Applied AI and machine learning
Model evaluation, predictive systems, natural-language processing and responsible AI delivery.
- Predictive modelling
- Model evaluation
- Natural-language processing
- Human-supervised AI
About Theunet AI
Engineering AI that works inside real organisations
Theunet AI helps organisations connect their data, cloud infrastructure and operational systems so artificial intelligence can move beyond experimentation into secure, measurable and maintainable services.
Organisational dataCloud & integrationDependable intelligenceHuman oversightMeasured outcomes
What Theunet AI does
From fragmented systems to dependable intelligence
Theunet AI works where data engineering, cloud platforms, software integration and machine learning meet. We help organisations improve the processes surrounding AI—not simply select a model—so teams can move from disconnected data and manual workflows to reliable, human-supervised automation.
- 01Reliable data pipelines
- 02Secure system integrations
- 03Reproducible development
- 04Controlled deployment
- 05Monitoring and observability
- 06Human review
- 07Governance
- 08Continuous improvement
Founder expertise
Technical leadership shaped by delivery, research and continuous learning
Gabriel Mutua is a data, cloud and AI practitioner with experience translating operational challenges into data products, automated workflows and deployable technology systems. His technical work spans data engineering, analytics, cloud architecture, machine-learning delivery, system integration and infrastructure automation.
MSc in Artificial Intelligence · In progress
Alongside applied technology delivery, Gabriel is currently pursuing an MSc in Artificial Intelligence. His research explores ethical, human-supervised and multilingual AI systems for complex African information environments—work that strengthens Theunet AI’s approach to model evaluation, localisation and responsible deployment.
Bachelor of Science in Actuarial ScienceData and analytics
Reliable data products that improve reporting, decisions and operational visibility.
- Data engineering
- Big-data technologies
- Business intelligence
- Statistical computing
Cloud and infrastructure
Repeatable cloud environments and deployable services designed around real operating constraints.
- Cloud architecture
- Infrastructure as code
- Serverless systems
- Monitoring
Technical delivery
Translating business needs into maintainable systems, documentation and team capability.
- System integration
- Architecture design
- Technical leadership
- Capability development
Verified credentialsProfessional certifications
Artificial intelligenceAWS Certified AI Practitioner
Data engineeringAWS Certified Data Analytics – Specialty
Cloud architectureAWS Certified Solutions Architect – Associate
Cloud developmentAWS Certified Developer – Associate
Cloud foundationsAWS Certified Cloud Practitioner
Cloud foundationsMicrosoft Certified: Azure Fundamentals
Infrastructure as codeHashiCorp Certified: Terraform Associate
Data warehousingSnowflake Hands-On Essentials: Data Warehousing
Business intelligenceMicrosoft: Analyzing and Visualizing Data with Power BI
ProgrammingMITx Introduction to Computer Science and Programming Using Python
Prior professional outcomes
Selected technical impact
Verified outcomes from the founder’s prior professional experience—not Theunet AI client engagements.
Scaled personalised systems
A machine-learning recommendation system supported personalised digital experiences at scale through dependable data processing.
Increased engagement
A recommendation-engine implementation improved engagement by connecting behavioural data to more relevant experiences.
Improved retention
Predictive modelling and targeted interventions reduced churn and supported more timely decision-making.
Improved delivery capability
A structured technical mentorship programme improved reported project-delivery speed and quality.
Results vary according to each organisation’s data, operating environment and implementation.
Technical capability
Capabilities for taking AI from idea to operation
Technical choices are connected to the process, people and outcome they need to support.
Data and system integration
Connect information trapped across business systems so it can support analytics, automation and AI-assisted decisions.
We design secure connections across approved enterprise systems, selecting an architecture according to complexity, cost, scale and the organisation’s ability to operate it.
- API and service-to-service integration
- Event-driven, batch and streaming pipelines
- Data validation and observability
- ERP, CRM, POS and operational-system integration
- Microservices only where independent scaling justifies them
- Serverless processing for suitable event-driven work
Machine Learning Operations (MLOps) and production AI
Turn experiments and models into dependable services that can be tested, deployed, monitored and improved.
Production AI is the complete surrounding system—not only the model. It includes reproducible evaluation, controlled releases, monitoring, rollback and operational ownership.
- Versioned code, data and model artefacts
- Reproducible training and evaluation
- Experiment tracking and model registries
- Continuous Integration and Continuous Delivery (CI/CD)
- Batch, real-time and containerised inference
- Drift, performance, latency and cost monitoring
- Staged releases, rollback and incident response
Agentic AI and Model Context Protocol (MCP) integration
Connect AI applications to approved organisational tools and information through controlled, observable interfaces.
MCP is an open standard that allows AI applications to connect to approved organisational data sources, tools and workflows through consistent interfaces. It does not remove the need for security, permissions or governance; we present it as a capability and reference-architecture pattern.
- MCP clients, servers and secure tool gateways
- Approved APIs and retrieval systems
- Specialised agents and workflow orchestration
- Role-based access and secrets management
- Human approval for sensitive actions
- Tool-call logging, audit trails and failure handling
Cloud migration and infrastructure automation
Modernise applications and data platforms while making environments more repeatable, secure and easier to operate.
Technology choices should match the workload, risk, skills and budget. Kubernetes, microservices or multi-cloud designs are recommended only when their operating value justifies their complexity.
- Cloud-readiness and dependency assessment
- Rehosting, replatforming or refactoring plans
- Container and serverless architecture
- Infrastructure as Code (IaC), including Terraform where appropriate
- Identity, networks, secrets and automated deployment
- Monitoring, recovery and cost optimisation
Responsible and localised AI
Adapt AI to the organisation’s domain and users without losing control of privacy, safety or quality.
Localisation may use Retrieval-Augmented Generation (RAG), configuration, domain evaluation or fine-tuning, depending on evidence and governance. Appropriately governed client-owned data remains isolated and is never assumed to train a third-party model.
- Domain-specific and multilingual evaluation
- Representative test data and data minimisation
- Consent, purpose limitation and residency controls
- Bias, fairness and appropriate explainability
- Human review, escalation and activity logging
- Red-team testing and ongoing monitoring
Process improvement and AI adoption
Introduce AI where it improves a real process, with clear ownership, controls and measurable results.
AI adoption changes a process and its operating model; it is not simply a software installation. We connect technical delivery to people, baselines and measurable outcomes.
- Process, AI and data-readiness assessment
- Use-case prioritisation and baseline measurement
- Workflow redesign and controlled pilots
- Human-in-the-loop design
- Training, change management and adoption measurement
- Business and technical KPIs with continuous improvement
Delivery approach
From a real problem to an operable system
We do not assume that every project needs a new model. The starting point follows the evidence.
- 01
Discover
Understand the business problem, current process, users, risks and expected outcome.
- 02
Assess
Review data quality, existing systems, integration requirements and organisational readiness.
- 03
Design
Select the simplest suitable architecture, define controls and agree measurable success criteria.
- 04
Build and integrate
Develop the data pipelines, applications, model services and business-system connections.
- 05
Deploy responsibly
Test security, quality, performance and human-review controls before release.
- 06
Operate and improve
Monitor reliability, model behaviour, adoption, cost and business impact.
Engineering principles
How we make technology decisions
Integrate before replacing
Preserve useful systems and connect them where that produces a better outcome.
Automate with appropriate controls
Match oversight and approval to the consequence of each action.
Design for observability
Make system health, quality and cost visible enough to operate responsibly.
Keep humans responsible
Consequential decisions retain clear human ownership and escalation.
Localise using evidence
Evaluate language, context and domain performance rather than relying on assumptions.
Protect client data by design
Apply isolation, minimisation and purpose controls from the start.
Fit architecture to the workload
Choose the simplest approach that meets risk, scale and operating needs.
Measure beyond model accuracy
Track reliability, adoption, cost and organisational outcomes too.
Build for ownership
Prioritise maintainability, documentation and effective handover.
Move from an AI idea to a workable delivery plan
Tell us about the process, system or decision you want to improve. The discovery tool will help identify whether the right starting point is integration, automation, data engineering, cloud modernisation, model deployment or a custom AI solution.