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Artificial intelligence

AI in your tools.
Built for real use cases.

We start with a concrete task: finding information, processing a document or assisting a voice conversation. We then evaluate the data, models and integrations required.

Let’s discuss your project

USE CASES BEFORE TECHNOLOGY

What we can build.

01

Knowledge & documents

Search, information extraction and writing assistance.

02

Voice assistants

Transcription, speech synthesis and connections to business services.

03

Assisted automation

Preparation of tasks and human approval at defined stages.

Evaluate before scaling up.

An initial trial using representative data measures response quality, errors and limitations. Data access, providers, costs and human checks are defined before integration.

FOR OUR FIRST CONVERSATION

A concrete task.
Before choosing the model.

Let’s start with an example of work you want to simplify. We can then assess whether AI is an appropriate answer.

WHAT TO SHARE WITH US
01The task and an expected result
02Available, authorised sources
03What makes a response acceptable

You don’t need a complete specification to get started.

HOW IT WORKS

From data.
To a response you can check.

AI is one component of a workflow. Its value depends on accessible data, response quality and the controls around the model.

EXAMPLE WORKFLOW · A DOCUMENT ASSISTANT
  1. 01

    The sources

    Authorised, up-to-date documents.

  2. 02

    The search

    Passages relevant to the question.

  3. 03

    The response

    A proposed response with references.

  4. 04

    The check

    Checks and decisions appropriate to the use case.

This diagram illustrates a document assistant. Voice and extraction projects use a tailored workflow, defined during scoping.

DECISIONS TO MAKE TOGETHER

The choices that
make the difference.

Concrete decisions, before turning them into development work.

01Usefulness & success criteria

Which task do we want to simplify? We define what the tool should produce and how to assess quality: relevance, errors, unanswered cases or the need for human intervention.

To agree: target task and evaluation criteria.
02Data & access

We review sources, their quality, access rights and hosting constraints. Providers and proposed processing are discussed before your data is used.

To agree: authorised sources and data processing framework.
03Prototype & evaluation

A representative test dataset allows us to compare responses, identify limitations and observe costs and processing times. Unsatisfactory results may lead to a revised scope.

To agree: trial results before wider rollout.
04Integration & monitoring

We define checks, human validation and fallback scenarios. Feedback, errors and changes to sources should inform ongoing monitoring.

To agree: validation rules and operational monitoring.

A DEFINED PROJECT, VISIBLE PROGRESS

From the first conversation
to launch.

Our agile method
  1. 01

    Qualify the use case

    Task, available data and evaluation criteria.

    You validate

    The use case, data and criteria.

  2. 02

    Prototype & measure

    Test dataset, response comparisons and analysis of limitations.

    You see

    Results and limitations from a test dataset.

  3. 03

    Integrate & monitor

    Integration with your tools, checks and results monitoring.

    You receive

    Documented integration and control rules.

DELIVERY & HANDOVER

What you
receive.

  • Prototype and evaluation results for the agreed scope
  • Documented integrations and configuration
  • Identified limitations and validation rules
  • Monitoring and handover arrangements

The scope and deliverables are specified in the proposal.

Your questions.
Practical answers.

Where does the data used by AI go?

Hosting, providers and processing are defined during scoping according to your confidentiality requirements and the services considered. These decisions precede the use of your data.

How do we know whether AI is useful?

We define task-specific criteria: response quality, errors, processing time or the need for human intervention. A prototype lets us evaluate them before expanding the scope.

Can AI make mistakes?

Yes. The system needs to handle uncertain answers, missing information and errors. Depending on the use case, we plan human review, a refusal to answer or a fallback workflow.

Do we need to train a custom model?

Not always. An existing model, a search across your documents or conventional automation may be enough. The prototype helps choose an approach proportionate to the task.

DESIGN & DEVELOPMENT

Explore our other services.

From mobile products to business tools, the expertise your project needs.

Mobile app

iOS and Android apps, from initial user journeys to publication on the stores.

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Web app & SaaS

Browser-based platforms connected to your data and services.

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Business software

Custom tools for your internal processes, data and business rules.

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Your project.
Let’s talk.

Describe your needs