Relogic designs and engineers digital systems where data, AI and human decision-making have to work together. From clinical intelligence and analytics to responsible-AI governance, each product is designed around a real workflow.
A clinical data intelligence system combining bilingual voice recognition, OCR-assisted ingestion and structured research-ready information. ClinTx turns fragmented clinical inputs into a workflow that researchers and teams can actually inspect and use.
Discuss ClinTx →A research and development project focused on exploring ideas, testing approaches and developing practical solutions.
Discuss EdgesMatrix →A gesture-based screen controlling system that lets users interact with their screen through hand gestures.
Discuss Velos →An evidence-backed command center for founder-led businesses. Projects, clients, cash, operations and business signals sit in one place so a decision can be traced to the record behind it.
Try FounderCMD →A logistics tracking and end-to-end support system covering intake, live movement, exceptions, handoffs and proof of completion — so operations teams work from one support surface.
Discuss Neervan →Relogic works with teams to define, design and engineer custom digital products around difficult workflows. Engagements can span product strategy, UX systems, full-stack implementation, data infrastructure and AI-powered experiences.
Start a Product Engagement →The strongest digital products connect the interface, data and technical architecture from the beginning. Relogic can work across the complete product lifecycle or enter at the layer where your team needs additional capability.
Problem framing, user workflows, information architecture and product direction before implementation.
Responsive UX/UI systems and interaction patterns designed around complex information and real tasks.
Full-stack implementation connecting front-end experiences with reliable backend and production infrastructure.
RAG, generative AI, ML pipelines and intelligent product features with evaluation and human review.
Structured data flows, analytics infrastructure, quality checks and decision-ready information layers.
Iteration, validation and improvement after launch based on user behavior, performance and system needs.
Understand the user, decision, workflow and technical constraints.
Translate the workflow into information architecture, interface and system logic.
Connect interface, data, AI and backend services into a production-ready system.
Review product behavior, edge cases and real-world use before continuing to iterate.