AI Services & Consulting
At Webnoria, you will find the expertise to carry out an end-to-end AI consulting or development project and shape your ideas into ready-to-go solutions. We assist you throughout the entire AI implementation lifecycle, from the early assessment phase to deployment and beyond.
- Industries
- Roadmap
- Tech stack
Our AI services
- Use case identification
- Data mapping and quality assessment
- Existing solution audit
- Advisory with initial project setup
- Development process review
- ROI analysis
- Solution architecture design
- Tech stack selection
- Project budgeting
- MVP conceptualization
- Risk management strategy creation
- User training and support
A cloud-based ERP platform is an ERP system hosted and managed in the cloud by an ERP vendor, while an on-premises ERP system is deployed and run on local hardware and servers by the company’s in-house IT department or a third-party service provider. While both options support the same business processes and workflows, they have significant distinctions, such as:
- ETL/ELT pipeline setup
- Data pre-processing (cleansing, annotation, transformation)
- Data protection and cybersecurity elaboration
- AI algorithm selection
- AI model training
- Development process review
- Software integrations and APIs creation
- UX/UI and data visualization setup
- Deployment to the production environment
- End-to-end testing
- Post-launch support, updates and modernization
AI-related technologies we use
Our developers’ expertise encompasses a wide array of technologies that we apply to forge high-performance solutions tailored to your requirements.
We deliver ML solutions powered by supervised, unsupervised, and reinforcement learning algorithms to process data, identify patterns or anomalies, and provide insights that will boost your decision-making.
Webnoria’s data scientists rely on the best tools and techniques to gather structured or unstructured data, ensure its reliability, and derive business-relevant knowledge that will drive large-scale decisions.
We help your organization to extract value from its big data assets and external sources by providing a full range of services covering data mapping, transformation, warehousing, processing, and visualization.
Webnoria develops AI-enhanced robotic process automation solutions that function as smart assistants with advanced cognitive capabilities to support employees in performing time-consuming activities.
Key areas of AI application
Infused with AI capabilities, Webnoria’s solutions can analyze visual content, mimic human communication, and dig into huge datasets to unearth valuable information.
- Computer vision
- Natural language processing
- Data mining
Our experts build AI-driven systems to process visual inputs and identify objects or persons with superior speed and accuracy.
Medical image analysis
We deliver software designed to examine radiological images, body scans, or other sources and enable more accurate diagnoses.
Inventory management
We develop retail-oriented solutions that scan barcodes, monitor what customers pick, and update inventory levels to help keep stocks replenished.
Video surveillance
We create solutions that can spot intruders or shoplifters in action based on their anomalous behavioral patterns, fostering security.
Visual inspection
We deploy computer vision tools to detect product defects or equipment failure, facilitating quality control and asset maintenance.
Itransition’s NLP solutions can extract knowledge from written and audio data and interact with humans in a wide range of scenarios.
Conversational AI
We configure chatbots and virtual assistants to provide 24/7 user support, remote patient care, or interactive elearning support
Sentiment analysis
We build AI platforms that scan online interactions to assess the user base’s attitude towards products, services, and brands.
Content moderation
We develop filtering systems to identify and remove toxic content from social media, including hate speech, fake news, and spam.
Cognitive automation
We help you implement intelligent RPA bots to automate a variety of time-consuming tasks, such as messaging or document processing.
Our team creates systems that sift through data to spot patterns or anomalies, facilitating decision-making with insights and forecasts.
Predictive analytics
We develop data analytics software capable of forecasting trends and outcomes to enable informed decisions while minimizing risk.
Recommendation engines
We deliver recommendation systems relying on AI algorithms to segment customers and target them with personalized suggestions.
Anomaly detection
We configure AI-based solutions that identify various types of outliers to predict fraud, malfunctions, health issues, and other risks.
Marketing analytics
We equip marketing service providers with AI tools to monitor user sentiment on social media, facilitate lead scoring, and drive sales.
AI use cases by industry
Here is a variety of industry-specific AI solutions designed to address the challenges and complexities of different operational scenarios.
- Retail
- Education
- Banking
- Automotive
- Transportation
- Automotive
- Healthcare
- Agriculture
- Manufacturing
- Real estate
- Demand prediction for inventory management
- Marketing optimization and lead conversion
- Social media analytics
- Customer relationship management
- Dynamic product pricing
- Personalized learning programs
- Identification of students at risk of dropout
- Virtual tutors and chatbots for elearning apps
- Real-time lesson translation and transcription via automatic speech recognition
- Virtual reality for professional training
- Portfolio and wealth management
- Financial risk management
- Stock market trend forecasts
- Cybersecurity and data privacy
- Loan processing automation
- Automatic plate recognition
- Voice-activated driver assistance
- Smart parking guidance and information systems
- ML-based route optimization for logistics
- ADAS for assisted driving
- IoT-based remote vehicle diagnostics
- Equipment predictive maintenance
- Quality control based on anomaly detection
- ADAS for assisted driving
- IoT-based remote vehicle diagnostics
- Equipment predictive maintenance
- Quality control based on anomaly detection
- AI-enhanced radiology and medical imaging
- NLP-powered medical assistants
- Syndrome prediction
- Patient data management automation
- ML-based crop management
- Weed and disease detection
- Fertilizer use optimization
- Selective breeding assistance
- Industrial asset predictive maintenance
- Automated product assembly lines
- Optimized supply chain via product tracking
- Computer vision-based quality assurance
- Asset value prediction and pricing
- Smart portfolio management
- AR/VR-enabled virtual tours
- KYC and lease contract processing
AI services delivery pipeline
After framing your needs and assessing your current tech ecosystem, we help you decide where and if it makes sense to invest in AI instead of conventional development. We also define your future solution’s functional and non-functional requirements.
We perform an exploratory analysis to map available data sources and assess their reliability. These can include corporate data assets owned by the customer and public databases.
Based on our technical and business evaluation, we select a suitable tech stack, design the product’s architecture, and define an implementation plan. We can also provide a PoC to ascertain the feasibility, economic viability, and potential limitations of the solution.
We carry out data pre-processing, which includes data cleansing, annotation and transformation. Then, we establish the solution’s evaluation criteria and train an AI model via supervised, unsupervised and reinforcement approaches. We can build an ensemble of AI and machine learning models to make sure we achieve the desired output.
We integrate the trained model into the software architecture to power any AI-based modules and functionalities with the model’s output. Then, we deploy the AI solution on-premise or in a cloud-based environment.
We provide ongoing support, maintenance, and regular updates according to DevOps best practices. This includes fine-tuning the accuracy of the AI model output by retraining the solution with new data sets.