Critical Infrastructure for Autonomous Intelligence.
Litcq Studio by Sheva Aqila Ramadhan engineers high-velocity distributed scrapers, edge machine learning models, and deterministic multi-tenant RAG systems with industrial reliability.
Engineered for Precision, Resilience, and High Throughput.
Drawing inspiration from industrial supply chain infrastructure, Litcq Studio treats data as a critical utility: reliably harvested, continuously validated, and instantly transformed into intelligence.
Distributed Scraper Clusters & Geo Ingestion
Multi-VPS headless Playwright networks and API failover pools designed to harvest high-density geospatial datasets across hundreds of regional queries with zero IP bans.
Architectural Guarantees:
- •Multi-VPS distributed Playwright clusters with stealth fingerprinting
- •Automated cooldown state machines and Apify multi-account failover
- •Structured data audit with coordinate geolocation extraction
Knowledge Distillation & Edge Acceleration
Deep neural network compression transferring heavy teacher architectures into lightweight students (~500K params) deployable to low-power edge microcontrollers.
Architectural Guarantees:
- •Multi-teacher Knowledge Distillation with KD+ and dynamic re-routing
- •Direct embedded deployment to ESP32-CAM and Raspberry Pi 5 via Arduino IDE
- •72-hour continuous hardware stress testing with verified zero degradation
Multi-Tenant RAG & Predictive Intelligence
Production conversational engines with strict Role-Based Access Control (RBAC), context leakage prevention, and predictive classifiers for enterprise telemetry.
Architectural Guarantees:
- •Role-segregated RAG architectures with Supabase vector database
- •Prompt caching and context optimization reducing token spend by 56%
- •Multilingual NLP (XLM-R) and tabular risk models (Scikit-learn)
Flagship Platforms & Live Deployments
Production-grade systems engineered and maintained by Litcq Studio, designed to identical architectural specifications, strict equal sizing, and rigorous reliability standards.
LitcqnWatch
Frame-synchronized collaborative video streaming platform with sub-second drift compensation, stream extraction, and real-time room chat.
- •Frame-accurate playback synchronization across distributed network clients.
- •Built-in stream extractor supporting custom video URLs and catalog browsing.
- •Full host management: PIN protection, public/private lobby, and kick controls.
Distributed Scraper Cluster
High-density geospatial scraping engine with multi-VPS Playwright clusters, anti-bot cooldown state machines, and API failover pools.
- •Automated Playwright cluster scraping 13,000+ regional query combinations across Java.
- •State machine with automated cooldown, anti-blocking logic, and multi-token failover.
- •Geolocation coordinate extraction, phone/rating parsing, and daily automated backups.
Edge Knowledge Distillation
Model compression pipeline transferring heavy deep learning networks into compact student architectures deployable on low-power microcontrollers.
- •Multi-teacher Knowledge Distillation (InceptionResNet & YOLO11) into ShuffleNetV2.
- •Compiled and deployed directly to ESP32-CAM via Arduino IDE, linked to Raspberry Pi 5.
- •Completed a 72-hour continuous non-stop hardware stress test without degradation.
Enterprise RAG AI Engine
Role-Based Access Control conversational system with vector search, context leak prevention, and 56% token consumption optimization.
- •Multi-tenant data isolation strictly preventing cross-role and cross-class leakage.
- •Context builder optimization reducing token consumption from ~8K down to ~1K-3K.
- •Evaluated across Claude 3.5 Sonnet, DeepSeek, Qwen, and GPT-4o-Mini models.
Comprehensive Engineering Capabilities
Every system is engineered to identical modular standards, with production reliability and verifiable performance under real-world enterprise load.
Distributed Scraper & Data Ingestion
Multi-VPS Playwright stealth clusters
Engineered crawling networks designed to harvest structured records from dynamic geospatial web applications, with browser pool recycling, anti-ban cooldowns, and schema auditing.
Architectural Deliverables:
- •Multi-VPS distributed Playwright architecture with stealth fingerprinting
- •State machine auto-resume with Apify multi-account failover fallback
- •Automated daily backups, coordinate extraction, and offline CSV audits
Implementation Toolchain:
Edge AI & Knowledge Distillation
Low-latency microcontrollers & IoT
Compressing heavyweight deep learning networks into compact student models (< 500K params) deployable to low-power edge hardware with verified endurance.
Architectural Deliverables:
- •Multi-Teacher Knowledge Distillation (KD+) with dynamic re-routing
- •Microcontroller deployment to ESP32-CAM & Raspberry Pi 5 via Arduino IDE
- •72-hour continuous hardware stress testing with zero endurance degradation
Implementation Toolchain:
Computer Vision & Imbalance Mitigation
Object detection, YOLOv8/11 & classification
Custom neural vision systems engineered to tackle severe real-world data skew (class imbalance) using weighted losses, sampling strategies, and state-of-the-art detectors.
Architectural Deliverables:
- •Real-time object detection with YOLO11L, YOLOv8 (mAP@50 >90%), and RF-DETR-M
- •Mitigation of extreme 70%+ class imbalance via WeightedRandomSampler & ConvNeXt
- •Zero-Shot Object Detection with Hugging Face Grounding DINO & inference caching
Implementation Toolchain:
Multi-Tenant RAG & Multilingual NLP
RBAC secure conversational & tabular models
Production conversational engines with strict tenant boundary enforcement, prompt token optimization, multilingual text classification, and tabular cybersecurity ML.
Architectural Deliverables:
- •Multi-tenant RAG with RBAC isolation and Supabase pgvector integration
- •56% prompt token reduction (~1K-3K per query) via structured context building
- •Multilingual NLP classification (XLM-R) and IoT botnet detection (Random Forest)
Implementation Toolchain:
Engineering Rigor & Verified Background
Litcq Studio is an independent engineering laboratory founded by Sheva Aqila Ramadhan, combining rigorous data science research with production deployment across microcontrollers, distributed crawlers, and cloud systems.
Sheva Aqila Ramadhan
Founder & Lead Data Scientist
"From building distributed scrapers harvesting 250,000+ geospatial records across 80+ cities to compressing neural networks down to 500K parameters for edge microcontrollers, Litcq Studio engineers systems that deliver verifiable intelligence under enterprise stress."
Universitas Negeri Semarang (UNNES)
S1 Teknik Informatika (2023 - Present) • Prior: SMK Negeri 1 Pemalang (Computer & Network Engineering).
CV Serpihan Tech Solution (AI & IoT Division)
5-month engineering internship: Multi-Teacher Knowledge Distillation, edge deployment on ESP32-CAM / Raspberry Pi with 72h stress test, and multi-tenant RAG chatbot (Sekolahin).
Distributed Crawlers & Production Models
Architected multi-VPS Playwright scrapers harvesting 250,000+ geospatial records, YOLOv8/11 vision pipelines, and live collaborative streaming platform LitcqnWatch.
Production Toolchain & Infrastructure Stack:
Real Market-Aligned Rates & Scopes
Tarif realistis berbasis riset pasar dan rekam jejak riil pengerjaan proyek: tanpa markup agency berlebihan, ruang lingkup transparan, dan akses langsung ke Lead Data Scientist.
Custom Web Scraping & Ingestion
Ekstraksi data otomatis dari direktori web dinamis, e-commerce, Google Maps, atau portal publik dengan bypass anti-blokir dan format rapi (opsi unit @ Rp 40rb / kota).
Deliverables & Inclusions:
- •Ekstraksi atribut lengkap: nama, kontak, foto URL, dan koordinat lat/long
- •Playwright stealth crawler dengan cooldown otomatis dan failover token
- •Hasil data terstruktur: CSV per wilayah/kategori, JSON, atau SQLite
- •Garansi deduplikasi data, audit kualitas, dan 1x revisi format kolom
ML & Computer Vision Sprint
Pelatihan arsitektur neural network kustom (YOLOv8/11, ConvNeXt), kompresi Knowledge Distillation, hingga deployment ke perangkat edge microcontrollers (ESP32-CAM / RPi 5).
Deliverables & Inclusions:
- •Exploratory Data Analysis (EDA) dan penanganan class imbalance ekstrem
- •Pelatihan & fine-tuning arsitektur vision/klasifikasi (PyTorch, YOLO)
- •Kompresi Knowledge Distillation ke model student ringan (< 500K params)
- •Deployment ke endpoint REST API (FastAPI) atau firmware IoT (Arduino IDE)
Enterprise RAG & Full Build
Pembangunan arsitektur AI lengkap: Chatbot multi-tenant dengan pengamanan Role-Based Access Control (RBAC), Supabase vector store, serta penghematan token hingga 56%.
Deliverables & Inclusions:
- •Role-Based Access Control (RBAC) mencegah kebocoran data antar tenant
- •Integrasi database vektor (Supabase pgvector) dan prompt context caching
- •Evaluasi model LLM (Claude, DeepSeek, GPT) untuk rasio biaya-kualitas optimal
- •Integrasi full-stack (Next.js web client / API) dan dokumentasi deployment