AI Development Services
The typical problem with most AI projects is that they either: The model doesn't get to production. The model ships, but isn't integrated with the operational systems business runs. Or the model makes it but after 6 months it's met some end of life because there was no plan for after deployment. We build to prevent all three, and this shows in our services.
Custom AI Software Development
We develop AI software to fit your business rules, data and users. We do everything from design and model development to deployment and integration and deliver systems that are documented well enough for your team to take over once we're gone. This is where most of our enterprise clients begin.
Generative AI Application Development
We develop enterprise generative AI applications for text, image, audio and multimodal models. The projects involve model selection (GPT, Claude, Gemini, or open source), prompt engineering, RAG workflows for grounding, and orchestration to turn a model into a product. We also use this approach to build our own content generation tool, WriteWise AI.
Conversational AI: Chatbots, Voice Assistants, and AI Agents
We build and deliver AI chatbots, virtual assistants and AI agents to provide customer support, lead generation, internal automation, and multi-step task management. Our chatbots retain context, can be integrated with CRM and other systems, and learn and improve over time. For more information on this service, please refer to our AI Chatbot Development page.
NLP and Language Intelligence
We create natural language processing systems that comprehend, categorise, and produce human language. The applications include sentiment analysis, entity recognition, document classification, semantic search, intent extraction, and summarization. We use fine-tuned Large Language Models (LLMs) and NLP pipelines depending on the use case, accuracy needed and budget.
Computer Vision and OCR
We develop computer vision solutions to process images and video in real time such as object detection and recognition, face recognition, image segmentation and OCR to process documents. Use cases include manufacturing quality control, banking identity recognition and visual search for retail.
Predictive Analytics and Forecasting
We create predictive models that take data from the past and present and make predictions that drive business action. We have delivered predictive platforms for sales, demand, churn, fraud and other operations. An example of these is Scowtt, an AI predictive sales and marketing solution we built.
AI for CRMs, ERPs and Legacy Systems
Enterprises don't need another AI platform. They need AI capabilities in the systems they already use. We embed AI models into Salesforce, HubSpot, SAP, Oracle, custom-built CRMs and legacy systems, so the insights are available in the systems your team already uses.
MLOps and Model Lifecycle Management
We don't just build and fit models. We also work on the infrastructure that will keep the models healthy in production, such as monitoring, drift detection, retraining, version control and evaluation. This is what determines success or failure of your AI investment.
The above services are how we work. The list below is what we've delivered during those engagements, broken down by problem type rather than solution, so you can find a match for your use case.
Decision Support and Forecasting
Predictive analytics for business intelligence enables organisations to shift from descriptive to predictive, helping them plan for the future in sales forecasting, demand planning and other operational decisions. Anomaly detection systems identify anomalies in financial transactions, medical records and network traffic, detecting fraudulent behaviour, equipment malfunctions and cybersecurity threats. Collaborative filtering and recommendation engines provide tailored recommendations for products, services and content based on user activity and context.
Visual and Spatial Intelligence
Computer vision and object detection systems process video streams in real time for applications such as smart video surveillance, traffic monitoring and management, quality control, and automation. Face recognition and identity verification systems enable authentication, access control and security processes in banking, healthcare and retail. Human activity recognition systems read human posture and movement to detect falls, track health and fitness, and monitor patients and safety.
Language and Document Work
Document understanding and automation systems extract information from invoices and contracts, forms and unstructured documents, extracting structured data from documents that would otherwise be reviewed by humans. Semantic search engines know what you mean instead of simply what you say, making corporate knowledge bases searchable. Content classification, sentiment analysis and spam filtering tools execute across customer communications, reviews and support tickets to clean up the inbox and highlight what is important.
Voice and Audio Applications
Voice recognition and audio transcription systems turn audio into searchable text for voice queries, call mining and meeting transcription, and accessibility. Intent recognition systems understand user needs in voice or written queries and messages, and automatically direct them to the appropriate workflow.
Data Preparation and Annotation
Image annotation and data labeling services generate datasets your models can use to perform optimally, with custom labeling for computer vision, medical imaging, autonomous vehicles and any other application where existing datasets are insufficient. Data mining and pattern recognition on structured and unstructured data help find signals in large datasets that aren't apparent with traditional analytics tools.
Rapid Prototyping
Our MVP development services help you to build rapid prototypes for products from ideas to demos with a combination of pre-built models, transfer learning and minimal infrastructure. This is our approach when a team is looking to test an idea before building it.
Let’s turn it into a scalable, AI-powered product that delivers real results
Get Your AI MVP ReadyIt's not just code that leads to useful AI apps. They come from getting the strategy, data, and deployment right. All of our AI projects go through eight steps, in the same sequence, because they build on each other.
Discovery and Strategic Alignment
We begin with a comprehensive understanding of your objectives, challenges and data landscape. Our AI experts collaborate with your team to establish KPIs, prioritise relevant AI use cases, and ensure a business-focused approach. It's here that most issues are identified, before they become costly reworks.
Data Landscape Audit and Preparation
Good AI models are fed good data. We review your data assets, locate deficiencies, clean up the mess and engineer what's missing so the model can learn from it. On most first-time AI projects, this phase takes longer than people expect, but it saves a lot of time in the long run.
Intelligent Architecture Design
Then, we design the system architecture before training models. It's a trade-off between the accuracy of the algorithms and the desired performance, its technical realities, and the money. These decisions determine whether the project goes from prototype to production or it's shelved because there's something wrong with the foundation.
Model Development and Experimentation
We create, experiment, and refine model candidates following best practices and using the latest ML tools. We measure accuracy, fairness (if this is an issue) and speed, and we not only ensure the model works, but also beats the baseline you may have. Here's where we build the smarts.
Real-World Simulation and Evaluation
Models can perform well on test data, but fail in practice. We push ours through real-world scenarios, simulated settings and corner cases that are important to your business. We want to find out how it's going to fail before your customers do.
Scalable Deployment and System Integration
Whether your deployment is in the cloud, data center, or edge, we integrate our tools into your processes without replacing what you already have. This means an AI solution that integrates with your processes, rather than you having to rework your processes to accommodate an AI solution.
Continuous Monitoring and Learning Loop
AI is not a "set and forget" tool. So, we set up monitoring processes that help us track model drift, use and feedback mechanisms to ensure that the system continues to improve as your business and the data change.
Lifecycle Management and Optimization
Once it's in, we continue to work with you on audits, retraining and upgrades. This ensures your AI system is fresh, safe, and profitable as things change, rather than it quietly becoming obsolete.
Our experience spans both traditional machine learning (ML) and the latest generative AI, having built software for a decade and delivered AI for 5+ years. Here's what our team does each day.
Machine Learning
We build, test and deploy supervised, unsupervised, semi-supervised and reinforcement learning systems for prediction, classification, clustering and dynamic decision making. The applications include churn and demand prediction, item recommendation, fraud prevention, and pricing. We test all models with cross-validation, precision and recall, ROC-AUC and stress testing.
Deep Learning
We develop deep neural network solutions such as CNNs for image analysis, RNNs and transformers for sequential modelling, and GANs for generative tasks. We prefer TensorFlow, Keras, PyTorch, MXNet, and ONNX for projects when the focus is on fast prototyping, research, or portability.
Natural Language Processing and LLMs
Our NLP engineers have experience working with the foundation models GPT, Claude, Gemini and Mistral, as well as open-source LLMs such as Llama and Mistral variants. Use cases include text summarisation, question answering, named entity recognition, semantic search and conversational AI with tool use and multi-step reasoning.
Computer Vision
We develop vision systems for object detection, segmentation, OCR and video analysis using OpenCV, YOLO, ResNet, and EfficientNet. Our solutions are used for automation, safety, medical imaging, and visual inspection in manufacturing, retail and medical systems.
Speech and Audio Processing
Our experts develop text-to-speech, speech-to-text, and speaker recognition technologies using Whisper, Wav2Vec, Google Speech API, and Amazon Polly. These are used to build voice-enabled apps, IVR deflection for phone calls, accessibility software, and audio analytics for call centers.
AI Agents and Copilots
We build AI agents and copilots that help users, automate complex sequences of tasks, and learn from the user. Our research includes tool calling, function calling, memory management and orchestration layer that transforms a language model into a tool.
Edge AI
We bring AI models to mobile devices for on-device decision-making in low connectivity or low latency environments. Our work includes model pruning, quantization and fine-tuning for mobile, IoT and embedded devices.
Explainable AI and Privacy-Conscious AI
For clients in regulated industries, our emphasis is on model interpretability, federated learning and privacy-preserving training methods. This makes AI systems auditable and ready for compliance with regulations such as GDPR, HIPAA and SOC 2.
Multi-Modal AI
We develop systems that combine text, image and/or audio into an AI system, useful for contextual search, intelligent personalization and enhanced user experiences. Multi-modal AI is one of our fastest-growing areas of work, and the solutions we're delivering now are very different from just two years ago.
Scowtt: AI Predictive Sales and Marketing Platform
In mid-2024, the Scowtt founders approached us with a problem that plagues most sales and marketing teams. They had a lot of data in their CRMs, but could not figure out which leads to focus on, or what paid campaigns were generating revenue. They could do better, they thought, with predictive intelligence on first-party data, rather than sharing their customers' data with third-party AI providers.
We built an MVP of Scowtt in four months as their full stack product and engineering team. The tech stack is Next.js, Node.js and serverless on Google Cloud and AWS Lambda, with predictive power provided by in-house developed ML models.
Now Scowtt is a live enterprise product delivering increased lead quality and ROAS, and the product's readiness played a role in raising $12M in Series A financing.
Ceiy: Financial Education Meets Mobile Smarts
When Ceiy approached us with their vision, it was both straightforward and technologically challenging. They wanted to create an experience for financial literacy that was as addictive as Duolingo, targeted to students of colour, with culturally relevant content and personalised learning journeys provided by AI. The trick would be to merge fintech tools with edtech learning in a mobile, scalable app.
We developed the app in Kotlin and Swift, with a backend on AWS and MySQL. The AI component includes learning journeys that adapt to the user, real-time financial simulations such as budget and loan simulations, and a smart learning engine that tailors content to the students' progress. We've created an engaging financial literacy app that's not like a textbook, now used as a generational movement.
SeekrCareers: AI Resume Builder
When two industry experts from the UK came to us with the vision of creating a resume builder as simple as online form, we built it as an AI-development project. The app is built with PHP, MySQL, AWS, and JavaScript; it provides AI-powered resume builder that recommends content as you type, ATS-friendly resume templates and job recommendations based on user interests and profile.
SeekrCareers is now a career advisor for job candidates, offering smart resume downloads in various formats and infrastructure that can scale through thousands of users seamlessly.
WriteWise AI: Content Creation at Scale
WriteWise AI is our proprietary AI content creation tool created to address a common problem we encountered while working with clients. It's costly to generate high-quality, timely, consistent content at scale and agencies were spending a fortune for little return. So we built the tool we needed.
WriteWise AI uses GPT language models to generate natural-sounding content, NLP and machine learning to personalise and match the tone, image generation APIs to create matching images and content marketing tooling to ensure the content is search engine optimised.
Users choose the tone, the format, and receive a write-up for their channel, be it an article, LinkedIn post, or Instagram caption. WriteWise AI is in beta and growing in popularity as an alternative to outsourcing content creation.
Client's Appreciations
Our AI development services give you instant access to the top #1% of India's skilled AI developers, data scientists, and AI architects. You get expert AI talent ready to build impactful solutions, from automating repetitive tasks to solving complex problems with no hidden costs and long-term lock-ins.
AI Developer Team
For Startups & businesses exploring AI solutions
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AI Team Plus
For Scaling AI projects with more resources
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AI Growth Team
For Advanced AI product development & scaling
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For custom enterprise engagements or to hire 4+ AI experts, reach out at [email protected] or schedule a discovery call.
We build AI solutions for different sectors, and the work we do in each is driven by the needs of the industry. Here are the areas where we're delivering AI development.
Healthcare
We develop AI systems to enhance patient health without crossing the regulatory line required for every healthcare project. The projects include predictive diagnostics to detect disease early, image analysis in radiology and pathology, virtual health coaches for appointment scheduling and triage, and treatment recommendation systems that use patient history. All systems we build for our clients are HIPAA compliant by default, with audit logging, encryption and PII management baked into the system rather than added at the last minute.
Insurance
With insurance firms, we're focused on the AI that accelerates claims processing and improves risk assessment. Virtual assistants answer policy questions and streamline onboarding 24/7, claims processing software reads through documents and assigns matters in minutes instead of days, and risk scoring algorithms blend historical and real-time data to detect fraud, forecast churn and set premiums. The outcome is quicker claims for the customer and profitable policies for underwriting.
Retail and eCommerce
We power the AI engine that makes websites more personal than "one size fits all". Recommender systems make product suggestions based on psychology and intent, sentiment analysis understands what customers think through their online reviews and support emails, and stock forecasting makes stock decisions less of a guessing game. Recommendation engines get the right message to the right customer at the right time, which causes the conversion bump.
Fintech
In fintech, we concentrate on AI that detects exceptions and unlocks opportunities. Fraud detection models catch suspicious transactions before they happen, credit scoring models assess borrowers with alternative data sources to credit thin file borrowers, and financial product matching engines recommend the right savings account, loan or investment product for the consumer based on their behaviour. Explainability and compliance are designed for because regulators demand it.
Logistics and Supply Chain
We turn logistics from Reactive to Proactive. Dynamic route planning considers traffic, weather, fuel costs, and time windows to create better route plans than dispatch software can. Delivery prediction models predict which deliveries will be late so customer service can proactively defuse complaints, and demand forecasting with AI ensures warehouses are stocked without tying up too much capital. Fleet management software predicts when vehicles will need maintenance to prevent breakdowns.
Real Estate
In real estate, we create AI solutions that lead-qualify in a hurry and make closing deals easier. Lead-qualifying chatbots answer preliminary questions 24/7, understand buyer preferences, and schedule real estate viewings into agent's calendars automatically. And computer vision systems automatically identify features of properties from photos and floor plans, while document authentication automation streamlines the tedious aspects of closing deals, such as ID and title verification.
Education and EdTech
We create AI that works with students' natural learning styles instead of the one-size-fits-all approach. AI tutors explain concepts in simple terms, explain problems step by step, and know when someone is confused or just bored. Personalised learning paths adapt to how well students are doing, and for campuses with international student populations, we create multilingual tutors that automatically translate mid-conversation.
Media and Entertainment
For media companies, we help you make more with less effort. We generate articles, video scripts, video captions and social media posts in your company voice, with editorial features to keep everything on-brand. Video tagging automatically classifies scenes for searchable video libraries, which is critical for broadcasters with huge archives, and content recommendations personalize content for viewers likely to watch it, which is key to subscriber retention on streaming services.
Manufacturing
In manufacturing, we put AI on the shop floor to minimise downtime and increase quality. Predictive maintenance systems learn from sensor data to detect potential equipment failures, computer vision quality control systems spot defects more consistently and quickly than human inspectors, and production line optimization systems find bottlenecks and suggest changes to increase production without additional investment.
To develop AI models that stand up to production, we need a toolkit of the right tools, selected for your business needs not the latest fads. Here's what powers our AI development.
Machine Learning Algorithms
We use the entire spectrum of machine learning methods, and select methods based on your data and the complexity of the problem. Supervised learning is used for predictive analytics such as fraud detection, churn, and sales forecasting with regression, decision trees, random forests, and gradient boosting. Unsupervised learning includes pattern mining and segmentation algorithms like K-Means, DBSCAN, PCA and t-SNE. Reinforcement learning handles feedback-based learning, such as recommender systems with Q-learning, DQN, and PPO, while semi-supervised learning makes up for the lack of labels in data. We test all models with cross-validation, precision-recall curves, ROC-AUC scores, and live testing before they go out the door.
Deep Learning Frameworks
When it comes to data-intensive applications such as computer vision, NLP and time-series analysis, we choose the right framework for the job. We use TensorFlow and Keras for prototyping and production, PyTorch for research and customised neural network models, and MXNet and ONNX for high-performance models needing to run on the cloud, mobile and edge devices. It's a matter of whether the project needs to get to market fast, be flexible for experimentation, or be deployed across platforms.
AI Libraries, SDKs and Modules
We leverage established open-source libraries to build quickly without compromises. Scikit-learn to support machine-learning algorithms for traditional workflows and rapid prototyping, Hugging Face Transformers to support modern NLP tasks such as BERT, GPT, and Llama models, and OpenCV to support computer vision tasks such as object detection or real-time video analysis. For natural language processing, we use spaCy and NLTK, and for data manipulation and plotting, we use Pandas, NumPy and Matplotlib. These libraries speed up development without vendor lock-in.
Cloud AI Platforms
We develop cloud-based AI systems that grow your business, choosing the platform based on your infrastructure and regulatory requirements. We use AWS SageMaker for managing the entire ML lifecycle, Azure Machine Learning for CI/CD pipelines for model development and enterprise integration, and Google Vertex AI for access to Google's foundation models and automated ML. We also use IBM Watson and Oracle AI for enterprise compliance. All our cloud solutions provide hybrid and on-premise deployment through Docker and Kubernetes to support data sovereignty and compliance.
Data Engineering Tools and Pipelines
Good data pipelines are critical to whether AI systems operate well in production or not. We approach the data layer as carefully as the model itself, using Apache Spark for parallel processing of large data sets, Apache Airflow to schedule and orchestrate workflows, dbt for data transformation inside data warehouses and Kafka or RabbitMQ for real-time event streams. This ensures your data is clean, well-organised and predictable, which is essential for production AI.
Programming Languages
We adopt a polyglot approach to use the right language for the right job. We use Python for the bulk of our AI development including ML, deep learning and NLP. R is used to handle statistical work and exploratory data analysis in research projects. Julia takes the place of Python when speed is a constraint, while C++ and Java are used for enterprise integration, edge devices, and memory-constrained and low-latency applications. This is a stack that works at each layer, not one where C++ or Python does what it isn't good at.
MLOps and Model Lifecycle Automation
We approach AI like the product software it is with operational tooling to keep our AI working in production. MLflow provides lifecycle management for experiments and model versions, Kubeflow orchestrates workflows for Kubernetes-based deployments, DVC provides data versioning to ensure the model produces the same results, and Docker and Helm provide the containerization and automation for deployments. MLOps, with CI/CD, monitoring, and retraining, keeps AI from decaying and becoming obsolete.
Pre-Trained Models and Transfer Learning
We leverage pre-trained foundation models to speed up development and boost performance, rather than starting from square one. For NLP we use GPT, BERT and T5 for summarization, classification and question answering. YOLO, ResNet and EfficientNet are used for computer vision tasks such as object detection and classification. We use Whisper and Wav2Vec for speech tasks like transcriptions and creating voice interfaces. Using these models for transfer learning saves time, money and improves accuracy in use cases where you don't have huge private datasets.
1000+
Apps & Web Apps
500+
Clients Served
10
Awards and Recognitions
50+
Developers
100+
5-Star Client Reviews
$10M
Revenue Generated by our Apps
15+
Years of Experience in Developing Tech Solutions
The success of your AI project often boils down to the AI development company you choose as a partner - whether your project gets off the ground, scales up, and actually moves the dial for business outcomes, or whether it becomes one of the "proof of concepts" that sits in the corner and never gets past experimentation. We're technically deep, strategically astute, and business-focused.
A True AI Partnership
We are the one-stop-shop for AI development, including conceptualization, strategy, model deployment and post-deployment optimisation. Our team will work with you to develop a proof of concept, build a full-scale AI system, or even scale an existing system. This ensures technical excellence and business fit throughout the project lifecycle, not just at the design stage.
Engineering with Purpose and Precision
We don't just use a standard suite of algorithms, data pipelines or model architectures for every project, we build around your problem. We prioritise accuracy, speed, and scalability to ensure the solution works in the real world and not just in theory. The solution is built for the environment in which it will be deployed, which is crucial for building AI that works and scales, rather than fails.
Dedicated R&D-Led Innovation
Our AI research team is constantly experimenting with new techniques, bespoke model architectures, and training strategies. So, whether the project requires fine-tuning transformer models, developing AI workflows for a domain, or exploring multi-agent systems, we bring a university-style approach to the table. This expertise is reflected in the production code we deliver, not just in research talks or product pages.
Security-First, Compliance-Ready
When it comes to AI, trust begins with data. We employ rigorous data privacy, secure model training, and ethical AI use policies, in line with compliance frameworks like GDPR, HIPAA, and SOC 2. We provide anonymized data sets, explainable AI model predictions, and audit-ready documentation as part of the development process and not as fixes applied just before compliance audits. For our clients in regulated sectors, this is what determines if the project moves out of staging.
Industry-Proven AI Experts
We have senior AI architects, ML engineers, data scientists and domain experts who have deep experience in healthcare, fintech, retail, logistics and media. Having delivered hundreds of AI solutions, we know how to develop AI and how to make it fit the industry realities, including regulatory, data, and customer requirements that drive production AI.
Results You Can Measure
We design systems with return on investment (ROI) in mind. Cutting unnecessary manual labor, increasing the accuracy of their predictions, and enabling data-driven business opportunities through automation. Our clients report improved efficiency, customer satisfaction and better decision making on systems we have delivered. We'll let the results speak for themselves rather than fall back on case study speak with no numbers to support it.
Tool-Agnostic and Business-Centric
We're not platform agnostic or vendor neutral. We choose the right tool for the job, whether that's open-source or enterprise software, or even cloud-based AI services. The architecture is not about a vendor's business model, but your infrastructure, scalability, and plans. So we choose a technology for the task at hand without vendor locks and hidden agendas.
Customer-Focused Process and Support
We also work with you to monitor, retrain and refine your models, gather feedback from users to inform development and maintain your systems over time through retainer-based support. We are your technology partner rather than just a vendor - helping you build and refine your AI strategy and capabilities as your business evolves, and as the production environment uncovers insights not anticipated in the original design.
Ready to build something intelligent, ethical, and future-proof? End your search here with a trusted AI development partner.
AI vendors generally fall into two categories: staffing agencies that throw temps at your project, or small firms with limited scalability and capability. We're a hybrid with plug-and-play AI development teams, provide flexible engagements, domain expertise and experience with LLMs, NLP, computer vision, and ML model deployment.
Yes. We work on custom AI development for specific applications, rather than shoehorning your problem into a template. Our projects include route planning for logistics, fraud protection for fintech, chatbots for ecommerce, predictive sales for B2B SaaS, and many other custom-built systems we've delivered. During discovery we identify the right architecture and model to use so you don't over-pay for technology you don't need.
Absolutely. We've worked with several founders and startups to build AI-powered MVPs with scalable architecture, transfer learning from existing models, and small engineering teams to minimise costs. The time to build the MVP is 4-8 weeks depending on the readiness of data and integration with existing systems, and we build the MVP in a way that it can be scaled into the product rather than being abandoned.
Our packages include an allocation of dedicated AI engineers as the main resource, with shared UI/UX experts, data scientists, DevOps engineers and project managers, and on-call AI architects. The number of resources varies with your selected package, and hours are fully flexible, across one or more projects. The engagement can be scaled up and down, suspended or resumed on demand - and there's no commitment to ongoing work, which is what most teams want for their evolving AI projects.
Our onboarding process takes about 3-5 days after scoping the project and deciding on the engagement model. For more urgent projects, where data and tooling are accessible, we can onboard even quicker. The discovery and scoping session can take up to two days in itself, and then the team can begin to work once the engagement is signed.
Yes. We have AI app development services that add AI features to your existing software stack, such as chatbots, recommendation engines, predictive analytics tools, NLP-driven search or computer vision. The integration work includes your CRM, ERP, payments and any other existing software you have so you can implement the AI in an existing system your team and customers are already familiar with rather than a new system that needs to be learned.
Yes. We develop AI chatbots with NLP, LLMs, RAG that support complex conversations, learn from user interactions and evolve based on feedback. These chatbots work with WhatsApp, web widgets, mobile apps, Slack, and custom CRM systems, with fall-back rules to hand-off complex cases to human agents with a complete context of the chat.
Not necessarily. Having access to proprietary data is beneficial for domain-specific models, but we can help you collect, clean and generate synthetic data as needed, or reuse open datasets when possible. When using LLM-based models, transfer learning from the foundation model means you don't need to train the model on huge training datasets, which allows you to get an AI system up and running quickly and inexpensively compared to the usual machine learning process.
We deploy via Docker, Kubernetes, AWS, Azure and GCP, with CI/CD automation on GitHub Actions or GitLab CI. The monitoring is via MLflow, Prometheus and our own dashboards, which monitor for model drift, accuracy and use, with automatic retraining to maintain performance. We want an AI system that gets better rather than worse, rather than it ceasing to work six months later, as is common when monitoring is an afterthought.
Yes. We work with AWS SageMaker, Microsoft Azure Machine Learning, and Google Vertex AI for cloud native, scalable AI solutions with full continuous integration/continuous development (CI/CD) support. And hybrid and on-prem deployments using Docker and Kubernetes when needed for compliance, data sovereignty, or cost reasons. The cloud vendor is determined by your IT landscape and where the rest of your stack is hosted, not who we are closest to.
