Stable, verifiable process
Rules and automation
Best when decisions are deterministic, exceptions are defined and ambiguity does not need interpretation.
Artificial Intelligence and Machine Learning
AI to solve real problems—not to add another tool.
We design and develop Generative AI and Machine Learning solutions around our clients’ specific processes, data and objectives.
GenAILanguage, knowledge and content applied to workflows.
MLPrediction, classification and pattern detection.
VoiceAutomated interactions connected to the business.
HumanControl, validation and traceability where they matter.
Applied intelligence
Technology begins by understanding which problem is worth solving.
We do not start with a model or a trend. We analyse the real work, available data, decisions and exceptions. We then select the right combination of Generative AI, Machine Learning, automation and software development to create a useful, integrated and measurable solution.
AI architecture and operations
A useful AI solution is designed to be evaluated, controlled and operated.
The model is only one component. Value appears when data, context, instructions, tools, permissions, controls and people form a system that completes a task end to end.
Choose the right technology
Not every problem needs Generative AI, and not every workflow should become an agent.
Patterns in historical data
Machine Learning
Prediction, classification, scoring, recommendation or detection when representative data and a measurable target exist.
Language and knowledge
Generative AI and RAG
Understanding, extraction, synthesis or generation grounded in company context, with sources, boundaries and purpose-built evaluation.
Multi-step workflows
Tool-using agents
For interpreting, deciding and acting across systems. Every tool needs least privilege, limits, traceability and human escalation.
From use case to production
- 01
Objective
User, task, acceptable outcome, impact and business metric.
- 02
Data and context
Sources, quality, permissions, knowledge, retention and sensitive data.
- 03
Architecture
Model, retrieval, tools, integrations, state and experience.
- 04
Evaluation
Real cases, expected outputs, exceptions, attacks and acceptance thresholds.
- 05
Controls
Authentication, authorisation, guardrails, validation, limits and human review.
- 06
Integration
Application, APIs, observability, versions, tests and gradual rollout.
- 07
Operations
Quality, latency, cost, errors, drift, feedback, incidents and improvement.
Quality is specified before testing
We turn “works well” into observable, repeatable criteria.
- 01Task success
- 02Accuracy and source grounding
- 03Instruction adherence
- 04Safety and authorised use
- 05Latency and availability
- 06Cost per accepted outcome
- 07Correct human escalation
- 08Regressions between versions
Autonomy proportional to risk
The ability to act expands only when evidence and controls support it.
- Read
Consults authorised sources and returns traceable information.
- Recommend
Proposes a decision for a person or deterministic system to validate.
- Prepare
Creates drafts, classifications or actions pending approval.
- Act
Executes reversible, bounded and logged actions within its permissions.
- Escalate
Stops and hands over when uncertainty, risk, exceptions or high impact arise.
What we measure in production
Quality
Success, errors, unsupported outputs, acceptance and human review.
System
Latency, availability, limits, tool failures and dependencies.
Business
Time saved, capacity created, conversion, avoided risk or cost per outcome.
Evolution
Feedback, new edge cases, model changes, regressions and improvement decisions.
What we can solve
Time-consuming processes, information that does not scale and decisions that can improve.
AI creates value when it becomes part of a specific workflow and its outcome can be validated clearly.
Repetitive manual work
Reading, classification, checking or responding consumes time and limits scale.
Unstructured information
Documents, conversations and internal knowledge exist but are hard to consult or process.
Patterns that are hard to anticipate
Historical data contains signals that can support forecasting, prioritisation and detection.
Isolated experiments
AI prototypes or tools exist but are not integrated with data, systems and accountable teams.
What we develop
Complete solutions, from the model to integration with everyday work.
We select technology and architecture around the use case, risk, data and volume—not a closed platform.
Generative AI
Language and knowledge inside real workflows.
- Specialised assistants and copilots
- Document and knowledge retrieval
- Content extraction, summarisation and generation
- Model and API integration
Machine Learning
Models that learn from data and patterns.
- Prediction and forecasting
- Classification and prioritisation
- Scoring and recommendation
- Anomaly detection
Voice and conversation
Automated interactions connected to operations.
- Calls and voice agents
- Transcription and understanding
- Data verification and capture
- Escalation to human teams
Product and integration
We turn the model into a usable capability.
- Applications, interfaces and back office
- APIs and system integration
- Permissions, security and traceability
- Monitoring and evolution
How we work
From problem to an integrated, validated solution that can evolve.
- 01
Define the problem
We specify users, workflow, expected outcome, boundaries and measures of value.
- 02
Prepare data and architecture
We review sources, quality, permissions, risks and integration with existing systems.
- 03
Test and validate
We build a bounded first solution and test accuracy, usefulness and exceptions with real cases.
- 04
Integrate and operate
We deploy, monitor, document and improve the solution through real use.
Featured use cases
Intelligence applied to real processes.
Two solutions developed around specific operational and customer relationship needs. We will expand both cases with data and outcomes.
Warranty management
A solution for the Warranty Management system.
Voice automation
FUTURENER
An automated calling system to verify data and manage electricity supply contract activation.
Why Sitelicon
AI requires development, integration and process knowledge.
We have spent 21 years building technology and connecting systems. That experience lets us turn AI models into solutions that fit existing applications, data, teams and operations.
- Generative AI and Machine Learning led by the use case
- Software development and systems integration
- Human validation, security and traceability
- Deployment, monitoring and continuous evolution
Frequently asked questions
Artificial Intelligence with a clear use case.
01What is the difference between Generative AI and Machine Learning?+
Generative AI is particularly useful for language, documents, knowledge and conversations. Machine Learning learns patterns to predict, classify, prioritise or detect. A project may use either technology or both.
02Can you integrate the solution with our systems?+
Yes. We build applications, APIs and automations that connect intelligence with ERP, CRM, ecommerce, databases, telephony and other platforms.
03Do we need a large amount of data?+
It depends. Some predictive models need enough reliable history; certain generative solutions can use documentation and existing services. We assess this before choosing the approach.
04How do you control AI output?+
We define evaluation criteria, traceability, permissions, boundaries and human review according to the process impact and risk.
05Can we begin with a bounded trial?+
Yes. We recommend limiting the workflow, sources and metrics to validate usefulness and viability before expanding.
Applied Artificial Intelligence
Which real problem do you want to solve with AI?
Tell us about the workflow, people involved, available data and the outcome that should improve. We will design a verifiable first scope.
Send an enquiry
Let’s start with the context.
We only ask for the information needed to understand and route your enquiry correctly.
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