Boss fights, beaten

The challenge

UK platform Haute Secret Shoppers aggregates designer inventory from major luxury retailers into one catalogue — a very complex taxonomy keyword search can’t cope with. And its shoppers don’t think in filter menus: they describe a moment (“wedding guest”), a vibe (“boho”), or simply show a photo of what they want.

The play

We built the platform’s AI layer end to end — categorizing, attributing, and embedding every product — then put a stylist named Ellie on top:

• Semantic search on Qdrant vector retrieval — it understands product meaning, visual style, occasions, and trend language, with brand, color, size, price, and sale filters layered straight into the results
• Ellie, a live-chat stylist powered by large language models (OpenAI + AWS Bedrock), asks clarifying questions and remembers context across the whole shopping session
• Multimodal queries — text, image, or voice, in any mix — backed by AI vision tagging that classifies every product image to keep search and styling accurate
• Agentic outfit generation that anchors on a starting piece, enforces real styling rules, self-corrects against budget and style constraints, and lets shoppers swap any single piece of a look
• An AI wardrobe — photograph your own clothes so styling accounts for what you already own — plus a peer-to-peer preloved marketplace served by the same AI search, with direct add-to-cart and checkout from results

The result

Live in production on Haute Secret Shoppers: ask for “top-handle bags with a tassel” or upload a sneaker photo, and refined, shoppable results come back across new and preloved designer stock — the deepest AI build in our portfolio, and the pattern transfers to any large catalogue.

The challenge

A US training provider needed heavy-vehicle operators trained on real controls, real scenarios, and real weather — without real-world risk. The simulator existed; live instruction, multiplayer, and the hardware flexibility to run it anywhere did not.

The play

We engineered the multiplayer architecture from scratch and rebuilt the training loop around the instructor:

• Custom multiplayer across LAN, Steam, and a supervisor dashboard — instructors join live sessions with voice chat to guide trainees in real time
• A Shared Replay system that saves every session to the supervisor's machine for multi-angle review
• Vehicle-specific interactive cabin controls and dynamic, plowable snow physics across new tutorial and reversing modules
• A unified configurator covering every setup — keyboard and mouse, VR headsets, motion trackers, professional simulation rigs, and headset-free triple-monitor mode with motion tracking

The result

Trainees now master complex vehicles in rain, snow, and traffic before they ever touch the road — while instructors watch live, coach by voice, and replay every run from any angle. One build runs on everything from a desktop to a full simulation rig.

The challenge

A fully immersive VR metaverse of alien worlds and virtual DJ performances needed avatar creation that feels physical — browsing life-size characters with your hands — without letting asset-heavy, high-fidelity models wreck the frame rate.

The play

Our engineers embedded in the AAA co-development team and took ownership of the avatar experience:

• Proximity-triggered avatar terminals that activate as players approach — and power down as they walk away
• Gesture-driven browsing: swipe through life-size character previews with VR hands
• Asynchronous asset loading in Unreal Engine that keeps frame rates smooth under heavy character models
• A customization backend for character colors and multiple persistent loadouts — plus VR 3D menus, objective pointers, and AccelByte backend integration in Blueprints and C++

The result

The avatar experience players touch first in Sensorium Galaxy — physical, smooth, and persistent across worlds — with multiple variations of the customization machines running throughout the metaverse.

The challenge

A global entertainment-technology company needed high-quality renders in volume — every mesh, material, camera angle, and lighting permutation — and its artists were burning days on manual iteration inside Unreal Engine.

The play

We architected and built the complete Editor Utility from the ground up, taking full ownership of the pipeline:

• Python automation for mesh spawning, dynamic material swaps, camera sequencing, and lighting configurations
• Automated capture from a multitude of predefined camera angles — including Fibonacci-sphere camera arrays with full lens control
• An artist-facing Editor Utility UI that abstracts the whole pipeline into one control panel
• A distributed architecture — the local machine drives scene control while a dedicated node handles the heavy rendering

The result

Batch renders at scale across predefined camera and lighting setups, with manual iteration time slashed for artists who need thousands of images — an end-to-end automation solution delivered as one tool.