CIN: U27102TN2023PTC162066 IEC: AALCC1858J GSTIN: 33AALCC1858JIZH
Proof of Capability

Flagship Deep-Tech Engineering Projects

These project tracks demonstrate our ability to take advanced theory and translate it into functional hardware and edge systems.

Project Track 01

Local Edge AI Chatbot (Institutional LLM)

The Challenge: Universities and enterprises face strict data privacy rules and recurring costs when sending internal documentation to public cloud-based LLM APIs.

The Solution: An on-premises conversational intelligence appliance that performs zero-latency vector searches over institutional handbooks, syllabus databases, and internal policies without routing bytes over public external networks.

Quantized Open-Source LLMs Local Vector Store Python / Flask Local Linux Edge Appliance
Deploy Local AI for Your Institution

Technical Architecture

  • Embedding Layer: Document chunking and local high-dimensional vector generation.
  • Inference Engine: CPU/Edge-accelerated 4-bit/8-bit quantized LLM weights.
  • Privacy Assurance: Strictly 100% offline; zero external network egress during queries.
  • Deployment Cost: Zero recurring API token subscriptions.

Communication Subsystems

  • Ad-Hoc Wireless Mesh: Sub-second broadcast beacons over 2.4GHz RF and LoRa.
  • CAN-Bus Intercept: Real-time extraction of wheel speed, brake pedal state, and steer angle.
  • Collision Warning Matrix: Algorithmic forward time-to-collision warning algorithms.
  • Latency Profile: Under 25ms end-to-end warning propagation between units.
Project Track 02

Vehicle-to-Vehicle (V2V) Communication

The Challenge: Blind spots and delayed braking reactions in multi-vehicle factory convoys and autonomous test tracks cause preventable collisions.

The Solution: Short-range ad-hoc wireless mesh units that broadcast real-time telemetry between moving platforms, providing collision warnings before obstacles are physically visible.

ESP32 MCU CAN-Bus Transceiver NRF24L01+ LoRa RF
Consult on V2V Integration
Project Track 03

GPS-Denied Autonomous Inspection UAV

The Challenge: Inspecting indoor warehouses, boiler furnaces, and underground pipelines where satellite signals cannot penetrate, rendering standard drone position-hold useless.

The Solution: An autonomous quadcopter leveraging down-facing optical flow vision odometry and high-frequency LiDAR rangefinding to achieve stable hovering and obstacle avoidance indoors.

PX4 Flight Stack Optical Flow Sensor 1D / 2D LiDAR Companion SBC
Inquire on Confined Drone Systems

Navigation & Sensor Fusion

  • Position Hold: Real-time optical displacement tracking over floor textures.
  • Altitude Retention: Millimeter-accurate LiDAR surface reflection measurements.
  • Companion Computer: Processes safety overrides and companion diagnostics.
  • Industrial Target: Boiler furnace tubes, high-voltage tunnels, and mines.