Industry leaders that rely on N-iX tech expertise
Improve your business outcomes with cutting-edge AI technologies
Fast data-driven decisions
Make informed impactful decisions with insights gained from AI and Machine Learning development services.
Data monetization and revenue growth
Increase your profits by optimizing operations and expenses, improving customer satisfaction with personalized experiences, conducting effective marketing, and more.
Automation and optimization
Drive business innovation, efficiency, and sustainability by automating internal processes, eliminating manual work, and boosting employee productivity.
Faster AI and ML adoption with effective team scaling
Expand your team with our experienced specialists who can help you streamline your Artificial Intelligence development and ML.
Adopting AI: Our clients’ success stories Case studies
Your AI and ML journey with N-iX
What we do:
We start by aligning your AI and ML vision with business goals and compliance needs. This phase defines your data strategy, validates technical feasibility, and ensures readiness for secure and responsible AI adoption.
Key actions:
- AI readiness and data maturity assessment
- Use case discovery and prioritization
- Risk, compliance, and privacy assessment
- Responsible AI principles and governance framework
- ROI and cost-efficiency modelling
What we do:
We design modular architectures that integrate ML, RAG pipelines, and multimodal AI systems. Prototyping validates performance, accuracy, and data protection before large-scale rollout.
Key actions:
- Data pipelines and feature store design
- RAG (Retrieval-Augmented Generation) and enterprise AI search
- Multi-agent orchestration using LangChain, LangGraph, CrewAI
- Multimodal data processing
- Secure data flow and anonymization practices
- Human-in-the-loop validation and feedback setup
What we do:
We develop, train, and deploy ML models and AI systems with full MLOps / LLMOps automation for traceability, version control, and continuous evaluation. Security, observability, and cost optimization are built into every deployment.
Key actions:
- End-to-end pipeline automation
- Model registry, lineage tracking, and audit trails
- Guardrails: prompt-injection prevention, content filtering, role-based access control
- Continuous training, retraining, and drift detection
- Token and compute optimization, caching, quantization
- Deployment flexibility: cloud, hybrid, edge, or on-device
What we do:
We maintain performance, transparency, and compliance throughout the model lifecycle. Our monitoring and governance frameworks ensure your AI systems remain safe, explainable, and cost-effective at scale.
Key actions:
- Observability and real-time model performance dashboards
- Explainability and bias detection tools
- Continuous compliance and vulnerability assessments
- Proactive retraining and feedback loops
- Governance and KPI reporting for business stakeholders
Effective strategy for AI and Machine Learning adoption
Successful AI and ML development requires a solid adoption strategy. Whether you need to build a strategy from
scratch or want to ensure the existing one is up for the task - N-iX, as a ML development company, can help.
Adoption strategy design
- Defining, qualifying, and prioritizing use-cases and objectives
- Vision for Artificial Intelligence development
- PoC and cost estimation
- AI/ML solution architecture design
- Tech stack and team composition
- Roadmap and timelines
- Model governance
- Framing ModelOps cycle, ML Model Governance, and managing models life cycle
Adoption strategy assessment
- Qualification of use-cases and objectives
- AI/ML solution architecture review
- Data strategy review
- Roadmap and timelines assessment
- Reviewing ModelOps practices, including ML model governance and models lifecycle management
- AI readiness estimation
- AI and Machine Learning development workshopst
Our AI and ML development capabilities
ML models development
Deep learning
Computer vision
Natural language processing (NLP)
Chatbots and conversational AI
Recommendation systems
Reinforcement learning
Deploying ML to production
Boost AI and ML ecosystems with generative AI, together with N-iX
Generative AI has become an integral layer of enterprise AI maturity. Our team can extend existing AI and ML solutions with generative AI capabilities, adding reasoning, summarization, and multimodal, contextual understanding to established models. We help enterprises evolve from predictive analytics to systems that interpret, communicate, and act.
Enterprise RAG and AI search
Enable context-aware answers and knowledge retrieval using retrieval-augmented generation (RAG). Our teams integrate vector databases, embeddings, and content validation into your ML pipelines for reliable, traceable insights.
Agentic AI systems
Deploy coordinated autonomous systems for analytics, process automation, and decision support. Built with LangChain, LangGraph, or CrewAI, our agentic architectures include human oversight, policy enforcement, and auditability.
Multimodal AI systems
Combine text, image, video, and sensor data to power richer enterprise intelligence. We design document understanding, visual analytics, and cross-domain data reasoning.
LLMOps and evaluation
Ensure performance, safety, and scalability with robust model observability and evaluation. We unify LLMOps and MLOps for continuous improvement, drift detection, and controlled retraining.
Responsible and compliant AI
Adopt Generative AI responsibly with privacy-by-design principles, human-in-the-loop validation, and compliance with GDPR, ISO 27001, and SOC 2. Our architectures include guardrails for prompt injection, content filtering, and data protection.
Performance and cost optimization
Reduce operational costs through token optimization, caching, and quantization. We design hybrid and self-hosted deployments that balance accuracy, latency, and budget.
Efficient and stable solution performance with MLOps and AI/ML maintenance
With the help of MLOps and AI/ML maintenance, N-iX enables a stable and efficient performance of all your
systems. We also make sure that they are easy to scale, support, and reproduce.
MLOps
- Orchestrated experiments
- Automated ML pipelines
- Connecting DataOps and MLOps pipelines using feature stores
- CI/CD for Machine Learning
- Continuous training for orchestrating and automating pipeline execution
- ML pipeline deployment
- Model governance
- Developing and setting up monitoring and observability
- Leveraging MLOps ecosystem
AI/ML maintenance
- Monitoring data and ML model pipeline health
- Monitoring and analyzing model performance
- Detecting drifts in model inputs and outputs
- Monitoring data quality
- Model explainability
- Performing root-cause analysis of issues in model production
- Troubleshooting
- Retraining models
- Post-deployment management
AI and Machine Learning tech expertise at N-iX
What helps N-iX match any AI and Machine learning development need
60+
Data science and AI projects delivered
150+
Data and cloud
certified experts
200+
Data, AI, and ML experts
ISG-recognized
Rising star in data
engineering
23+
Years of experience
2,400+
Software engineers and
IT experts
Meet our Technology leaders and Data experts
Igor Tymchuk
Head of Data Unit, AVP Delivery
Valentyn Kropov
Chief Technology Officer
FAQ
This is what any AI project starts with. Our experts assess how AI-powered technologies align with particular business goals, evaluate data readiness, prioritize high-value yet low-risk use cases, validate AI-driven solutions, and optimize AI harnessing.
When creating an AI/ML adoption roadmap, we define, qualify, and prioritize objectives and use cases, form a vision for AI development, perform PoC and cost estimation, design AI/ML solution architecture, determine the tech stack and team composition, and establish a project timeline.
We set up a robust AI governance framework, implement stringent data encryption and system access controls, conduct data anonymization and minimization, perform regular adversarial testing and risk audits, continuously update dependencies and models, and monitor regulatory norms in the industry.
The implementation timeline is conditioned by the solution's complexity, training data availability, the required computational infrastructure, integration needs, etc. Usually, a single-process AI/ML solution can be created within three to six months, whereas comprehensive enterprise-wide platforms take one or even two years to build.
On demand, we track the system's performance and outcomes, study real users' feedback, monitor current market shifts and domain dynamics, and fine-tune the solution to ensure it operates seamlessly, keeping your company ahead of the curve.
More about our Data Engineering, AI, and Computer Vision expertise
Data Engineering & Analytics
AI, ML & GenAI
Computer Vision & NLP
Contact us
Drop a message to our team to see how we can help
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