Irish start-up aims to offer pay-as-you-go finance research

DCM Editorial Summary: This story has been independently rewritten and summarised for DCM readers to highlight key developments relevant to the region. Original reporting by Irish Times, click this post to read the original article.

Analysts, investors, researchers and financial professionals all have one thing in common: they need constant access to top-quality data from reliable sources.

Big organisations have both the budget to buy multiple data subscriptions and the computing power required to crunch the information.

Smaller operators generally don’t, and it was this dual problem of access and processing capacity that Swastik Sahu set out to address when he began developing Trading Studio in October 2024.

“What really drove me to build Trading Studio was my own frustration as an active investor and my experience of trying to develop algorithmic investment strategies,” Sahu says.

“What frustrated me most was spending weeks building a system that only allowed for the testing and refinement of a single strategy. I kept thinking: researchers shouldn’t have to spend time setting up infrastructure, they should focus on actual research.”

“It’s like being forced to buy a car when you only need an occasional taxi,” Sahu says.

Trading Studio offers its customers on-demand access to institutional-grade financial data and computing power, but without the expensive subscriptions.

“At the moment, if someone wants to research financial instruments properly, they need expensive tools like a Bloomberg terminal (around €32,000 per year) or separate data feeds (around €6,000 per year each) as well as cloud infrastructure (around €2,000 per year) and the technical skills to put it all together.

We are the only platform that combines three critical elements: curated financial data, configurable compute resources and a powerful SQL engine (structured query language – used for interacting with databases) in one integrated system. Every other platform in the space locks you into fixed pricing tiers.

[ Irish start-up’s wearable device detects life-threatening allergies in childrenOpens in new window ]

“A quick analysis with us costs pennies,” Sahu adds. “A complex back test across say 20 years of data scales up resources for that job only. So, someone only pays for the compute power they actually use. Currently, people are paying for multiple expensive tools or locked out entirely by pricing.

“We also have integrated AI that speaks plain English so instead of learning specific languages such as SQL or Python, someone can ask the system to ‘show me Irish companies growing faster than 10 per cent’ and get instant results. But it’s also possible to write SQL directly against our data warehouse for maximum control. The AI, the SQL engine, the data, and the compute are all seamlessly integrated, which no competitor offers,” Sahu says.

Sahu is no stranger to dealing with huge amounts of data and computing power, as before he set up Trading Studio, he worked for Amazon Web Services (AWS), where he built an enterprise network analytics platform capable of processing terabyte-scale data streams.

“This experience of building systems that handle massive data volumes directly translates to the computational challenges involved in financial data analysis. Before AWS, I worked in software engineering, including early roles at start-ups, where I learnt to adapt quickly and wear multiple hats,” Sahu says.

Trading Studio was started on a tiny cash budget of around €8,000 and hundreds of hours of founder time spent on building the system. Sahu is currently on the New Frontiers programme at ATU Sligo and is aiming to raise around €200,000 in funding by the end of Q2 to start building his team.

The company already has paying customers, and ideally, Sahu would like an investor, or possibly someone to join him as a co-founder, who would bring experience as well as money to the table.

[ How an Irish AI start-up plans to fix manufacturing’s biggest bottleneckOpens in new window ]

The platform’s data is drawn from multiple sources, including Federal Reserve Economic Data, Financial Modeling Prep, Eurostat and the World Bank.

Potential users (independent investors, analysts at smaller funds, quantitative researchers and financial advisers managing high net worth portfolios) can explore the platform for free. Monthly subscriptions for increased access and features start at $15 (€13) a month. He says the platform is not selling products or advice, so it does not require regulation.

An early Beta version of Trading Studio has been live since June of last year, and the platform was launched last month (January 2026) to a small group of customers who are providing user experience feedback.

“It’s one thing to build features you think users need and another to watch them actually use the platform and discover what truly matters,” Sahu says.

Sahu adds that building the platform has been the relatively “easy” part of the start-up as it taps into his skills and experience. For him, the steeper learning curve has been on the commercial side of things as he’s had to build his business acumen and learn how to run a company.

“I found the structured approach to validating business models and the access to experienced mentors invaluable on New Frontiers,” he says.

[ Why Cork’s newest protein brand is skipping the gym-bro aestheticOpens in new window ]

“The support system does a good job of helping founders think through their business fundamentally rather than just building technology. If anything, more direct connections to early-stage customers in specific industries would be helpful. Sometimes the hardest part isn’t building the product but finding the first ten customers willing to provide honest feedback.

“I have started with the finance domain because I experienced the problems involved at first hand, but the underlying issue is domain-agnostic,” adds Sahu, who holds a Master’s in data science from Trinity College Dublin.

“Whether you’re analysing financial markets, genomics data, or supply chain patterns, you need the same basic infrastructure, only the data sets change. So, while we’re targeting the financial community to begin with, we will be moving into other sectors and applying the platform to any domain requiring quantitative research. The need for accessible data analysis infrastructure is universal.”