Ai4G solutions

Applied artificial intelligence and AI assistants

Integrate generative and semantic AI into specific analysis and management tasks

We create assistants that query data, interpret results, process documents and guide workflows with context and human oversight.

Tell us about your case

The challenge

When the volume of information exceeds the capacity to interpret it

Teams spend time finding answers, summarising changes, classifying text and reconstructing instructions. AI delivers value when it is integrated into these tasks with defined sources and boundaries.

  • Querying data requires knowledge of technical tools or languages.
  • Reports require a manual explanation of the main changes.
  • Classifying documents or requests takes many hours.
  • Procedures depend on finding instructions spread across several sources.

The outcome

Useful assistants embedded in real workflows

We combine AI models, business context, rules and human validation to accelerate cognitive tasks without losing control or traceability.

  • Natural-language queries against authorised data.
  • Summaries and insights generated on a recurring basis.
  • Documents and requests classified automatically.
  • Guided processes with controls and relevant sources.

What we do

Capabilities applied to your operations

We select and combine the capabilities required according to the challenge, existing systems and the outcome the project needs to deliver.

  • Natural-language data queries

    We translate questions into queries and return tables, summaries and clear contextual information.

  • Reporting and intelligent insights

    We provide context for variations, trends and anomalies according to prioritisation rules and historical context.

  • Document intelligence

    We read, summarise, extract entities from and classify documents or free-form text.

  • Process assistants

    We propose controls, checklists and instructions according to the context of each case.

  • Assisted decision-making and workflows

    We combine classification, recommendations and automation with explicit human validation points.

Use cases

Practical, measurable examples

  • Ask questions about your data

    A user asks a question and receives a table, a summary and the main changes.

  • Weekly summary

    The system explains which metrics have changed and identifies possible explanations based on the data.

  • Classification of incoming requests

    A request is classified, prioritised and prepared for validation by the appropriate team.

  • Review guidance

    The assistant proposes mandatory checks and links to the documentation applicable to the case.

How we work

From the challenge to a solution designed to scale

  1. Assessment

    We identify the challenge, the users involved, the available data and the business outcome the solution needs to deliver.

  2. Functional MVP

    We build an initial usable version to validate the approach with real cases, measure its impact and adjust priorities.

  3. Production rollout

    We integrate, automate and strengthen the solution so that it can operate securely, with traceability and the capacity to evolve.

  • Accessibility

    More users can query and understand information without mastering technical tools.

  • Productivity

    Reading, classification and summarisation tasks require less repetitive intervention.

  • Human oversight

    Important decisions retain human validation, sources and traceability.

Let’s discuss your case

Define a practical, measurable initial scope.

Request an assessment