From robotic arms sorting fruits without bruising a single one, to AI receptionists that never miss a call: a look at the systems we've built for real clients and real problems.
Client: Confidential (American Automation / AgriTech Company)
Sorting and packaging mixed produce by hand is slow and error-prone. The client needed an autonomous robot that could identify, handle, and sort mixed fruits on the fly, fulfil custom packaging orders without human input, and do it all without bruising delicate inventory.
We built an AI-driven spatial recognition and manipulation pipeline pairing an xArm robotic arm with an Intel depth camera. A tuned 2D detection model classifies apples, bananas, and oranges in mixed piles, while the depth camera streams the spatial coordinates the arm needs to approach and grasp safely, keeping compute low and control tight enough to avoid damaging surrounding produce. A programmable tasking engine handles both categorical sorting and custom kitting (e.g. "4 apples, 2 bananas, 3 oranges into Box A"), actively counting items as it packs.
A manual sorting line became a fully autonomous, high-speed workflow: flawless picking and counting, zero damage to fragile produce, and a framework the client can adapt as demand changes.
Client: Confidential (MedTech AI Developer)
Training robust computer vision models takes massive volumes of perfectly annotated data, but photographing real surgical equipment inside an operating theater runs straight into privacy regulations, high cost, and logistics. The client needed to scale up training data volume without relying solely on manual collection.
We architected a dual-pipeline synthetic data generation system combining procedural 3D rendering with interactive augmented reality. An automated Blender pipeline drops 3D models of medical equipment into simulated operating theaters, sweeps the virtual camera through multiple positions and lighting conditions, and auto-renders each variation with precise bounding-box coordinates saved to a backend file. Alongside it, a custom AR Android app lets users place those same 3D models anywhere in physical space through their phone camera; every photo taken saves its bounding-box data in the exact same backend format. The outputs of both pipelines are merged into one unified dataset: mathematically perfect, high-volume Blender renders combined with the infinite real-world background variation captured through the AR app.
Thousands of perfectly annotated training images were generated in a fraction of the time manual photography would take. The richer, more diverse dataset reduced model overfitting, improved inference accuracy, and significantly accelerated the client's time-to-market.
Client: Guns Nation Shooting Range, Ludhiana, Punjab, India
Indoor shooting ranges run on zero-tolerance safety margins: managing multi-tiered firearm clearances, preventing rule violations like weapon sweeping or off-target flagging, and keeping rigorous attendance and usage records. Manual oversight by Range Safety Officers can't watch every stall at once, track every patron through the facility, or automatically verify that a shooter is cleared for the weapon in their hands. Guns Nation needed automated, real-time oversight, tracking, access verification, and hazard prevention.
We built an active, real-time safety and audit layer on a synchronized multi-camera network covering the entire range. Human detection and recognition identify each person on arrival and track them across overlapping camera feeds (lobby, staging area, shooting lanes), maintaining a persistent identity for their whole visit. This drives automated attendance logging with tamper-proof arrival/departure timestamps, no manual sign-in required. Because the system always knows who is in which lane, it detects the specific weapon in use and matches it against that shooter's verified clearance tier, flagging any unauthorized firearm pickup instantly. Human pose and orientation estimation then verifies each shooter is correctly positioned at the firing line with the weapon oriented downrange; any unsafe movement, such as turning with a drawn weapon or flagging adjacent stalls, triggers an instant safety alert before an accidental discharge can occur.
Guns Nation moved from passive CCTV recording to active, vision-based safety monitoring: facility-wide spatial awareness with no unauthorized entry going unnoticed, zero-blindspot detection of muzzle deviations and improper handling, fully automated enforcement of skill-gated firearm access, and the elimination of manual attendance and weapon check-out logs.
An AI-powered phone receptionist that handles incoming and outgoing calls with natural, human-like conversation, and never clocks out.
Watches a Google Drive folder, pulls invoices the moment they land, and turns scanned PDFs and photos into clean, validated accounting data.
Reads onboarding paperwork and medical reports and turns unstructured pages into structured, usable data through a simple upload interface.
Tracks ASINs and products across marketplaces, pulling live pricing so teams can compare and react without checking every storefront by hand.
Turning company documents into an AI knowledge assistant: one that retrieves the right context and holds a conversation about it, not just a keyword search.
A live 3-camera demo (one Intel RealSense depth camera and two standard webcams) where clicking a point in one feed instantly projects the same real-world point onto the others. It's the same math behind self-driving car sensor fusion, AR-guided surgery, and volumetric 3D capture.
Six-degrees-of-freedom pose estimation for parts on a manufacturing line, giving downstream robotics the precise position and orientation data they need to pick, place, and assemble.
An augmented-reality inspection app that overlays quality-control cues directly onto the physical part being inspected, speeding up defect detection on the line.
The cloud, ML, and engineering foundations behind every case study above.
Tell us what you're trying to automate, sense, or understand: we'll tell you what it takes to build it.