Marloo raises $10M from Blackbird Ventures for an AI platform for financial advisers

Marloo has raised $10M in a seed round led by Blackbird Ventures. The startup wants to go beyond the AI note-taker and become a full operating system for financial advisers.

Where the money will go

With the new round, Marloo's total funding has reached $12.7M in less than a year. Blackbird also took part in the earlier pre-seed round and has now raised its stake: after two rounds, the fund owns roughly 34% of the company. Icehouse Ventures also participated in the round.

Founded in 2024, the team plans to strengthen its position in the UK and Australia, launch in the US, and expand its product line around advisers' day-to-day work.

"It reminds us of the early days of

Canva," is how Blackbird described Marloo's trajectory.

Not just notes

Marloo does not pitch itself as a meeting transcription service, but as an AI partner for financial advice practices. The logic is clear: this is one of the most heavily regulated and paperwork-intensive functions in financial services, so advisers spend too much time on notes, compliance documents, correspondence and reports instead of working with clients. Before launching the product, the founders say they spoke with about 800 potential customers to understand where the market hurts most.

• Takes notes during meetings and calls
• Generates advisory documents and follow-up emails after meetings
• Runs compliance processes and maintains internal notes
• Fills in forms and prepares follow-up tasks
• Pulls emails, documents and notes into a single client profile

On its website, Marloo stresses that the system remembers the full context of the client relationship: past meetings, emails, attachments, tasks and company templates. That is an important shift from the typical AI note-taker, which usually stops at a transcript and a short summary. Here the idea is broader: build a single layer of memory and action around the adviser, so every new task builds on data already collected instead of starting from a blank slate.

Metrics and safeguards

According to the company, Marloo already has more than 650 paying financial advice firms in six countries, and revenue has grown by more than 40% month over month for 11 months in a row. The company describes churn as almost zero, which for B2B SaaS is a stronger product-market fit signal than fast growth alone. In customer case studies, Marloo claims to save 7–10 hours a week, and documents that used to take up to eight hours are now put together in under four.

For this category, speed and convenience are not the only things that matter; control over data does too. Marloo specifically highlights SOC 2 Type 2 and GDPR compliance, the ability to delete recordings and transcripts, and work within a single client timeline. For the financial advice market this is not a marketing detail but a baseline requirement: if a system fails security and compliance checks, it simply will not be allowed into the daily workflow.

The main test

The key question for Marloo is why a vertical product will not be crushed by general-purpose models such as Claude, ChatGPT or Gemini. Co-founder Shakeel Lala answers through context: a general model can recap a meeting, but it has a harder time combining legal requirements, a specific firm's rules and an adviser's personal preferences in a single answer. In financial advice, a mistake is costly, so the value shifts from a "smart model" to a system that can work within tightly defined limits.

The next stage is the US. It is the largest market but also the hardest: more fragmented, governed by a mix of federal and state rules, and requiring local adaptation. For Marloo this is not just selling in a new geography but a test of the whole hypothesis: can the compliance know-how built up in the UK and Australia be moved quickly into an environment where the rules, players and documentation standards are noticeably more complex?

What this means

Marloo is another example of AI money flowing not only into foundation models but into industry-specific products built on top of them. If the company can carry its compliance-focused approach into the US, the market will get a strong case that vertical AI can beat general-purpose assistants where regulation, context and trust matter.