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What we do · Practice
Intelligence.
We build AI into applications and business processes, supporting software development and modernisation, task automation, data analysis and decision support.
The idea
From the use case
to production.
Many AI projects stall at the pilot because the data is not good enough, the core systems are not connected, or nobody looks after the model once it is live.
How it reaches production
A structured
path.
Every model goes live from a real use case and with measures that let it be judged over time.
Phase 01
Finding the use cases that matter first
We analyse the business processes to find where artificial intelligence can bring a real benefit.
Phase 02
Technology assessment and data readiness
We assess what data is available, how good it is and what it looks like, alongside the existing technology and infrastructure.
Phase 03
Designing the data platform and the AI architecture
We settle the data platform, the models and the AI services, and wire them into the existing systems and applications.
Phase 04
Integration and industrialisation
We build the AI solutions into the business systems, connecting them to the data and the processes they are meant for.
Phase 05
Governance, monitoring and steady improvement
We watch how the solutions behave and judge their performance, reliability and usefulness over time, finding where they can be improved.
The services
From artificial intelligence to its use inside a process.
01
Artificial
Intelligence
We build generative AI and agents into the existing business systems, keeping the data inside the company’s own infrastructure.
Where we work
- Generative AI in use: RAG, chatbots, semantic search, summarising and extracting from documents
- Building agentic workflows
- Intelligent process automation
- Decision support systems
The path runs from setting the strategy to going live: integration with the existing systems, data security, compliance and performance control.
02
Machine
learning
We build predictive models and put them to work inside business processes, following how they perform over time. One use is categorising indirect spend, where accuracy reaches 95%.
The life cycle of a model
- Data engineering and feature engineering
- Training, validation and tuning
- Deployment in the cloud or on-premise
- Performance monitoring and model governance
Kinds of solution
Predictive models support classification, forecasting, anomaly detection, recommendations and process optimisation. Built into the IT architecture, they become parts of the business solutions, with results that can be watched and measured over time.
03
Governance
of models
Every model comes with documentation and a record of the decisions behind it, overseen by a named owner.
What stays switched on after release
- Model documentation: training data, limits, intended use cases
- A log of decisions, to reconstruct why the model answered as it did
- Human oversight on the decisions that matter
- Performance monitoring, drift detection, retraining
Documentation, traceability and human oversight are the groundwork for meeting the requirements of the AI Act.
The products of this practice
OptiMIPS
Reads the mainframe data, attributes consumption to the applications and finds where it can be optimised.
TSV — Total Spending Visibility
Categorises indirect spend with AI to an accuracy of 95%, giving visibility by category and supplier.
Calamìta
Cross-references NatCat data with geolocated policies in real time and finds the portfolio exposures.
Tequery
Lets you query databases in plain language and see the SQL generated behind every answer.
PriceSense
Reads news and market data to estimate raw-material prices and availability. An analyst validates the estimates.