Most business workflows - approvals, onboarding, document review - follow a set of rules that are individually simple but collectively expensive to run by hand: someone has to read a document, decide where it goes next, and remember to follow up. We built a workflow automation platform that automates not just the handoff between steps, but the decisions inside those steps.
Technology Stack
The Challenge
Traditional workflow tools automate the routing between steps but still expect a human to read every document, extract the relevant fields, and decide which branch a case should follow. For document-heavy processes - approvals, claims, applications - that manual reading and deciding is the actual bottleneck, not the handoff between people.
What We Built
- Visual workflow builder: a no-code canvas for defining multi-step business processes, branches, and approval chains without writing code
- AI document intake: OCR and NLP extract structured data directly from uploaded documents - forms, invoices, applications - removing manual data entry from the first step of every workflow
- Intelligent routing: a model reads the extracted data and case history to route each item to the correct next step or approver automatically, instead of relying on a fixed rule tree
- Predictive next-step suggestions: for cases that don't clearly match an existing rule, the system suggests the most likely next action based on how similar past cases were resolved
- Exception flagging: automatically surfaces cases that look unusual compared to historical patterns, so reviewers focus their attention on genuine edge cases instead of routine ones
How It Works
A document or request enters the workflow, and AI-driven OCR and NLP extract the fields needed to evaluate it - who submitted it, what it's requesting, and any supporting details - without a human re-typing anything. A routing model, trained on how similar cases were historically resolved, decides the next step: auto-approve, route to a specific reviewer, or flag as an exception. Business users still define the overall workflow structure visually; the AI layer removes the manual reading-and-deciding work inside each step.
The Outcome
Processes that previously required someone to open every document and manually decide where it goes now route themselves for the large majority of routine cases, with human attention reserved for genuine exceptions the system flags. Teams running the platform get a workflow builder they can adjust themselves, backed by an AI layer that keeps getting more accurate as it sees more resolved cases.