See the unseen.
Automate the impossible.
Turn raw pixel data into real-time operational intelligence. We deploy deterministic, edge-compatible computer vision pipelines that reduce manual inspection errors to near-zero and scale autonomously across your entire physical footprint.
STRATEGIC ALIGNMENT
Recognized for engineering excellence.
Forbes India Award
Honored at the Forbes India Small Business Summit 2024 for exceptional technological enablement and digital engineering solutions.
LiveMint 40 Under 40
Visionary leadership and privacy-first artificial intelligence innovation recognized in India's 40 Under 40 list for CEO Gaurav Jaiswal.
NVIDIA Inception Partner
Member of the elite deep-tech program, collaborating on state-of-the-art AI, generative modeling, and computer vision systems.
Microsoft for Startups
Backed by the Microsoft Founders Hub, driving enterprise scalability with advanced Azure cloud and AI infrastructure support.
Clutch Global Recognition
Double-validated as a top-ranked technology pioneer in India for both Top A-Frame Development and Top Immersive Language Experiences.
ISO 27001:2022 Certified
Globally accredited Information Security Management System (ISMS) compliance, validating our enterprise-grade data security.
Computer Vision. Driven by Outcomes.
We don't just process pixel arrays. We deploy production-ready video pipeline networks that extract structured metrics from feed cameras to run physical locations automatically.
Vision AI for Revenue Growth
- ✓Body Measurement Analytics
- ✓Retail Shelf Monitoring
- ✓In-Store Foot-traffic Heatmaps
Boosts brick-and-mortar sizing conversion and inventory allocation accuracy by up to 25%, while optimizing product positioning layouts based on visual dwell-time.
Vision AI for Operations
- ✓Climb Track Defect Audits
- ✓Manufacturing QA Sorting
- ✓Thermal Overhead Scan Alerts
Flags micro-cracks and structural defects in real-time under 15ms latency, reducing quality escape rates and workplace safety hazards by up to 90%.
Vision AI for Leadership
- ✓Automated Gate & Ingress Logs
- ✓Football Video Analytics Dashboard
- ✓Risk Boundary Intrusion Alerts
Converts video streams from existing facilities security cameras into structured event registries with 100% compliance coverage.
Manual inspection is a massive operational liability.
Human operators suffer from alert fatigue and scaling limitations. True enterprise computer vision removes the human bottleneck entirely.
The Legacy Approach
- ✕
Random Sampling QA
Manual operators can only inspect 5-10% of total throughput, allowing defects to slip through.
- ✕
High Cloud Latency
Sending heavy video feeds to the cloud introduces 500ms+ latency, making real-time physical actuation impossible.
- ✕
Bandwidth Saturation
Streaming 4K video 24/7 from 50 cameras crushes local network infrastructure and spikes cloud costs.
Edge-Native Vision
- ✓
100% Deterministic Coverage
Every single frame is analyzed. Zero fatigue, zero bias, and mathematically proven defect detection rates.
- ✓
Zero-Latency Actuation
Models run directly on local GPU/NPU hardware (Edge), delivering inferences in <15ms to trigger physical sorting arms instantly.
- ✓
Bandwidth Elimination
Raw video never leaves the facility. Only lightweight JSON telemetry (the inference result) is sent to the cloud.
Hard ROI from Day One.
Computer vision is not an R&D experiment. It is a direct lever for driving down operational costs and preventing multi-million dollar defects from reaching your customers.
Automating manual visual inspections across continuous production lines.
Identifying anomalies and triggering robotic sorters faster than human reaction limits.
Moving from 5% random sampling to comprehensive analysis of every single unit.
Eliminating shift changes, fatigue, and human error from the inspection process.
Engineered for Precision Verticals.
Every environment carries unique visual constraints. We build bespoke vision pipelines optimized for custom lighting, frame rates, and edge latency.
Logistics & Transport
Tracking cargo containers and monitoring safety on tall climbing structures manually.
Custom multi-model Yolov9 detection pipelines (Climb Track) to identify safety line attachment states and track infrastructure issues in real-time.
99.8% Safety Event Capture | Zero manual inspection delays
Sports Analytics
Manually cataloging match plays and players' body positions in sports broadcast frames.
DeepSORT object tracking + Pose Estimation models mapping positions, speed metrics, and player actions in real-time under 20ms frame latencies.
100% Automated Play Tagging | Pre-built training datasets output
Luxury Retail & E-Commerce
High return rates due to sizing mismatches across custom design cuts.
Edge-based computer vision body measurement classification parsing measurements in standard camera frames without storing sensitive images.
90% Reduction in sizing returns | GDPR-compliant local inference
Public Utilities
Monitoring structural safety of power lines and high-voltage generators across states.
Unmanned aerial drone video parsing running automated thermal anomaly detection to preemptively flag hotspots.
Pre-empts utility grid outages | Reduced manual hazardous audits
Manufacturing QA
Manual visual verification of high-speed conveyor sorting, letting defective units slip by.
Sub-millisecond TensorRT models triggering digital sorting switches to isolate micro-defects.
0.01% Defect escape rate | Runs 24/7 at 120 items per minute
Smart Building Safety
Monitoring occupant density and fire hazard blockages in crowded arenas.
Zero-PII semantic segmentation engines that audit emergency egress paths without tracking faces or saving local feeds.
GDPR Compliant | Automated safety compliance registries
Production Computer Vision.
We deploy edge-optimized deep learning models, not just cloud integrations. Look at our live running vision pipelines.
Climb Track Safety System
Developed an automated YOLO-based object detection model configured to run on low-power Edge platforms. It monitors safety line attachments and climbing clip counts for operators on tall telecom towers.
Instantly alerts facility hubs of harness deviations under 15ms local edge latency.
Real-World Vision Architectures.
We do not build generic proof-of-concepts. We engineer specialized, hardware-accelerated computer vision pipelines designed for extreme physical environments.
A continuous production line moving at 5 meters per second cannot wait for cloud round-trips to identify defects. By the time the cloud responds, the defective unit has already passed the sorting mechanism.
We deploy quantized YOLO models directly onto NVIDIA Jetson Orin hardware installed on the factory floor. Cameras feed raw RTSP streams via local ethernet. The GPU runs inferences locally in <15ms, triggering PLC controllers via Modbus/TCP to actuate sorting arms instantly. Only the JSON log of the defect is synced to AWS for central reporting.
The Business Case for Vision AI.
We understand that deploying Computer Vision requires low latency, camera fleet compatibility, and absolute privacy compliance.
For the CFO
We leverage existing CCTV infrastructures, eliminating massive equipment rollouts. We deliver optimized pipelines on a fixed 12-week cycle.
- Feasibility & Frame AuditWeek 1-2
- Edge Model MVPWeek 3-8
- Scale & PLC HookupWeek 9-12
For the CTO
Deploy optimized weights directly onto low-power edge nodes (Jetson/Intel) using TensorRT and ONNX runtimes.
Native integration with RTSP video streams and network video recorders (NVR).
Under 15ms local loop response speeds for immediate physical PLC triggers.
Containerized deployment models (Docker) controlled via remote Kubernetes clusters.
For the CISO
Maintain data custody. Visual feeds are processed on-device and instantly discarded. Faces and license plates are redacted at ingest.
Zero-PII local inference mode with automated facial and gait anonymization.
Isolated VPC metadata transport; raw video frames never leave physical premises.
ISO 27001 standard video storage protocols and private VPC containment.
The Vision AI Delivery Framework.
We accelerate enterprise vision deployments using our 12-week structured framework and pre-engineered software components.
Discovery
Frame & Stream Audit. We analyze camera layouts, lighting, frame rates, and latency limits to size processing requirements.
Design
Model Selection. Choosing architectures (YOLOv9 vs RT-DETR vs SegFormer) and mapping CUDA edge hardware specs.
Prototype
Model Training. Fine-tuning models on client custom datasets and setting up basic bounding box confidence scales.
Production
Edge Orchestration. Compiling models to TensorRT/ONNX, setting up local edge nodes, and connecting downstream PLC hooks.
Optimization
Scale & Accuracy Tuning. Optimizing model weights (FP16/INT8 precision scaling) to hit consistent frame-rates under variable lighting.
Kraft-Lens
A pre-trained vision backbone that accelerates custom object annotation and bounding box tracking, cutting initial model training prep cycles by up to 4 weeks.
RTSP Stream Multiplexer
A low-overhead camera feed multiplexer that handles video ingestion, decoding, and frame queuing to process up to 32 streams concurrently on a single GPU.
Face & PII Redactor
An edge-native segmentation pipeline that automatically blurs human faces, vehicle license plates, and text tags locally before indexing frames.
The Engineering Stack.
We don't rely on basic APIs. We build close to the metal, utilizing low-level C++ and GPU acceleration to squeeze every drop of performance out of edge hardware.
Hardware-Accelerated Edge CV
We deploy models directly to physical hardware (NVIDIA Jetson, Edge TPU) using low-level optimization libraries to achieve maximum FPS at minimal power draw, completely bypassing cloud latency.
Air-Gapped Privacy & Governance.
Streaming proprietary manufacturing lines or hospital floors to public clouds is a massive security risk. We build architecture that keeps your visual data on-premise.
Zero-Data Egress
Inferences happen on the edge device. No raw video or images ever leave your local network. Only metadata (the JSON result) is sent to central dashboards, ensuring absolute IP protection.
Strict GDPR Compliance
For public spaces and enterprise locations, we integrate real-time face and license-plate blurring at the hardware level before any processing occurs, guaranteeing strict regulatory compliance.
Model Drift Monitoring
Physical environments change (lighting, camera angles). Our MLOps pipelines automatically detect model drift and trigger alert notifications for retraining before accuracy drops.
INSTITUTIONAL TRUST // GLOBAL FOOTPRINT
Delivering complex software
for ambitious organizations.
A decade of institutional engineering. Since 2016, Kraftors has been the silent engine behind mission-critical systems. We don't build vaporware; we build for the next 10 years.
Sovereign validation
from industry leaders.
E-Commerce Platform Migration
Successfully migrated their e-commerce portal from .NET to Magento 2, providing continuous management and scaling for over 6 years.
Imtiaz Sayed
Owner, Oxshott Collections
AI Sleep Monitoring Platform
Built an intelligent, privacy-first sleep monitoring solution powered by real-time data and machine learning.
Shadi Abu Hayyah
CEO & Founder, Continual Sleep App
All-in-One AI Platform
Developed a category-based generative AI platform eliminating the need for multiple AI subscriptions.
Prasad Kale
Founder, Kaletech Private Limited
Ed-Tech Platform Success
Designed a user-friendly website allowing students to easily log in and register for various courses and workshops.
Tushar Chetwani
Author & Memory Trainer, Memory Infinite
Media Apps & Reader Engagement
Partnered to build engaging applications for readers during Covid, including large-scale platforms like the All India Memory Test.
Alok Sanwal
COO, Dainik Jagran - inext
Strategic Tech Partnership
A strong collaborative partnership executing multiple complex projects, from e-commerce platform builds to full-scale migrations.
Shubhra Shrivastava
CEO, Digiprima Technologies
Frequently Asked Questions
Clear, authoritative answers to your technical and operational questions.
Enterprise computer vision involves deploying deep learning models to analyze visual data (images/video) in real-time, enabling automated quality control, safety monitoring, and logistics profiling without human intervention.
Edge processing eliminates bandwidth constraints and cloud latency. By running inferences directly on local hardware (like NVIDIA Jetsons), we achieve sub-15ms response times, which is critical for actuating physical robotics or stopping production lines instantly.
No. We utilize zero-data egress architectures. The raw video is processed on the edge and instantly discarded. Only the metadata (e.g., 'Defect found at timestamp X') is transmitted to your central dashboards.
Human inspectors typically hit 85-90% accuracy due to fatigue. Our custom foundational models, trained specifically on your physical data, consistently achieve >99% inference accuracy 24/7 without degradation.
Usually, no. Our pipelines are designed to ingest standard RTSP streams from existing IP cameras. We only recommend hardware upgrades if the physical resolution or lighting is mathematically insufficient for the required inference.
We implement continuous MLOps pipelines. When confidence scores drop due to environmental drift, the system flags edge-cases for human review, retrains the model, and deploys the updated weights over-the-air.
Yes. By utilizing unsupervised anomaly detection and autoencoders, our systems can flag deviations from the 'normal' state, even if that specific type of defect has never been seen before.
A typical Proof of Concept (POC) takes 4 to 6 weeks. This includes site feasibility, synthetic data generation, model training, and deploying a single edge node to validate accuracy in your live environment.
Deploy Your First Edge Node in 30 Days.
Skip the endless R&D cycles. Our engineers will audit your physical facility, map the optimal hardware constraints, and deploy a working vision pipeline tailored to your exact environment.
Book a Technical Assessment