American businesses spend over $300B annually on data entry and business process outsourcing, yet 90% of insurance workflows and credit decisions still rely on people manually reviewing PDFs and documents. This manual approach creates bottlenecks that slow critical business decisions, from underwriting loans to processing insurance claims, while teams struggle with repetitive tasks that pull them away from high-value work. Heron Data is addressing this challenge with an AI-powered workflow platform that reads and understands business documents like bank statements, insurance policies, and loss runs, converting them into structured, decision-ready data with human-level accuracy. The platform integrates seamlessly into existing underwriting, credit, and operations workflows, automating manual work while keeping teams in control of their processes. With over 150 customers including FDIC-insured banks and insurance carriers, Heron processes more than 350,000 documents weekly, helping companies like SMB lenders cut submission-to-decision time by 60% and insurers automate over 80% of inbound submission triage.
AlleyWatch sat down with Heron Data CEO and Cofounder Johannes Jaeckle to learn more about the business, its future plans, recent funding round, and much much more…
Who were your investors and how much did you raise?
We raised a $16.6M Series A led by Insight Partners, with participation from Y Combinator, BoxGroup, and Flex Capital.Tell us about the product or service that Heron Data offers.
Heron is a workflow platform that uses AI to read and understand business documents – like bank statements, insurance policies, and loss runs – and convert them into structured, decision-ready data.
Our product suite supports configurable workflows that combine high-accuracy document parsing with enrichers, validations, and decision logic — all tailored to match how real teams operate.
We’re built to integrate seamlessly into underwriting, credit, and operations workflows – helping customers automate the manual work while staying in control of their process.
What inspired the start of Heron Data?
In 2020, we started building with LLMs, which were niche at the time – all of “modern AI” that is now so well-known started in 2017. We realised quickly how good the technology was at solving problems. By the time ChatGPT captured the public’s attention in November 2022, we had already spent 2 years building LLM-based products for financial services companies.
This helped us very early on to understand the power of LLMs, and so we got obsessed with bringing the power of AI to real businesses across America.
Heron’s mission today is to give white-collar teams their time back – to “save a lifetime of boring, repetitive work every day,” as we say internally. We believe AI’s greatest impact won’t come from replacing humans, but from liberating them. By automating the grunt work – inbox triage, document extraction, eligibility checks, form filling – Heron lets teams refocus on judgment-based tasks and high-stakes decisions.
How is Heron Data different?
Most automation tools just extract text – we go deeper. Heron reads documents with human-level accuracy and returns structured, trusted data that’s immediately usable for downstream systems and underwriting models. But what truly sets us apart is how we integrate into our customers’ existing workflows.
We don’t ask customers to rip out their infrastructure or adopt a new system — instead, we slot into real industries and real processes, like underwriting or SMB lending ops, and augment what already works. Our product is designed to adapt to each customer’s workflow, not the other way around. That’s how we earn trust – by delivering precision and speed where it matters, without disruption.
What market does Heron Data target and how big is it?
Heron targets the $300B a year market for data entry and business process outsourcing that powers industries like insurance, lending, equipment finance, and compliance. These sectors depend on reading and understanding unstructured documents – PDFs like bank statements, loss runs, insurance policies, and contracts – to drive critical business decisions.
Our entry point has been SMB lending and insurance, where we’ve already demonstrated strong ROI by automating workflows previously handled manually. But the broader opportunity is massive:
Over $6T in credit decisions each year are influenced by document-based underwriting.
90% of insurance workflows still rely on people manually reviewing PDFs.
With AI reaching enterprise maturity, there’s growing demand to modernize these processes – we’re building the AI infrastructure layer to meet that demand
What’s your business model?
We use a usage-based SaaS model. Customers pay per document processed, with pricing that scales based on volume and service level.
How are you preparing for a potential economic slowdown?
We’re staying lean and focused on workflows that deliver clear, measurable ROI – like reducing manual processing time or accelerating revenue-generating decisions. In uncertain markets, companies double down on efficiency, not experimentation.
That’s why we prioritize essential, non-optional workflows in industries like insurance and credit, where documents still need to be processed regardless of the macro environment. Our product becomes even more valuable when teams are stretched and headcount is constrained.
What was the funding process like?
It was fast but intentional – the raise took 1 month, and then 1 month of due diligence. We had strong conviction in our traction and customer pull, so we ran a focused process with a short list of investors who understood both the technical depth and market potential of our product.
Rather than casting a wide net, we prioritized partners who shared our long-term vision and had experience scaling infrastructure companies in regulated industries.
What are the biggest challenges that you faced while raising capital?
The biggest challenge was communicating the complexity of our product in a simple, compelling way. We’re not just doing OCR – we’re building AI that understands documents the way a human would, and that nuance can get lost without hands-on demos or customer context.
We also had to bridge the gap between technical excellence and business value – helping investors see how our accuracy, reliability, and workflow integration translate into real cost savings and faster decisions for customers.
What factors about your business led your investors to write the check?
Strong customer pull and evidence of valueInvestors spoke directly with our customers and consistently heard the same thing: Heron is mission-critical. Customers described us as “a game-changer” for their workflows – saving hours of manual work, increasing throughput, and enabling faster decisions. Several had expanded usage within months of onboarding, and many cited Heron as a key part of their operational stack. That level of conviction — not just adoption, but advocacy — stood out.
A massive, underserved marketOur focus is on industries like insurance and credit where document-based decisions are the norm, not the exception. These are trillion-dollar markets where the workflows still run on PDFs and email, and few vendors are solving the problem with real technical depth. Heron’s ability to plug into legacy systems and return structured, reliable data makes us uniquely positioned to win in this space.
What are the milestones you plan to achieve in the next six months?
Deepen our presence in core verticalsWe’re focused on expanding within insurance and SMB credit — going deeper into customer workflows, adding support for adjacent document types, and increasing usage within our existing accounts. These are industries where we already have strong traction, and there’s still a lot of room to grow.
Improve model accuracy, reliability, and feedback loopsWe’re continuing to push the limits of what high-accuracy AI can do in production. That includes better handling of edge cases, reducing the need for human review, and building tighter feedback loops so the system improves with every document. Trust is critical in our space, and we’re investing heavily to make Heron’s outputs more reliable and transparent over time.
What advice can you offer companies in New York that do not have a fresh injection of capital in the bank?
This is one of the best times in history to build a software company if you’re leveraging AI effectively. With today’s tools, a small team can ship enterprise-grade software, iterate fast, and deliver real value without needing massive capital or headcount. We were able to get to millions of ARR with a relatively small team because the margins of an AI-first product are amazing.
Focus on solving painful, recurring problems. Stay close to users. Build things people will pay for today – not just in theory.
Where do you see the company going now over the near term?
We’re doubling down on what’s working — going deeper in insurance and credit, expanding document coverage, and investing heavily in accuracy, reliability, and feedback loops.
We’re also making it easier for new customers to adopt Heron with faster onboarding and better tooling. The goal is to become the trusted infrastructure layer for document-heavy decisions – starting with a few key verticals, then expanding outward from there.
What’s your favorite summer destination in and around the city?
Swimming in the Rockaways and getting food at Tacoway Beach!
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