Creating the Future with AI and Machine Learning

Creating the Future with AI and Machine Learning
We are a leading software house specializing in advanced AI and machine learning solutions. Our expertise spans R&D, industrial integrations, large language models (LLM), computer vision (CV), and natural language processing (NLP).
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Services
AI & Generative Intelligence
We create systems based on large language models (GPT, Claude, LLaMA), implement Retrieval-Augmented Generation (RAG), and build AI Assistants using LangChain, LlamaIndex, and Semantic Kernel.
Computer Vision & Perception
We develop object detection and segmentation systems using the latest technologies (YOLOv8, Detectron2, MMDetection) and solutions based on LIDAR and CV and sensor fusion for industrial applications.
ML & MLOps
We design and deploy classification, regression, and predictive models using AutoML, optimize inference (ONNX, TensorRT), and create comprehensive MLOps pipelines.
Our works
We have implemented various projects using artificial intelligence, computer vision, and advanced language models.
Pallet Recognition System for AGV
Client/Industry: Leading mobile robots/AGV manufacturer
Problem: Difficulty in precise recognition of unevenly placed pallets in industrial environments.
Goal: Develop a vision system for accurate pallet detection and localization.
Solution: We implemented an advanced vision solution that enables precise recognition and localization of unevenly placed pallets in pallet nests. The integration of machine vision algorithms with autonomous forklifts increased operational efficiency and safety in industrial environments.
Results:  95% Fewer Warehouse Accidents: The AI-powered detection system significantly reduced safety risks and improved operational workflows. Cost Savings: The outsourcing approach saved 35% compared to building an in-house AI team.
Empathiq – Reputation Management Platform
Client/Industry: Healthcare / San Diego, California
Problem: Inability to effectively monitor and respond to customer opinions across multiple channels.
Goal: Create a real-time sentiment analysis system integrated with existing CRM infrastructure.
Solution: We developed a comprehensive platform using NLP and advanced sentiment analysis to monitor customer opinions in real-time. The system enables quick response to feedback and integrates with existing CRM systems, providing companies with key indicators regarding brand reputation.
Results: Within the first three months of implementation, response time to negative feedback decreased by 65%, while the customer satisfaction score (CSAT) increased by 22%. The system detected an average of 93% of key brand mentions in real time, enabling the customer support team to proactively address issues. CRM integration improved team efficiency by 30%, thanks to automated case tagging based on customer sentiment.
Fork Recognition System for AGV
Client/Industry: Industrial logistics
Problem: Forklift forks were often undetected by standard sensors, leading to AGV (mobile robots) collisions and safety hazards.
Goal: Develop a reliable AI-powered detection system for forklifts' forks.
Solution: We developed an AI-powered detection system using YOLOv5 and Faster R-CNN with 95% precision and real-time performance (50ms). The solution included a mobile app for live detection optimized with ONNX Runtime, and extensive image collection and annotation for training. The system was integrated with IoT infrastructure using AES-256 encryption
 Results: 95% fewer accidents and 35% lower costs compared to in-house AI development.
Predictive Monitoring System for Transport Vehicles
Client/Industry: Industrial logistics
Problem: Frequent and costly forklift failures causing operational downtime and high service costs.
Goal: Develop an intelligent system for predicting transport vehicle failures based on operational data.
Solution: Our team developed a machine learning module that analyzes vehicle operational data (such as motor hours, number of impacts, load exceedances) and predicts the probability of four types of failures: forks, carriage, engine, and pump. The implementation included synthetic dataset generation, preprocessing, algorithm implementation , and containerized deployment with high-availability HTTPS endpoints. 
 Results:
Real-time response: less than 20 milliseconds
Prediction accuracy: over 91% for forks, carriage, engine, and pump failures
Downtime reduction: 30–50% less unplanned downtime
Maintenance planning: up to 2 weeks before predicted failure
Operational savings: up to €120,000 per year for a fleet of 50 forklifts
Scalability: supports over 1,000 vehicles with containerized AWS infrastructure

Distributed LLM Locally
Client/Industry: Data-sensitive organizations/insurance
Problem: Need for advanced language model capabilities without compromising data security by sending it to external servers.
Goal: Create a secure solution for utilizing large language models in a local environment.
Solution: We created a secure solution enabling the use of large language models in a local environment, eliminating the need to send data to external servers. The implementation is based on modern RAG technologies, OpenAI, and LangChain, providing full control over processed data and high performance while maintaining complete data privacy.
 Results: 
100% of data processed locally, ensuring no data leaves the client’s infrastructure,
30% annual cost savings on data processing,
compliance with GDPR and internal security policies, verified through an IT security audit.


News - AI in 2025: Key Trends in Generative AI, Computer Vision, and MLOps
April 2025 — The AI revolution is accelerating. From advanced AI agents and hybrid LLM architectures to cutting-edge computer vision and fully automated MLOps pipelines, the state of artificial intelligence in 2025 is defined by performance, efficiency, and trust.
🤖 Rise of AI Agents & Frameworks
AI development frameworks like LangChain (LangGraph), Semantic Kernel, and LlamaIndex now power intelligent, multi-agent systems with long-term memory, planning, and tool integration.
🌐 Cost-Efficient AI with Hybrid LLMs
The HERA (Hybrid Edge-cloud Resource Allocation) scheduler reduces cloud inference costs by up to 30% by intelligently routing tasks between local small language models (SLMs) and cloud-based LLMs.
🧠 Prompt Engineering Becomes Critical
New research shows that prompt sentiment significantly impacts LLM accuracy. Neutral prompts yield the most factual outputs, while negative tones reduce precision by 8.4%.
⚙️ Autonomous AutoML with LLM Agents
The AutoML-Agent framework automates the entire machine learning pipeline — from data preprocessing to model selection — using LLM-powered agents, making ML accessible to non-experts.
👁️ YOLOv12: Attention-Based Computer Vision
The release of YOLOv12, an attention-centric architecture, improves real-time object detection accuracy and speed for tasks like fire detection, drone vision, and industrial safety.
🔬 Camera-LIDAR Fusion: Hardware Innovation
Kyocera's integrated camera-LIDAR sensor eliminates parallax errors and calibration delays, combining 3D depth and visual data in one unit for autonomous systems.
🔧 MLOps in 2025: Automation & Governance
Modern MLOps pipelines now incorporate automated model monitoring, drift detection, CI/CD for ML, explainability (XAI), and compliance with regulations like GDPR and the EU AI Act.
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Team
Thomas, PhD
Machine Learning & Computer Vision Research Engineer
Distinguished mathematician and AI researcher with exceptional software engineering capabilities, specializing in R&D and back-end development. Expert in designing and implementing sophisticated algorithms across applied mathematics, automated planning, and machine learning. Seamlessly combines profound theoretical knowledge with practical programming expertise across multiple languages to deliver innovative, high-performance solutions. Scientific interests include autonomous systems inspired by neuroscience, such as Adaptive Resonance Theory and Spiking Neural Networks.

Raphael
Backend/AI/ML Developer
Accomplished Senior Backend/AI/ML Developer with comprehensive experience in developing and deploying advanced systems leveraging Artificial Intelligence, Machine Learning, and cutting-edge technologies. Since 2017, has been instrumental in delivering transformative solutions for complex projects across diverse industries. Masterfully combines technical expertise with strategic problem-solving, continuously exploring and implementing emerging technologies to drive efficiency and breakthrough innovation.
Peter
Senior Backend/AI/ML 

Machine learning expert with a particular focus on classification and regression models. Experienced cloud solutions architect, specializing in advanced optimization of inference systems. His innovative design approach enables the creation of highly scalable and efficient AI implementations in complex production environments.
Wojciech
AI & ML Specialist 

Wojtek builds advanced AI systems with a focus on generative intelligence, computer vision, and MLOps. He works with large language models (GPT, Claude, LLaMA), RAG pipelines, and AI assistants using LangChain, LlamaIndex, and Semantic Kernel. He develops object detection solutions (YOLOv8, Detectron2) and fuses LIDAR with vision for industrial use. He also designs ML models and MLOps pipelines, optimizing inference with ONNX and TensorRT.
Technology
Programming Languages
Python for advanced data analysis and machine learning
TypeScript for building modern user interfaces
C++/CUDA for high-performance parallel computing
Julia for Accelerated Scientific Computing
Scala/Java for building scalable enterprise systems
Statistics and data science in R
AI/ML Frameworks
We utilise the latest tools such as PyTorch, TensorFlow and JAX for deep learning, Hugging Face Transformers and Diffusers libraries for working with language models and generative models, and LangChain and LlamaIndex for building advanced AI applications. We optimise performance through ONNX and TensorRT.

Computer Vision
We implement advanced vision solutions using YOLOv8, Detectron2 and MMDetection for object detection, OpenCV and Open3D for image analysis, and specialised 3D point, LIDAR and SLAM technologies for spatial mapping, with a focus on efficient edge processing.
Backend & Cloud
We build scalable and performant systems based on FastAPI, Node.js and Spring Boot, deploy modern communication protocols (gRPC, GraphQL, REST), optimise search through vector databases, and provide reliable production environments through Docker, Kubernetes and automated CI/CD processes.
Research and Development
Our company actively invests in scientific research and technological innovation, collaborating with leading institutions and industry experts.
25%
Scientific staff
With doctoral degrees, possessing specialized knowledge in the fields of artificial intelligence and machine learning
6
Completed research projects
Carried out in collaboration with top academic centers, delivering breakthrough solutions for the industry
3
Patent applications
Filed for innovative technologies that will revolutionize the way data is processed and analyzed in AI systems
9
Scientific publications
In renowned industry journals, confirming our position as experts in the field of advanced technologies
Contact
Reach out to us directly and our team will respond to your inquiry as soon as possible.
Email address 
contact@scientiq.ai
Address
Scientiq 
Av. T. Kościuszki 80/82,
90-437 Lodz, Poland
Phone
tel. +48 42 209 18 90
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