power a chatbot that could help employees answer common questions and deliver personalized guidance for choosing the right benefits based on their individual circumstances, past selections, or usage patterns.

4. Quoting agent — Insurance brokers spend much of their time preparing quotes for employers — time that could otherwise be spent consulting with these clients. AI agents have the potential to automate much of this process by plugging into existing insurance APIs and quoting software. The agent could quickly pull data from multiple carriers, plans, and pricing structures, then automatically draft personalized proposals tailored to client needs with plan comparisons, pricing options, and key benefits. 

Innovate with Finch

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