No-Code Studio
Hypatos develops deep-learning automation for enterprise document workflows.
Its no-code B2B platform enables large organizations to automate invoice processing (from data extraction to fraud detection), transforming manual financial operations into AI-driven decision pipelines.
Scope
- Product and design strategy definition
- System and workflow analysis
- UX research and behavioral insights
- Interaction and decision-flow design
- Prototyping and system modeling
- Visual and interface architecture
- Design system and documentation
- Handover-ready artifacts for engineering and product teams
My Role
- Led the end-to-end design of an AI-driven enterprise platform
- Translated complex automation logic into legible human workflows
- Defined design operations and long-term UX roadmap
- Designed interaction models for human–AI collaboration
- Evaluated design effectiveness through qualitative and quantitative signals
- Collaborated with product, engineering, data science, and business teams to align AI behavior with user cognition and organizational constraints
About Hypatos
Hypatos provides end-to-end deep-learning automation for organizations processing large volumes of documents. The platform enables no-code orchestration of document workflows, supporting hyper-automation across classification, extraction, validation, and enrichment processes.
The Hypatos Studio operates as an AI layer over enterprise operations, backed by data science and machine learning models that augment — rather than replace — human decision-making.
The Problem
Manual document processing is slow, costly, and structurally vulnerable to errors, fraud, and overpayments. Existing automation technologies often focus on data capture rather than decision accountability, leaving organizations exposed to hidden systemic risks.
The challenge was not only technical automation, but designing a system where AI decisions could be understood, questioned, and governed.
Objective
Design a no-code AI platform capable of automating complex document workflows (classification, splitting, capturing, validation & enrichment) while preserving meaningful human control over critical decisions.
The goal was to shift users from repetitive manual tasks toward supervisory roles, without obscuring uncertainty or responsibility behind automation.
Team Workflow
Competitive Analysis
Competitive analysis focused on identifying structural gaps in existing automation platforms – particularly in transparency, control, and interpretability.
Each design team member monitored specific competitors to uncover underserved opportunities and systemic weaknesses in prevailing AI product patterns.
Users and Challenges
The platform served users across operational, financial, and technical roles.
Key requirements were transparency, cognitive clarity, and accessibility for users without deep expertise in machine learning or accounting systems.
The core challenge was designing an interface that made complex AI processes legible without oversimplifying their implications.
Users and Document Flows
User needs, goals, and pain points were mapped across organizational roles and decision points.
Document workflows were decomposed into structured stages to identify bottlenecks, ambiguity zones, and moments requiring human intervention.
Decision points were intentionally embedded to reflect real organizational hierarchies and accountability structures.
Wireframes
Wireframes were developed as cognitive models of the system rather than purely visual layouts.
Early sketches were used to explore how users interpret AI outputs, navigate uncertainty, and transition between automated and manual actions.
Prototypes were iteratively tested to refine the balance between automation and human agency.
Moodboards
Visual direction was defined to reflect the dual nature of the platform:
- technical rigor
- operational clarity.
Moodboards served as conceptual frameworks for translating AI complexity into coherent visual language aligned with Hypatos’ brand identity.
Style Guide & Visual System
A unified style guide was developed to ensure consistency across the platform, design system, and AI training interfaces.
The visual system functioned not only as a branding tool but as a structural layer reinforcing clarity, hierarchy, and decision transparency.
Project Screens
Key dashboards and workflows were validated through user interviews and iterative testing.
Interfaces were designed to support rapid scanning, contextual understanding, and continuity of work across automated processes.
The UX emphasized traceability, enabling users to understand not only outcomes, but how those outcomes were produced.
Results & Outcomes
- Increased user trust in AI-generated outputs
- Production-ready web application supported by a custom design system
- Structured AI-focused UX framework adopted across teams
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Coherent visual identity aligned with system logic
KEY METRICS
Reduction processing time per doc
Automation rate across workflows
Decrease in manual workload
Related Case Studies
Streamlining financial and accounting tracking with Real-Time monitoring of Incoming Invoice Processes.