As monetary providers corporations transfer AI techniques from pilot initiatives into stay funding workflows, the standard of the information beneath these techniques has change into the defining variable, an indicator hallucinated output in a valuation mannequin or earnings evaluation can have fast, pricey downstream penalties. The problem is structural: most monetary knowledge accessible to AI instruments is web-sourced, inconsistently formatted, and never traceable to authentic filings, which implies AI-generated outputs inherit these errors at scale and change into troublesome to audit or act on. In the meantime, even on the most refined funding corporations, analysts nonetheless spend monumental parts of their time pulling figures from SEC filings, investor displays, and earnings releases by hand earlier than they will start any precise evaluation. Daloopa addresses this on the infrastructure degree, offering a structured, source-linked monetary dataset overlaying greater than 5,900 public firms globally, with every knowledge level hyperlinked to its authentic submitting, and delivering it by a number of codecs together with Excel, API, cloud integrations, and MCP connectors that plug instantly into AI platforms analysts already use. The corporate’s platform delivers as much as 10 occasions extra knowledge factors per firm than competing suppliers, cuts model-building ramp time by as much as 70%, and has demonstrated that grounding AI brokers in its auditable dataset improves retrieval accuracy by as a lot as 71 proportion factors in comparison with web-based retrieval. Trusted by greater than 160 of the world’s main hedge funds, mutual funds, and bulge-bracket banks in addition to Anthropic, OpenAI, and Perplexity, Daloopa has greater than doubled income over the previous yr as corporations speed up their push towards automated funding analysis.
AlleyWatch sat down with Daloopa CEO and Co-founder Thomas Li to be taught extra in regards to the enterprise, its future plans, current funding spherical that brings the corporate’s complete funding raised to $101.4M, and far, far more…
Who had been your buyers and the way a lot did you elevate?
We raised a $47M Sequence C led by Brighton Park Capital, with participation from Squarepoint Capital, Touring Capital, and Nexus Enterprise Companions.
The financing comes at a degree the place funding corporations are beginning to transfer AI into actual manufacturing workflows throughout analysis, valuation, and evaluation, which is growing the significance of getting dependable, auditable monetary knowledge infrastructure beneath these techniques.
Inform us in regards to the services or products that Daloopa presents.
Daloopa supplies structured, source-linked monetary knowledge infrastructure that powers monetary establishments’ analysis workflows.
Traditionally, a variety of monetary evaluation has trusted analysts manually pulling and getting into knowledge from sources together with firm filings, investor displays, and press releases, then validating the accuracy of every datapoint. That course of is time-consuming and turns into much more difficult as soon as AI techniques begin counting on that knowledge – or web-scraped inputs – at scale.
Daloopa’s platform covers greater than 5,500 public firms globally, with every datapoint linked again to its authentic supply for auditability. Funding corporations use the platform throughout workflows like valuation, modeling, analysis, and reporting, and more and more as a part of AI and agentic workflows as properly.
What impressed the beginning of Daloopa?
The unique inspiration for Daloopa actually got here from seeing how a lot of monetary evaluation nonetheless trusted extremely guide work. Even at among the most refined funding corporations on this planet, analysts had been spending large quantities of time pulling numbers from filings, cleansing knowledge, checking sources, and rebuilding the identical workflows again and again in Excel.
On the time, that was already inefficient for people, however we additionally realized it was going to change into a a lot larger downside as soon as AI techniques began getting utilized to monetary workflows. If the underlying knowledge is inconsistent or not traceable again to authentic supply paperwork, the outputs change into unreliable in a short time.
So the concept behind Daloopa was to construct a structured, source-linked monetary knowledge layer that funding corporations may truly belief and use in manufacturing workflows.
How is Daloopa totally different?
I feel one of many largest variations is that we constructed Daloopa round the place monetary workflows are going, particularly as AI turns into a part of how funding analysis and evaluation get finished.
Traditionally, the method was extremely guide, with analysts spending an enormous period of time gathering, getting into, and validating monetary knowledge earlier than they may even start the precise evaluation work. What’s altering now’s that AI can automate a variety of that work, however provided that the underlying knowledge is dependable and structured appropriately.
The opposite main distinction is the depth of the dataset itself. We go very deep on the firm degree, and clients are more and more utilizing Daloopa not simply inside conventional fashions and analysis workflows, however instantly inside platforms like ChatGPT, Claude, Perplexity, and Rogo.
What market does Daloopa goal and the way large is it?
We primarily serve monetary establishments, together with hedge funds, mutual funds, bulge-bracket banks, and more and more AI-native monetary platforms.
The broader market is monetary knowledge and monetary infrastructure, given the introduction of AI and agentic workflows at monetary establishments. As corporations begin operationalizing AI throughout analysis, modeling, valuation, and reporting, the demand for dependable underlying monetary knowledge will increase considerably.
What’s what you are promoting mannequin?
Daloopa supplies a SaaS subscription that features entry to its complete monetary dataset, an Excel-native modeling assistant, and its knowledge layer by way of MCP to drive AI workflow adoption. The corporate additionally presents programmatic entry by way of API and cloud-native supply by Snowflake, Databricks, and AWS S3.
Traditionally, most clients used Daloopa by extra conventional analysis and modeling workflows, however during the last couple of years we’ve additionally seen rising adoption by AI platforms and agentic workflows. That features clients utilizing our knowledge instantly inside instruments like ChatGPT, Claude, Perplexity, and Rogo.
How are you getting ready for a possible financial slowdown?
Our focus is on fixing an actual downside that’s turning into much more vital as AI adoption in monetary providers will increase.
If something, corporations are below extra stress to enhance effectivity and productiveness, and that tends to speed up curiosity in automation and AI-driven workflows slightly than gradual it down.
What was the funding course of like?
It was a really considerate course of. We spent a variety of time speaking with buyers who actually understood the nuances of knowledge companies and monetary infrastructure particularly, as a result of this can be a very totally different sort of firm than a typical utility software program enterprise.
What in the end stood out with Brighton Park was their depth of understanding across the significance of knowledge high quality, accuracy, and long-term infrastructure worth in monetary providers.
What are the largest challenges that you just confronted whereas elevating capital?
I feel one of many largest challenges is that knowledge companies are very nuanced companies. In our world, small variations in knowledge high quality and accuracy matter loads to clients, particularly as soon as AI is used inside actual funding workflows.
So a giant a part of the method was discovering buyers who actually understood the significance of the underlying knowledge layer and appreciated the complexity behind constructing it properly.
What are the milestones you propose to attain within the subsequent six months?
An enormous focus for us over the subsequent a number of months is continuous to develop the depth and protection of the dataset itself. We already cowl greater than 5,500 public firms globally, however we wish to go deeper throughout the businesses.
We’re additionally investing closely in AI and agent workflows on high of the information layer, notably round merchandise like Scout and different instruments that assist clients automate components of analysis, modeling, and evaluation workflows.
After which extra broadly, we’re persevering with to develop integrations and partnerships throughout the AI ecosystem as adoption accelerates throughout monetary providers.
What recommendation are you able to provide firms in New York that do not need a recent injection of capital within the financial institution?
I feel the largest factor is staying very centered on the client and fixing their largest challenges. The easiest way to seek out product-market match is to unravel an actual downside your clients care deeply about.
What’s your favourite spring vacation spot in and across the metropolis?
Going for a run in Central Park.
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