FullSeam: The AI Employee for Finance Teams

In the modern enterprise, finance and accounting departments sit at the center of operational stability. They manage cash flow, ensure compliance, process invoices, and maintain the integrity of financial records. Yet despite the mission-critical nature of their work, many of their daily tasks remain painfully manual — chasing payments, reconciling accounts, entering data, responding to vendor inquiries, and correcting errors across disconnected systems.

FullSeam, a Winter 2026 startup, was founded on a simple but transformative idea: what if finance teams didn’t just use software tools, but could actually hire an AI employee?

FullSeam positions itself not as another dashboard, analytics layer, or workflow tool, but as an autonomous digital teammate that logs into existing systems and performs routine finance and accounting work on behalf of human staff. Instead of forcing companies to change processes or migrate systems, the platform integrates directly into current tools — accounting software, ERP systems, banking platforms, billing tools, and CRM systems — and executes tasks the same way a human employee would.

By reframing automation as employment rather than software usage, FullSeam aims to redefine how organizations think about operational efficiency in finance.

Who Are the Founders Behind FullSeam?

FullSeam is led by a trio of experienced entrepreneurs: Aaron Coppa, Thomas Dowling, and Geoff Segal. The founders are not newcomers to the intersection of finance and technology. They previously built TaxProper, a property tax software platform serving institutional real estate investors, which ultimately processed more than one billion dollars annually in property tax payments before being acquired by Opendoor in 2022.

Aaron Coppa served as the founding engineer at TaxProper, gaining firsthand exposure to the technical challenges of scaling financial operations. Thomas Dowling, a Rhodes Scholar with degrees from the University of Illinois Urbana-Champaign and Oxford, co-founded TaxProper and later advised the Mayor of Chicago while serving as a commissioner of an Illinois state agency. Geoff Segal also co-founded TaxProper, contributing to its growth and eventual exit.

This shared history is crucial to understanding FullSeam’s origin. The founders experienced the operational pain of scaling finance teams during rapid company growth. They saw that while accounting software tracked transactions effectively, it did little to reduce the human effort required to manage them.

Their insight was that finance teams don’t necessarily need better tools — they need additional hands. FullSeam became their answer to that problem.

Why Did the Founders Decide to Build an AI Teammate?

The inspiration for FullSeam emerged directly from the founders’ experience at TaxProper. As their previous company scaled, the accounting department struggled to keep up with invoicing, reconciliation, vendor communication, and exception handling. The workload expanded faster than headcount could realistically grow.

Existing software systems performed record-keeping but not execution. Humans still had to send emails, upload documents, correct mistakes, and follow up repeatedly until payments were completed. Even the most advanced financial platforms required extensive manual intervention.

The founders realized that the industry had focused on building tools that displayed information rather than systems that completed work. Dashboards and analytics improved visibility but not productivity.

FullSeam was conceived as a response to this gap. Instead of adding another layer of software complexity, the startup sought to create an AI entity capable of performing the tasks themselves — logging into systems, interacting with customers and vendors, and updating records automatically.

In essence, they aimed to move from “software as a tool” to “AI as labor.”

How Does FullSeam’s AI Employee Actually Work?

FullSeam’s approach centers on integration rather than replacement. The AI agents connect to existing enterprise tools — ERP systems, billing platforms, spreadsheets, banking software, and CRM systems — without requiring organizations to overhaul their workflows.

Once connected, the AI can execute tasks across several core areas of finance operations.

For accounts receivable teams, FullSeam follows up with customers until payments are received, generates requested documentation, enters invoices into customer accounts payable portals, and updates financial records accordingly. The AI effectively becomes a persistent collections specialist that never forgets to send reminders or track outstanding balances.

For accounts payable teams, the system ingests invoices from multiple sources, including email attachments, vendor portals, and PDF documents. It matches invoices against purchase orders, contracts, and proof-of-service records, ensuring accuracy before processing payments. When information is missing, the AI contacts vendors directly to request clarification.

The platform also categorizes expenses and pushes clean entries into the general ledger, reducing the risk of accounting errors caused by manual data entry.

Beyond AR and AP functions, FullSeam assists with reconciliation, cash application, and other routine financial processes that traditionally consume significant staff time.

By operating within existing systems rather than replacing them, the AI minimizes friction during adoption.

What Problems Is FullSeam Trying to Solve for Accounting Teams?

Finance departments often face a paradox: they are expected to operate with high accuracy and strict compliance while handling large volumes of repetitive work under tight deadlines. Hiring additional staff increases costs, while outsourcing introduces coordination challenges and potential security concerns.

FullSeam targets the inefficiencies created by manual processes. Accounts receivable teams spend countless hours chasing payments and responding to customer inquiries. Accounts payable teams must process invoices, verify documentation, and resolve discrepancies. Reconciliation tasks require meticulous attention to detail and are prone to human error.

These processes are essential but rarely strategic. They drain resources that could otherwise be devoted to financial planning, analysis, and decision support.

FullSeam’s AI employee aims to absorb this operational burden, allowing human professionals to focus on higher-value work.

The startup also addresses the issue of process fragmentation. Finance teams often operate across multiple disconnected systems, leading to duplicated work and inconsistencies. By functioning as a cross-platform executor, the AI acts as a unifying layer.

How Does FullSeam Differ from Traditional Automation Tools?

Automation in finance is not new. Robotic process automation (RPA), workflow engines, and AI-powered analytics platforms have existed for years. However, these tools typically require extensive configuration, rule-building, and maintenance.

FullSeam’s distinction lies in its agent-based model. Instead of programming workflows manually, organizations deploy AI agents that behave more like human employees. They can interpret tasks, communicate with stakeholders, and adapt to changing conditions.

This approach reduces the need for technical setup and ongoing maintenance. It also allows the AI to handle exceptions — situations where traditional automation would fail because predefined rules do not apply.

Moreover, the startup emphasizes working within existing processes rather than forcing companies to redesign operations. This makes adoption more feasible for organizations reluctant to undergo major system migrations.

What Impact Could AI Employees Have on the Future of Finance Work?

If FullSeam’s model proves successful, it could signal a broader shift in how organizations structure their operational teams. Instead of expanding headcount to manage growth, companies might deploy digital workers that scale instantly.

Finance departments could transition from transaction processors to strategic advisors, focusing on forecasting, risk management, and business planning while AI handles execution.

The concept also raises questions about workforce transformation. Rather than replacing human professionals, AI employees could augment them, reducing burnout caused by repetitive tasks and enabling more meaningful work.

For rapidly growing startups, the ability to scale operations without proportional hiring could become a competitive advantage.

What Challenges Might FullSeam Face as It Grows?

Despite its promise, FullSeam operates in a domain where accuracy, compliance, and security are paramount. Finance data is sensitive, and errors can have serious consequences. Building trust will be essential.

Integration complexity may also present challenges. Enterprises often use customized systems, legacy software, and unique workflows that are difficult to standardize.

Regulatory considerations add another layer of complexity, particularly for companies operating across multiple jurisdictions.

Additionally, the startup must demonstrate that its AI agents can handle edge cases and unusual scenarios — situations where human judgment is traditionally required.

Success will depend on balancing autonomy with oversight, ensuring that organizations retain control while benefiting from automation.

Could FullSeam Redefine What It Means to Have a Teammate?

FullSeam’s vision extends beyond efficiency gains. By framing its product as a teammate rather than a tool, the company challenges conventional assumptions about collaboration between humans and machines.

In this future, teams may include both human and digital employees working side by side. AI agents could attend to routine responsibilities while humans concentrate on strategy, creativity, and decision-making.

The founders’ experience suggests that this model addresses a fundamental need within organizations: not just better information, but actual help in getting work done.

If FullSeam succeeds, it could mark a turning point in enterprise software — the moment when companies stopped buying tools and started hiring AI colleagues.

What Does FullSeam’s Journey Say About the Evolution of AI Startups?

FullSeam represents a new generation of startups focused on embedding AI directly into operational workflows rather than offering standalone products. Its emergence reflects broader trends in the technology landscape, where the emphasis is shifting from predictive insights to autonomous action.

The company’s Winter 2026 debut signals confidence in the readiness of AI systems to handle complex business processes. It also underscores the growing demand for solutions that address labor shortages and operational inefficiencies.

By combining deep domain expertise with an agent-based architecture, FullSeam aims to occupy a strategic position at the intersection of finance and artificial intelligence.

Whether it becomes a dominant force in enterprise operations remains to be seen. However, its central premise — that software should do work, not just display data — resonates strongly in an era defined by automation.

As organizations seek ways to operate faster and smarter, the idea of an AI employee may soon move from novelty to necessity.