At Translytics, we're not just using AI — we're rebuilding our engineering DNA around it. Becoming an AI-native product company means embedding artificial intelligence into every layer of our tech stack — from frontend to backend, from code generation to deployment — to create faster, smarter, and more resilient supply chain solutions.
This shift isn't about buzzwords or chasing trends. It's a deliberate transformation in how we build, deploy, and scale products in an increasingly complex and dynamic world.
Over the past few months, our engineering teams have made significant strides in integrating AI agents into core development workflows — with measurable results.
In our ReactJS development, we've reduced UI build time by over 50% by leveraging generative AI agents for:
Component scaffolding
UI logic automation
Accessibility enhancements
Rapid prototyping
In the backend, intelligent code assistants integrated into our IDEs have helped cut API development time by nearly 30%, assisting in:
Boilerplate code generation
Input validation patterns
Test coverage scaffolding
Code reviews and refactoring suggestions
But this shift isn't just about productivity. It's about redefining how software is created, and empowering engineers to do more with less friction.
We view AI not as a replacement for human intelligence but as a collaborative partner.
Our engineers are actively using AI for:
Code generation and completion
Technical documentation drafting
Test automation and coverage analysis
CI/CD and deployment workflow automation
We're simultaneously investing in:
Internal training programs
Upskilling tracks for AI-assisted development
Change management practices to align teams
This ensures that our engineers feel enabled, not threatened — empowered to lead with creativity and insight while offloading repetitive tasks to their AI counterparts.
While the gains are real, we're clear-eyed about the limitations.
AI agents excel in narrow, repetitive tasks, but still struggle with:
Deep domain logic
Cross-service dependencies
Long-term architectural coherence
Debugging in high-scale environments
As our codebase grows, so do the challenges around agent governance, debugging complexity, and infrastructure costs.
To address this, we're building domain-specific AI agents tailored to our supply chain product context — especially in areas like:
Testing edge-case business logic
Security and compliance scanning
DevSecOps automation
We maintain full adherence to SOC 2 and ISO 27001 standards, ensuring our AI-embedded processes meet enterprise-grade security and governance requirements.
One of our most exciting internal discussions right now is this:
Should we invest in Feature Agents (focused on tasks like testing, documentation, refactoring)?
Or Application Agents (trained on our unique product logic and workflows)?
This debate reflects a deeper cultural shift — from AI as a helper to AI as a thinking partner that understands context, adapts to our ecosystem, and helps us ship better, smarter products faster.
It's not just a tooling change. It's a mindset shift — toward an AI-first product strategy for the modern supply chain.
At Translytics, we're not chasing AI trends. We're defining what it means to be AI-native in a real-world, enterprise-grade environment.
By embedding AI deeply into engineering, we're:
Speeding up development
Enhancing quality and reliability
Freeing human talent to focus on creativity and critical thinking
Building a platform that's resilient, scalable, and future-ready
We're not just building supply chain software.
We're building the future of how software is built.