SixThirty First Take: Rogo’s Recent Fundraise in the Context of AI for FIs
By Evan Thorpe, Principal at SixThirty Ventures
AI Analyst startup Rogo announced on 1 Oct, a $18.5M Series A at a reported $80M post-money valuation, just nine months following their $7M Seed round priced at a $48M post¹. What has changed and why the surge? Financial Institutions have been developing, partnering on, and deploying AI/ML capabilities for decades. The more recent advent of companies termed “AI Analysts” born of the Gen AI furor has jostled both the Street and the FinTech landscape. Let us examine the broader market and context. Rogo’s two rounds are among notable funds raised (see figure below) by companies SixThirty is tracking at the intersection of AI and FI’s. Note, this includes so-called analytical AI and the use of AI for classification and analysis of data, as well as generative AI for the creation of content, code, and other data:
What we are observing:
A year ago, many of these companies had minimal revenue traction and, from our point of view, could not justify the higher valuations placed on them by the market. However, in 2024, several, -including Rogo- have reportedly increased their traction towards meaningful levels for recurring revenue. Though the uptake is occurring, broadly, rounds are still pricing today at outlier-level revenue multiple premia. The prices suggest outliers but there are many players, so what constitutes is a competitive moat or evidence of an out-performance edge in this space?
Against this market landscape, early-stage investors have started to place multiple bets in this space. The most active VC investors at this intersection include Y Combinator, AlleyCorp, Khosla Ventures, A16Z, BoxGroup, Foundation Capital, and Thomson Reuters Ventures.[1] It appears some are so bullish on the companies in this space that they see reason to invest in more than one; but on what is the ebullience based?
This fundraising has prompted FIs to take notice and fueled their curiosity to trial and, indeed, pay for subscriptions from these startups. FI’s presently are conducting structured evaluations of these platforms to explore how knowledge workers in their firms can derive value beyond that which is furnished by ‘generic’ GenAI assistants such as Microsoft Copilot or OpenAI’s ChatGPT. In molding themselves to vertical-specific workflows germane to financial services, these so-called AI Analysts have several substitution monikers: AI teammate, AI assistants, AI copilots, sometimes elevated to AI Associates.
Tasks, including and especially those that required both intellectual effort as well as tediously applied elbow grease that previously took hours or days, now take only minutes. Desktop research can be turned around swiftly, and pitchbook drafts can be assembled at a moments’ notice without the same expenditure in effort (or human intellectual evaluation). Functionality and use-cases vary but the USP promised by the AI Analyst for financial institutions are benefits to efficiency afforded the automation of manual workflows including:
- Comprehension & contextualization of research — codifying the systematic elements of investment analysis and the development of research content;
- Collation of data whether company financials or portfolio holdings and performance reporting;
- Automation that applies content analysis to prescribed output (models, presentations, industry summaries) which connect to cross-functional workflows within the institution;
- Analysis of competitive landscapes or commercial drivers of vertical-specific business models and monetization strategies
The result? For many of the sell side-facing applications, this means the automated generation of pitch books, financial models, and sector analysis. For many of the buy side-facing use-cases, AI Analysts offer a means for junior-level private equity and venture capital professionals to compile the necessary overviews necessary for leaders in their firms to understand an unfamiliar or emergent space and get up to speed on it at the pace of a fast-moving fundraise, and then populate the requisite data and analysis needed to comprise investment memos.
Questions we are asking:
1. HA vs RA: What is the impact on the development of the next generation of research and analytical talent? Does leveling the playing field for intellectual rigor on the robot analyst (RA) side blunt the edge that a given firm can have over another based on the quality of their human analyst (HA)? What is the right mix of HA vs RA? Do these AI Analysts help firms identify that balance through POC-and-error?
2. What does financial institution uptake of AI Analysts suggest about the direction of AI technology, overall? Do financial institutions and their deal-driven pace and highly regulated context provide a special or valuable testing ground for Enterprises AI and its application to unlocking efficiency? Or does offloading too great a portion of analysis & synthesis diminish the value of financial services knowledge workers? Will knowledge workers have their knowledge work stripped away?
3. Is there competitive differentiation in business models or networked uptake? Does a market standard AI Analyst get established? If many companies are solving the same problem and addressing it by more or less the same way, how will these startups differentiate 1) their technology; 2) their business/company from one another? Customers’ build vs buy equation is often weighed and evaluated on the basis of value derived from internal edge (build) vs value derived by proven external market norms or a network (buy). And so, it follows that if there is no network or standard established, is there even a case to be made for going with ‘buy?’
4. Is there economic competitive differentiation? Does the aggregation of data and the mutualization of compute form a basis for economies of scale? The acquisition and tabulation of data alongside the enhancement of compute might imply that fixed –or perhaps even variable- costs may yet be contained. If this is the case, is there a scale economy achievable in the business model?
5. Why now? Analysts have noted that software with AI capabilities and Gen AI tools were met with skepticism in 2023 but have seen greater institutional uptake in 2024, what has changed? “Can’t Perplexity do this?” meets “Can’t Pitchbook do this?” is an early objection, and should GPT5 from OpenAI raise the bar significantly, this question may yet resound the louder.
6. Where might these unlock something big? If the notion of the AI Analyst is to synthesize a multitude of tables, texts, images, and audio, to whom in the corporation does the value ultimately and truly accrue? Digging a layer deeper than the what, if the how of scrubbing and iterating analysis can result in better decisions in the firm, while efficiency is also improved, then there is a case to be made for an opportunity to establish new norms and expectations. Should this value be unlocked, will these platforms and the workflows they support become features that firms cannot move into the future without?
¹ Source: Pitchbook Figures, Oct 2024
