Applied AI
Harness the power of your data with AI, deliver innovative AI-powered solutions for real-world challenges.
Is artificial intelligence (AI) smoothly integrated into your organization’s culture and operations? Are you using the right AI technologies and tools for your specific needs? Do you have the in-house expertise and resources to build robust AI models? How do you address security and governance regarding your AI initiatives?
Successfully harnessing AI to solve the toughest business and technical challenges requires addressing these important questions. As an AI consulting firm, Dataiso partners with you to confidently answer them. We deliver sustainable AI solutions that drive actionable value and unlock new opportunities for greater innovation and growth.
See how Dataiso’s AI capabilities accelerate AI-driven initiatives.
Your challenges
Your challenges
Artificial intelligence (AI) is a set of different theories, methods, and techniques allowing machines to simulate behaviours close to those of a human. As a major area of computer science, it encompasses various innovative subfields: machine learning (ML), natural language processing (NLP), generative AI (GenAI), computer vision (CV), and robotics.
Today, AI is transforming organizations across industries of all sizes, reshaping traditional processes. While its potential is undeniable, AI also brings various challenges at all organizational levels.
At Dataiso, we have a deep understanding of the unique challenges organizations face when integrating AI into their operations to achieve success.
AI's potential impact not fully grasped
Organizations struggle to unlock AI's transformative potential due to AI knowledge gaps and implementation hurdles. Successful integration requires strategic leadership and prioritising impactful AI projects
Lack of confidence in AI
Knowledge gaps hinder AI adoption. Many organizations face challenges trusting and effectively using AI due to a lack of understanding of technologies, pressuring AI teams.
Poorly managed data
Poor data management practices prevent unlocking AI's full potential. Data silos and quality issues isolate organizations, hindering their ability to leverage AI for business value.
Weak security and data protection practices
AI relies on vast data, creating security and data protection risks. Inadequate safeguards often lead to data breaches and privacy violations, undermining trust in AI.
High implementation costs, mediocre return on investment (ROI)
Rushed AI projects often result in poorly designed solutions as many organizations prioritize immediate results. Consequently, it leads to high implementation costs for mediocre outcomes.
Our key factors of success
Our key factors of success
Winning organizations make informed decisions by understanding the strategic value of AI. At Dataiso, effective AI initiatives are most often built through crucial key factors for success.
The journey of AI begins with its literacy. To achieve this, organizations must acquire a good AI culture to succeed in their AI transformation. While building AI knowledge takes time, it can lead to higher returns and better risk management.
Organizations should involve AI-savvy teams to manage AI expenses and initiatives effectively. Evaluation should be done for each use case based on data availability, operational constraints, change management, computing resources, and security.
AI offers vast opportunities but requires responsible use. Decision-makers must prioritise ethical principles like fairness, reliability, confidentiality, security, transparency, and accountability to ensure AI benefits people and protects data.
Acquiring AI skills is essential for AI project success. However, there is a shortage of qualified AI experts. Organizations should build AI-ready teams through targeted talent acquisition, training, and continuous development programs to face this.
With the rapid evolution of data, models, and algorithms, managing the AI lifecycle is easier with regular monitoring. To adapt to changing conditions and needs, organizations should adopt continuous monitoring for optimal performance and quality.
Effective AI models rely on data quality. Organizations must embrace data quality in their AI strategies. Strong data management methods like data cleansing, enrichment, and validation are vital for maximising value and trust from AI solutions.
Different AI techniques like classification, regression, segmentation, clustering, and recommendation systems offer unique solutions. To maximize AI success, organisations must evaluate their needs and data to choose the right one for each use case.
Our approach
Our approach
At Dataiso, we enable organizations to better navigate their digital transformation through a combination of innovative AI solutions and industry-proven methodologies.
Our data-centric approach is built upon a People-Process-Technology (PPT) framework, ensuring efficient project delivery while fostering collaboration between all aspects of the transformation. We leverage this collaborative model to help you drive successful AI initiatives.
Our services
Our services
Dataiso provides cutting-edge AI services to help organisations achieve real-world results. We go beyond theoretical methods, delivering bespoke solutions that address your specific challenges and unlock new opportunities.
AI strategy and roadmap
- Maximize return on investment (ROI) by aligning AI objectives with the overall strategy.
- Drive growth by identifying high-impact opportunities where AI can make a significant difference.
- Develop a comprehensive AI roadmap for successful implementation strategies.
- Define the appropriate AI technologies and tools to meet unique business needs and drive innovation goals.
- Ensure smooth AI adoption through effective change management and stakeholder engagement.
- Strengthen AI scaling strategies by implementing AIOps principles.
- Showcase AI value through compelling proofs of concept (PoCs) and proofs of value (PoVs).
AI audit and diagnosis
- Assess all existing AI practices, policies, and technologies.
- Identify gaps between the organization’s current state and AI best practices, including both technical and functional discrepancies.
- Assess AI health and observability, including models, pipelines, and data quality, consistency, and accessibility.
- Evaluate AI systems’ strengths and weaknesses using methods like explainable AI (XAI) and interpretability (IAI).
- Review AI ethics, sustainability, security, privacy, and compliance.
- Benchmark AI maturity against industry standards with proven maturity models.
- Maximize AI investments through efficient optimisation plans.
AI architecture and deployment
- Design tailored AI architectures to meet requirements, such as centralized AI, decentralized AI, and hybrid AI.
- Integrate best-in-class AI algorithms, languages, and tools, including for multimodal AI.
- Ensure seamless AI deployment on cloud platforms, on-premises infrastructure, or hybrid environments.
- Optimize AI infrastructure through smarter performance tuning techniques and efficient resource allocation.
- Strengthen AI security and governance through proactive measures, including data protection and privacy best practices.
- Streamline and scale deployments with AI for IT operations (AIOps) practices, including large language model operations (LLMOps) for AI model scalability.
Natural language processing (NLP), natural language understanding (NLU), and voice/speech recognition
- Enhance user experiences through natural language understanding (NLU) and natural language processing (NLP).
- Improve operational efficiency with automated text and speech processing, including sentiment analysis and named entity recognition.
- Gain valuable insights from textual and speech data by leveraging advanced analytics.
- Develop innovative AI-powered applications, such as chatbots, virtual assistants, voice assistants, and text analytics tools.
- Enable multilingual communication using advanced machine translation (MT).
- Create immersive experiences with innovative technologies like speech-to-text (STT), text-to-speech (TTS), and voice search, fueled by retrieval-augmented generation (RAG).
Generative AI (GenAI)
- Create hyper-realistic content using generative adversarial networks (GANs) or variational autoencoders (VAEs).
- Accelerate product design, development, and delivery through AI-driven generative design and automated prototyping.
- Enhance user experience with personalized content generated by advanced models like large language models (LLMs) and generative pre-trained transformers (GPTs).
- Explore new opportunities with GenAI-powered product innovation, leveraging the strengths of recurrent neural networks (RNNs) for sequential data processing and retrieval-augmented generation (RAG) for boosted contextual understanding and accuracy.
- Mitigate ethical concerns and biases in GenAI-generated content through robust auditing and diverse training datasets.
- Foster responsible AI practices in GenAI projects across the organization, ensuring transparency and accountability.
Computer vision (CV)
- Automate image and video analytics using deep learning methods like convolutional neural networks (CNNs), support vector machines (SVMs), and object detection.
- Gain actionable insights from visual data through image classification and clustering.
- Develop cutting-edge computer vision solutions, such as augmented reality or image recognition for real-world applications.
- Optimize business and IT operations with innovative computer vision-based quality control and inspection solutions.
- Identify potential threats through effective anomaly detection methods like behavioral analytics.
- Protect assets with robust facial recognition and object tracking for enhanced security and safety.
Expert systems (ES)
- Enhance operational accuracy and consistency with innovative knowledge graphs, rule-based systems, and fuzzy logic.
- Automate routine tasks and free up human resources for higher-value activities.
- Reduce errors and biases by leveraging probabilistic reasoning and uncertainty quantification.
- Enhance user experience with personalized advice using recommendation systems and decision support systems.
- Unlock new insights and opportunities with advanced expert systems using innovative cognitive architectures and human-computer interaction.
- Improve return on investment (ROI) with cutting-edge expert system solutions.
Robotics
- Increase productivity and efficiency through robotic process automation (RPA) and collaborative robots (cobots) solutions.
- Improve product quality and consistency with robotic precision systems.
- Make informed decisions with advanced perception systems, such as high-performance robot vision and sensors.
- Enable advanced cartographies with robotic mapping technologies like SLAM (Simultaneous Localisation And Mapping).
- Explore new automation opportunities with artificial intelligence (AI) for robotics through advanced robotics like autonomous mobile robots (AMRs).
- Gain a competitive edge by leveraging cutting-edge robotics solutions.
AI analytics
- Discover hidden patterns and trends in data using machine learning (ML) algorithms (e.g., clustering, association rule mining).
- Optimize data assets with predictive analytics and forecasting models.
- Incorporate prescriptive analytics and advanced optimisation techniques for stronger operational efficiency across the organisation.
- Elevate the overall AI experience by delivering personalised AI-driven analytics solutions.
- Facilitate key stakeholders’ understanding of complex data through AI-augmented interactive dashboards and reports.
- Gain a competitive advantage with analytics-driven innovation fueled by data intelligence for agile response to market change.
AI security, governance, and sustainability
- Safeguard AI landscape with efficient security measures (e.g., AI data classification, access controls) based on industry standards like ISO 27001.
- Maintain transparency, accountability, and compliance with regulations like AI Act, Data Act, GDPR, and CCPA through future-proof AI governance.
- Optimize AI sustainability through energy-efficient models and responsible AI practices.
- Uphold fairness, explainability, and privacy by addressing AI ethics and bias throughout the AI lifecycle.
- Enhance AI monitoring and preventive methods through proactive AI observability.
- Integrate AI governance with overall data governance frameworks and best practices, including comprehensive policies and procedures.
Our benefits
Our benefits
- Tailored AI solutions for specific challenges.
- Transformative business and technical results.
- Sustainable AI growth and innovation.
The constant presence of AI across industries positions it as a key driver of productivity, growth, and innovation. Yet, despite decades of progress, substantial challenges persist.
At Dataiso, we helps organizations leverage the power of AI to achieve greater outcomes. Collaborate with us to build your AI-powered future.