Our new Meet the Insurtech Leader series gives you a look into the minds of the executives guiding these tech-forward companies.
Insurtech founders and leadership are encouraged to reach out for a potential feature in the series by
Who are you, and how did you come into your leadership position?
I am David Moscatelli, and I co-founded Go Abacus — originally launched as Abacus Analytics LLP — in 2018 after years of working in public accounting and financial services.
At an accounting firm, I observed auditors spending a majority of their time simply searching for things like contracts, working papers or old invoices. I saw that the real bottleneck in most offices wasn't a lack of expertise, but a lack of access to information. That insight became the foundation for Go Abacus. I built the company's earliest version using natural language processing, before large language models existed, so employees could ask a question and get an answer along with the source document.
Today, as CEO, I lead the Go Abacus vision and strategy by focusing on building secure, compliant, on-premise AI infrastructure for regulated industries that include banking, credit unions, healthcare and insurance.
What advice would you give to other insurtech leaders, or to yourself 5 years ago?
If I were talking to myself five years ago, or to any leader in this space, I'd say get your data and information into a place where it can actually be consumed.
Insurance, as a business, is fundamentally about measuring and pricing risk, and risk is a variable. It changes over time. To price products and assign a premium correctly, you have to understand all your data, which means being genuinely organized in how you collect and store it.
For twenty years, this industry has been promised a "master data lake" that never quite materialized. What's different now is that, for the first time, there's a tool that can actually take that information and use it to make real decisions inside an enterprise. The leaders who get ahead of that will be the ones who have their data house in order first.
How do you see AI changing insurance over the next 5 years?
It comes down to how we price and forecast risk. An actuary's job is to calculate the probability of an event and assign a premium based on that probability. AI lets us capitalize on pattern recognition in a much deeper way, which means far greater precision and variability in premium pricing.
The other shift is on the consumer side. Insurance is a confusing product for most people, whether it's a business or a family buying home or auto coverage, and AI is going to help demystify it by helping people actually understand what they've purchased. Think about health insurance. The biggest problem most people have isn't the product itself, it's that they don't know what's covered and what isn't, and that phone call to find out is painful.
Over the next five years, I think we'll see the industry price products more accurately, assess risk more precisely, and deliver a much more surgical, white-glove level of customer service.
What is the biggest opportunity for insurtechs over the next 6 months?
Consumers mostly buy insurance either out of fear or because they're mandated to do so. There are genuinely great products in this industry that don't get adopted, not because they're bad, but because they're hard to explain; why someone needs them, what they actually do and, once purchased, the experience with the product itself is often poor, because the product is complex. That's not really the insurance company's fault — it's genuinely hard to have an answer ready for every possible coverage question. The opportunity is for insurers to use this technology to be far more prescriptive about coverage and how it relates to people who are shopping for it and those who already have it.
Take auto insurance as an example. Most of these customers also have a home, an apartment, maybe a second home, a boat or an RV, and yet they only carry one policy with that provider. There's a real opportunity to expand the relationship simply by delivering excellent customer service.
What is the biggest mistake insurtechs make these days?
The biggest mistake I see is being too risk-averse.
It's almost funny to say that about an industry with an entire business model to measure and price risk, and I understand why caution is baked into that point of view and disposition. But this is not the time to be cautious about innovation or unwilling to explore new frontiers in technology. The tools available today can do more with an insurer's own data than anything this industry has had access to before. Treating that shift too conservatively is, in itself, a risk. My advice is to be more aggressive on the innovation front, not less.
What is the meaning behind the company's name?
The company product and platform is branded Abacus. The abacus, one of the earliest computing tools in human history, is used as a symbol for the company's philosophy: precision, repeatability and trust, with no black boxes or guesswork, applied to modern AI infrastructure for regulated industries.
What is your primary line of business?
It's on-premise AI infrastructure for regulated industries, including banking, credit unions, insurance, healthcare and professional services. This includes AI deployment, enterprise knowledge management and compliance-grade audit infrastructure.
What's the origin story of the company?
Lisa Gillespie and I founded Go Abacus, originally as Abacus Analytics LLP in 2018, applying early AI and machine learning to help regulated industries solve complex data and decision-making challenges.
The idea came from my own experience as a financial service advisor at a credit union, and later working in public accounting. I repeatedly saw employees unable to quickly find internal procedures and documents that already existed, but were buried in institutional knowledge — costing significant time and creating inconsistent customer service. In 2022, the company was rebranded as Go Abacus Corporation and refocused specifically on building secure, compliant AI infrastructure for regulated industries.
What pain points is the technology trying to solve?
Go Abacus was built to solve the problem of regulated institutions wanting to adopt AI without being able to safely send sensitive customer, patient or member data to third-party cloud AI tools.
It addresses the data sovereignty, compliance and auditability gaps left by general-purpose, cloud-default AI platforms that were not built with examiner-grade compliance in mind. It also solves an internal knowledge-access problem: employees at large, highly regulated institutions often cannot quickly find the correct procedure, policy or document, which leads to lost time and inconsistent answers for customers.
What funding rounds has the company had?
Go Abacus raised a $5 million Series A round in November 2025, led by GFT Ventures, with participation from BankTech Ventures and Jason Calacanis's LAUNCH Fund. This followed an earlier Series Seed round.
What's ahead?
Hardware and devices are going to be a much bigger story for us in 2026 than ever before.
We continue to expand our operating system, Go.OS, which has become genuinely popular with our client base. Within it, our Model Studio lets users create their own LLM, calibrated to how they want it to behave, rather than what a large model provider wants it to do. It's a simplified way to build and train a model automatically, brought down to something both enterprises and individuals can use.
The bigger arc here is the same one computing has always followed: technology that used to require an entire room-sized mainframe eventually became small enough to fit on your desk. We're doing that with our hardware, with our operating system and now with models themselves — giving people the same access to this technology, in their own home or business, that used to require a giant data center.
How many employees does your company have?
53
Where is the company based?
Chicago, IL
