Eloquent AI

The AI Operator for Financial Services

[Duke Capital Partners @DukeCapPartners]

🚀 Excited to welcome @EloqAI to the DCP portfolio. The company raised a $7.4M seed round (in 3 days, 12x oversubscribed) to power AI-driven customer service for financial services. 💥$500K ARR in 4 weeks • 10 clients • 150+ waitlist. Vertical AI built for compliance.

[Duke Capital Partners @DukeCapPartners]

Congrats to portfolio company @EloqAI on ranking number 4 in @harmonic_ai Hot 25

In the past month they closed a seed round in 3 days, onboarded 7 enterprise customers in 4 weeks, and are reshaping financial services with AI.

🔗 https://t.co/yQ7zEvm51B

#AI #Startups #FinTech

[Foundation Capital @FoundationCap]

Most financial institutions still run on people pretending to be software.

Behind the polished apps and established systems is a hidden workforce doing what looks like automation, but isn’t. It’s a single human from compliance, manually pulling data from System A, cross-referencing it with System B, making a judgment call, then typing the results into System C.

A portco founder calls this the human data transfer layer. And now, it’s finally being replaced.

AI agents like those built by @EloqAI, Efficio, and @exacare_ learn by watching existing human workflows, then replicate those exact processes automatically without requiring fintechs to rip out their legacy systems or integrate new APIs. A bank can deploy meaningful AI capabilities like fraud detection, risk assessment, or customer service automation in days rather than years.

This is the start of a new kind of fintech middleware: agent-powered, outcome-based, and legacy-compatible.

And it doesn’t stop at fintech. Healthcare, logistics, manufacturing, legal services - every industry has human data transfer layers that agents can replace.

If we could replace this human data transfer layer with actual automation – replacing humans pretending to be software with actual software – we’d be fundamentally rewiring how financial services work.

More from @nicostainfeld and @znoorani on why we’re betting on AI agents as human APIs: https://t.co/DpHAGbB1of

[Daniel Worku @dbworku]

@alexandr_wang @scale_AI Our X25 batch is CRACKED. Watch out world. @EloqAI @cascade_space @lucis_life @ValueMateAI

[Y Combinator @ycombinator]

Eloquent AI (@EloqAI) automates complex, regulated customer operations for financial services.

No dev cycles, no APIs required. Their AI Operator learns directly by observing your SOPs and team’s actions, securely navigating your existing systems.

https://t.co/pGJGi9m3z1

Congrats on the launch, @TugceBulut and @AldoLipani!

[Eloquent AI @EloqAI]

We’re on a mission to 10x productivity for all customer-facing pros with Eloquent AI. Work smarter, close faster, win bigger ✨

[Andrew Ng @AndrewYNg]

A “10x engineer” — a widely accepted concept in tech — purportedly has 10 times the impact of the average engineer. But we don’t seem to talk about 10x marketers, 10x recruiters, or 10x financial analysts. As more jobs become AI enabled, I think this will change, and there will be a lot more “10x professionals.”

There aren’t already more 10x professionals because, in many roles, the gap between the best and the average worker has a ceiling. No matter how athletic a supermarket checkout clerk is, they’re not likely to scan groceries so fast that customers get out of the store 10x faster. Similarly, even the best doctor is unlikely to make patients heal 10x faster than an average one (but to a sick patient, even a small difference is worth a lot). In many jobs, the laws of physics place a limit on what any human or AI can do (unless we completely reimagine that job).

But for many jobs that primarily involve applying knowledge or processing information, AI will be transformative. In a few roles, I’m starting to see tech-savvy individuals coordinate a suite of technology tools to do things differently and start to have, if not yet 10x impact, then easily 2x impact. I expect this gap to grow.

10x engineers don’t write code 10 times faster. Instead, they make technical architecture decisions that result in dramatically better downstream impact, they spot problems and prioritize tasks more effectively, and instead of rewriting 10,000 lines of code (or labeling 10,000 training examples) they might figure out how to write just 100 lines (or collect 100 examples) to get the job done.

I think 10x marketers, recruiters, and analysts will, similarly, do things differently. For example, perhaps traditional marketers repeatedly write social media posts. 10x marketers might use AI to help write, but the transformation will go deeper than that. If they are deeply sophisticated in how to apply AI — ideally able to write code themselves to test ideas, automate tasks, or analyze data — they might end up running a lot more experiments, get better insights about what customers want, and generate much more precise or personalized messages than a traditional marketer, and thereby end up making 10x impact.

Similarly, 10x recruiters won’t just use generative AI to help write emails to candidates or summarize interviews. (This level of use of prompting-based AI will soon become table stakes for many knowledge roles.) They might coordinate a suite of AI tools to efficiently identify and carry out research on a large set of candidates, enabling them to have dramatically greater impact than the average recruiter. And 10x analysts won’t just use generative AI to edit their reports. They might write code to orchestrate a suite of AI agents to do deep research into the products, markets, and companies, and thereby derive far more valuable conclusions than someone who does research the traditional way.

A 2023 Harvard/BCG study estimated that, provided with GPT-4, consultants could complete 12% more tasks, and completed tasks 25% more quickly. This was just the average, using 2023 technology. The maximum advantage to be gained by using AI in a sophisticated way will be much bigger, and will only grow as technology improves.

Here in Silicon Valley, I see more and more AI-native teams reinvent workflows and do things very differently. In software engineering, we've venerated the best engineers because they can have a really massive impact. This has motivated many generations of engineers to keep learning and working hard, because doing those things increases the odds of doing high-impact work. As AI becomes more helpful in many more job roles, I believe we will open up similar paths to a lot more people becoming a “10x professional.”

[Original text: https://t.co/svQYHp3XVW ]

[Eloquent AI @EloqAI]

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