Inside Soal Labs' Mission To Modernise Private Capital with AI

Much of the investment process for private equity or credit firms still runs on outdated tools and methods. Deal teams receive a constant flow of teasers and information decks from bankers, conduct multiple layers of due diligence, and coordinate with auditors, consultants, and lawyers — all while tracking progress across scattered spreadsheets, emails, and shared drives. And when it's time to present to the investment committee, teams wind up compiling everything into a single memo that can form the basis of a multimillion-dollar decision.

Each fund's process is unique, shaped by team size, deal volume, and technical software, yet few have systems that enforce the consistency needed to make those decisions reliable and swift.

Soal Labs, founded by Joe Zein and Osman Ghandour, set out to solve this by building custom data and AI systems for private capital firms. Their services introduce technology to take care of many essential parts of the investment lifecycle, from document ingestion and due diligence tracking to portfolio reporting and investor updates.

The result is a faster, cleaner, and more reliable decision-making process, helping funds replace operational drag with measurable speed and clarity.

The Minds Behind Soal Labs

Joe Zein and Osman Ghandour met while studying engineering at Georgia Tech, Joe in computer engineering and Osman in industrial engineering. Both were drawn to systems thinking: how complex processes could be simplified, optimised, and scaled.

After graduation, Joe joined Lazard, one of Wall Street's oldest investment banks, where he saw firsthand how financial analysis relied more on instinct and hierarchy than data. 'A lot of the analysis bankers were doing was arbitrary,' he recalls. 'With my engineering background, I saw an opportunity to introduce algorithms for some common analyses: spreading comps felt more like a recommendation problem rather than simple filtering. The same way streaming services recommend songs using algebraic approaches, I thought bankers could benefit from a more scientific approach, like clustering to find precedent transactions and comparable companies.'

Joe later moved into the startup world, leading data at Pawp, a telehealth and insurance company for pets. There, he built the company's data infrastructure and processes from the ground up to automate reporting workflows around finance, operations, marketing, and business.

Around the same time, Osman earned his master's in operations research at Stanford, focusing on understanding how different optimisation algorithms can shape how large organisations work.

When they reconnected years later, Joe and Osman realised their combined background in systems and data could be applied to the complex, outdated, and fragmented world of finance.

Addressing A Fragmented Investment System

Financial firms must continuously perform critical tasks like running due diligence before new investments, tracking performance across their portfolios, and analysing markets to decide where to deploy capital next.

Yet much of that machinery still runs on spreadsheets, emails, and manual supervision. Analysts can spend months reconciling documents that overlap, risk contradicting each other, or interpret the same data in slightly different ways. As Joe puts it, 'So much of the data at these companies is unstructured and lives in scattered systems and people's heads, with no structured way for information to move through a firm.'

The result is a growing risk of information loss. Deal teams rely on bandaid point-solutions that don't communicate with each other, making it difficult to track who owns which part of a process or to surface insights quickly when decisions need to be made. And that same lack of structure carries into portfolio reporting, where firms chase updates from portfolio companies and manually enter financials into shared models, slowing communication and leaving room for human error.

This, in turn, can come with major operational costs, as it can expose firms to regulatory penalties for weak governance and risk oversight, alongside the drag that comes from re-creating work. In fact, industry research shows that working with unreliable or inconsistent data can cost as much as 15% of a company's yearly revenue.

That's the gap Joe and Osman sought out to address with Soal Labs.

Redefining Financial Workflows Through Soal Labs

Soal Labs builds tailored systems that manage a company's investment lifecycle from end to end, covering everything from document ingestion to portfolio reporting, as well as building other operational systems for fundraising teams.

The tools they build use AI to extract deal materials and turn them into standardised formats, significantly shortening the time between an incoming memo and an investment decision. Smart validation layers catch reporting inconsistencies before they reach investors, while consolidated data feeds into internal chat tools that let teams retrieve internal information instantly.

They also build agentic systems that handle coordination across teams, managing follow-ups, reminders, and routine communication with third parties, reducing the back-and-forth that often slows large transactions.

Realising that no two firms operate the same way (some run lean deal teams, while others rely on layered approval chains and third-party auditors), the founders avoided the one-size-fits-all approach common in enterprise technology. Instead, Soal Labs builds systems that mirror each client's internal structure. This means tailoring everything from custom data connectors to legacy tools, to ETL pipelines hosted on different cloud providers, to reporting interfaces and various BI implementations, enabling firms to integrate this platform into their existing workflows rather than forcing new ones.

This service-first model, Joe argues, is what makes Soal Labs effective: giving clients an unbiased vendor-agnostic opinion that adapts to their inherited environment. 'The finance field doesn't need another SaaS vendor,' he says. 'It needs people with expertise — and tools that can empower them.'

Aligning Technology and Capital

Joe and Osman's ambitions for Soal Labs go beyond the boundaries of consultancy. As of right now, the firm remains intentionally self-funded, with every dollar earned being reinvested into talent and long-term partnerships, reinforcing a culture built on properly executing the best possible work for their customers.

'Our main goal,' Joe explains, 'is to stay lean, expert-led, and completely aligned with our clients' success.'

They're also working on that focus to take a more direct financial shape. Joe explains they plan to use Soal Labs' profit to co-invest alongside the funds it supports, linking the company's technical work to tangible investment outcomes. The approach turns collaboration into shared risk and shared reward: when the client succeeds, Soal Labs does too. 'The vision is to eventually spin up a fund of our own and invest alongside our clients,' Joe says. 'That aligns incentives even more.'

In a market where private capital touches everything from consumer brands to logistics networks, Joe and Osman see an opportunity to set a new standard for partnership: one that creates value and expands multiples.

A Measured Path Toward Modern Finance

Soal Labs reflects the mission statement that Joe Zein and Osman Ghandour set out to build: a company focused on turning complex financial systems into processes that are simple, fast, and dependable.

'Our job is to build what actually works, and to keep making it better,' Joe says — a principle that continues to guide Soal Labs as it continues aiming to improve the inner workings of financial engineering.