Engineering
Intelligence that Works.

Challenge

A retail fashion brand needed to track emerging trends across 100+ online sources, but traditional web scrapers broke constantly due to website layout changes. Manual monitoring was too slow to catch trends early, and maintaining scrapers required constant developer intervention, creating a bottleneck in the trend discovery process.

Solution

Agentic scrapers powered by LLMs and self-healing logic automatically adapt to website layout changes and extract structured fashion data.

Challenge

Manual website redesigns were time-consuming, taking weeks per project. Each redesign had inconsistent code quality, was difficult to integrate with existing APIs and backend systems, and required extensive back-and-forth between designers and developers to ensure brand consistency and responsiveness across devices.

Solution

An AI agent that crawls existing sites, redesigns them in new themes, and generates responsive, props-driven React code.

Challenge

Building firmware and application logic for IoT devices required manual coding for each sensor configuration and device workflow. This created a development bottleneck as engineers had to write repetitive code for similar devices, debug hardware-specific issues, and ensure compatibility with the client's drag-and-drop editor framework.

Solution

A custom LLM trained on the client's SDK generates executable, tested code for device workflows and circuit logic.

Challenge

Creating display ads required manual design work for each format and size. Designers spent hours resizing layouts, adjusting typography, and ensuring brand consistency across dozens of ad variations. This process was expensive, slow, and made A/B testing at scale nearly impossible due to the creative bottleneck.

Solution

A multimodal pipeline that generates, critiques, and refines HTML/CSS ad layouts using GPT-Vision.

Challenge

Traditional product photography required expensive studio setups, professional photographers, physical shipping of products, and coordination of models for lifestyle shots. For catalogs with thousands of SKUs, this process was prohibitively slow and costly, often taking weeks to produce images and making it difficult to quickly test new product presentations or seasonal variations.

Solution

Generative diffusion engine creates realistic, studio-quality product visuals and virtual try-ons from smartphone images.

Challenge

Brands struggled to track competitor pricing, product catalogs, search rankings, and stock availability at scale across hundreds of SKUs and multiple marketplaces. Manual monitoring was time-consuming and incomplete, missing critical pricing changes and market shifts. Without real-time data, brands couldn't react quickly to competitive moves or optimize their product positioning effectively.

Solution

Vision + Text LLM agents automatically monitor competitor products across e-commerce sites, detect pricing changes, track search positioning, analyze product attributes, and surface market trends in real-time.