Transform your business with Operational AI solutions

We integrate AI into your existing IT systems and business processes, using three layers: databases, AI models, and applications. We support organisations in areas such as Marketing, Sales, Customer Service, Finance, HR, and Performance Management.

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What AI solutions do we have at Innsyte?

Talk to your Data – ask questions in plain English and get instant database insights – no SQL skills required. This tool transforms data access, empowering faster, smarter decisions

AI-driven operational reporting enhancement – automatically generate operational reports with AI-driven insights in minutes – speeding up decisions and boosting report utilisation across teams

AI-powered customer service automation – deliver faster, more accurate customer support around the clock with AI-driven chatbots that understand context and speak multiple languages – improving service quality while cutting costs

How do we define an AI solution?

We believe that effective AI solutions rely on strong data management, advanced AI models, and user-friendly applications. Our approach integrates seamlessly into your existing IT systems, providing a complete end-to-end experience that ensures smooth deployment, agile performance, and measurable results. We achieve this through three key layers: a database, an AI model, and an application – each playing a vital role in delivering scalable AI solutions tailored to your business needs.

Database: strong data management for AI preparation

The database layer is the backbone of our AI solutions, managing structured and unstructured data through storage options like SQL, NoSQL (e.g., Cosmos DB), and public cloud storage. It ensures that all data – whether customer information, transaction history, or unstructured text- is clean, secure, and ready for AI processing.

Key features

Business impact

Data structuring and storage: capable of handling a mix of structured (e.g., relational databases) and unstructured data (e.g., JSON, XML) efficiently

Data quality management: Implements robust data cleansing, transformation, and validation processes to ensure high data integrity and reliability.

Scalability and security: built on scalable cloud infrastructures like Azure or Google Cloud, offering built-in data encryption, access control, and compliance with regulations like GDPR.

Data integration: Seamless integration with existing data pipelines, third-party APIs, and legacy systems to consolidate data sources and enable real-time data flow.

Provides a single source of truth for analytics and decision-making.

Ensures data readiness for AI model training and application

Reduces data silos and enhances cross-departmental data collaboration.

The AI model layer uses advanced models, including large language models and other ML algorithms, to turn raw data into valuable insights and automated decisions. Built on dedicated infrastructure, it powers tasks like generative AI, natural language processing (NLP), predictive analytics, and more.

Key features

Business impact

Generative AI and NLP: leverages models like GPT-4 to generate human-like text, automate content creation, and provide intelligent responses in chatbots and virtual assistants,

Predictive analytics: employs machine learning algorithms to predict future trends, customer behaviours, and operational outcomes, aiding strategic decision-making,

Deep learning capabilities: Uses deep learning techniques for complex tasks like image recognition, anomaly detection, and personalised recommendations,

Custom model development: tailors AI models to specific business needs, whether through fine-tuning existing models or developing bespoke algorithms from scratch.

Model optimization and monitoring: Continuously monitors model performance to ensure accuracy, relevance, and alignment with business goals. Automated retraining mechanisms can be implemented to adapt models to new data or changing conditions

Enhances decision-making processes by providing actionable insights and predictions,

Automates repetitive and time-consuming tasks, freeing up human resources for more strategic activities,

Improves customer experience through personalised interactions and recommendations.

AI model: driving intelligence with advanced AI and machine learning

Application: deploying user-centric, AI-driven software

The application layer delivers AI-driven features directly to end-users through methods like serverless computing, front-end interfaces, reports and containerised environments. It transforms AI capabilities into practical applications such as intelligent chatbots, virtual assistants, dynamic dashboards, and automated reporting systems.

Key features

Business impact

Serverless computing: utilises serverless architectures (e.g., Azure Functions) to scale applications dynamically based on demand, ensuring cost efficiency and rapid deployment,

Containerisation and micro-services: deploys AI applications in containers (e.g., Docker) and micro-services architectures for portability, scalability, and resilience,

Front-end development: builds intuitive, interactive user interfaces (UIs) using frameworks like React, Angular, or Vue.js, tailored to end-user workflows and needs,

Integration with business systems: ensures seamless integration with existing business applications such as CRM systems (e.g., Salesforce), ERP systems (e.g., SAP), and BI tools (e.g., Power BI) for a unified experience,

Continuous improvement: supports agile development and iterative enhancements based on user feedback, ensuring the application evolves with changing business requirements

Delivers intuitive, AI-powered tools that enhance productivity and user engagement,

Reduces the time to market for deploying new AI features or updates,

Enhances operational efficiency by automating workflows and processes across departments.

How do AI solutions work at Innsyte?

Our AI solutions are designed to be “plug and play,” seamlessly integrating into your existing IT environment with minimal setup and no major infrastructure changes. Built to be cross-industry, they are versatile and effective across sectors like e-commerce, retail, finance, and healthcare. We focus on Operational AI, targeting key business functions to drive value, efficiency, and innovation.

Leveraging the power of public cloud – including SQL databases, OpenAI endpoints, and serverless computing – our solutions operate across three key layers: data management, AI models, and application deployment. This enables businesses to integrate sophisticated AI capabilities without extensive technical expertise or resource allocation.

Marketing operations – automated content generation tools help marketing teams create compelling, data-driven campaigns across social media and other channels

Sales operations – natural language SQL enables easy access to sales data, allowing teams to generate insights without technical SQL knowledge

Customer service operations – AI-powered chatbots and ticketing systems provide automated, context-aware responses to customer inquiries, improving response times and satisfaction.

Explore our detailed use cases to see how businesses have successfully implemented our AI solutions to transform their operations. From enhancing marketing and streamlining sales processes to revolutionising customer service and financial planning, our solutions deliver measurable business impact here.

Tell us about the challenge that you want to resolve.

We tailor most of our AI solutions to meet the unique needs of each client. Share your company’s situation with us, and we’ll let you know how we can help.

Free consultation

FAQ – most frequent questions to AI solutions

What types of businesses can benefit from Innsyte’s AI solutions?

Our AI solutions are designed to be cross-industry and adaptable for various business sizes, from mid-sized enterprises to large corporations. We specialise in sectors such as e-commerce, retail, banking, manufacturing, and healthcare, among others. If your organization is looking to automate operations, enhance customer experience, or leverage data for strategic decision-making, our solutions are a perfect fit.

How do Innsyte’s AI solutions integrate with our existing IT infrastructure?

Our AI solutions are designed as “plug-and-play,” meaning they can be seamlessly integrated into your existing IT landscape with minimal disruption. We use technologies in public cloud providers. In Azure, we primarily levearge Azure SQL DB, Cosmos DB, Azure Functions, Azure App Service, Azure OpenAI. On GCP we use Cloud SQL Server, Cloud Firestore, Cloud Functions, Scalable Web Application, Vertex AI, ensuring compatibility with your current systems and workflows.

What is the typical timeline for deploying an AI solution?

How do you ensure the security and privacy of our data during and after the integration of AI solutions?

How do you measure the success and ROI of an AI solution?

What is the process to get started with Innsyte’s AI solutions?

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