End-to-end service

AI and ML development and implementation

We take your project from the business problem and data preparation to model evaluation, integration and daily operation. Our work covers predictions, images, text, speech and generative AI.

Discuss your project Explore our process
End-to-end delivery
1 Scope and data Criteria
2 Evaluation Metrics
3 Deployment Integrations
4 Operations Monitoring

We start with a decision or workflow your team wants to improve and the data the business can use. The task might involve demand forecasting, visual inspection, document processing, call analysis or an internal assistant. That task determines the method and how we evaluate it.

Start with a workflow you can evaluate

During discovery, we review data quality, access restrictions and the consequences of errors. We record the current process as a baseline and agree on pilot criteria. The business case includes assumptions, operating costs and any human review the workflow needs.

You receive a proposed scope, delivery plan and cost estimate before implementation begins. The schedule reflects data readiness, any labelling work and access to integrations.

Evaluate the model and the application

We prepare representative data and compare suitable approaches with the current calculation or workflow. Training and configuration are separate from final evaluation. We examine important groups of examples, rare errors and incomplete inputs before extending a pilot.

The application connects to the systems your employees already use. Deployment can use a managed cloud service, private cloud or your own infrastructure. We define access, capacity, data storage and recovery, then test the full path from incoming data to an employee decision or application action.

Monitor what happens after launch

Monitoring covers availability, latency, changes in input data and task quality as verified outcomes become available. Model and workflow updates go through evaluation before release, with a rollback path for significant changes. Incident response and further development can continue through managed AI services.

If you already have an architecture or an internal development team, you can commission an individual service within the same delivery process.

Project stages

  1. 01

    Problem and data

    Agreed in the project plan

    We review the workflow and available history, establish a baseline and agree on constraints and pilot success criteria.

  2. 02

    Development and evaluation

    Agreed in the project plan

    We prepare data, compare approaches, train or configure a model and evaluate it on a separate test set.

  3. 03

    Integration and launch

    Agreed in the project plan

    We deploy to a cloud or private environment, connect business systems and test workload capacity and failure handling.

  4. 04

    Ongoing operation

    Under agreed support terms

    We monitor availability, data and quality, evaluate updates and respond to incidents through the agreed support process.

What you receive

  • Project plan with estimated benefits, risks and data readiness
  • A model or application with evaluation results and acceptance criteria
  • A working integration in the agreed environment
  • Operating documentation, monitoring and support terms