Automating Enterprise Workflows at Scale with Stack AI
Stack AI customers have processed over 5,000,000+ documents with Reducto
"When we discovered Reducto, we found it was one of the best in the market – the most flexible, easiest, and straightforward to have work with complex data." — Bernard Aceituno, Co-Founder of Stack AI
Bernard Aceituno believes automation should be within reach for every team—not just engineers or ML experts. For years, building AI workflows meant stitching together fragile scripts, retrofitted tools, and endless debugging hours. Stack AI set out to change that.
Their platform makes it easy for anyone inside an organization to build and deploy AI agents that automate everyday work—from parsing invoices to triaging legal contracts. For teams in finance, healthcare, legal, and defense, Stack AI helps orchestrate the unglamorous but mission-critical workflows that keep the business running.
But no AI agent can function without data. And in most enterprises, that data lives in PDFs, scans, emails, and unstructured documents—making document parsing a non-negotiable capability.
Below we highlight how Stack AI made Reducto their ingestion team of choice, show you an example of document parsing for a Data Room Agent, and discuss more about their future developments coming soon.
The Search for a Reliable Parsing Layer
From the start, Stack AI knew that their agents had to handle messy real-world inputs: receipts from vendors, handwritten claims, screenshots of internal dashboards. They evaluated several major OCR and document AI vendors but kept hitting the same wall: lack of flexibility, reliability issues, or tools too confusing to integrate with.
The top things they were looking for were:
- Performance
- Ease of implementation
- Speed and reliability
Bernard was introduced to Reducto after hearing about the success coming out of the YC batch, and immediately gave it a try. The performance cleared all their benchmarks, had an easy plug-and-go API, and was the most reliable option they’d seen.
"Other OCR providers had their pros and cons, but Reducto strikes a very good balance in which you have a system that is very reliable and of very high quality."
Today, Reducto serves as the foundational layer for these use cases—enabling high-fidelity document parsing, table extraction, and schema-based structuring that LLMs can reason over. Customers can dump hundreds of messy documents and build Knowledge Bases, Search workflows, and automated Chat bots to ask their data questions.
Building a Financial Data Room Agent
One example of a highly specialized AI agent use case involving Reducto is the Data Room Agent.
Data rooms allow financial parties to share confidential information such as financial statements, tax filings, and contracts during the M&A due diligence process. The data room serves as the backbone of information exchange—it accelerates timelines, reduces friction, and minimizes the risk of data leaks or compliance issues.
Stack AI uses Reducto to safely and accurately parse and extract data from any uploaded documents about target companies in the workflow, which can sometimes be incomplete, outdated, or poorly organized. Stack can also directly pull from sources like Google Drive, Notion, and Dropbox, which often contain hundreds of relevant data sources.
The document upload node in the workflow can then be used as clean data to be fed into an LLM of your choice, providing citations as well as structured outputs with exactly the information you need, such as:
- What are the trends in net income and profit after tax over the past five years?
- What is the current return on equity (RoE) and return on assets (RoA)?
- What is the current liquidity ratio and comparison to regulatory benchmarks?
- What is the current non-performing loan (NPL) ratio and its change over the years?
- What are the terms and conditions of recent investments or bond issuances?
The report generated can then help finance teams make faster decisions with more conviction.
Stack AI offers many templates you can customize to your use case depending on the documents uploaded.
The Ideal Collaboration
The collaboration between Stack AI and Reducto is deeply hands-on. Reducto’s team works closely with Stack’s engineers to fine-tune configurations, test new features, and ensure high performance in production.
"The Reducto team is always very hands-on whenever we need help, whenever they release something new, or whenever we want to learn about some new functionality or the best way to configure it."
Because Stack AI serves industries ranging from healthcare to finance to defense, Reducto’s processing layer needs to be fully industry agnostic. It should be able to handle any document type, format, or domain-specific language without sacrificing accuracy or security.
Looking Ahead: Agents That Learn and Adapt
The future for Stack AI goes well beyond today’s workflows. They’re building agents that evolve—systems that can learn from feedback, reroute based on context, and operate with fine-grained permissions inside high-security environments.
To get there, they need document understanding that’s not just accurate, but adaptable. That’s why Reducto remains a core part of their stack: it allows their agents to see, read, and reason through any document the real world throws at them.
In a world where everyone will be building AI workflows, the edge won’t just come from smarter agents—but from cleaner inputs. Reducto helps ensure that what goes in is as good as what you want coming out.
