AI opportunity assessment
Evaluate a specific task, its data, and its operating context before choosing an engineering approach.
- Workflow and data assessment
- Use case scope and evaluation measures
- Prototype and integration priorities
AI engineering
Build AI into business applications.
Develop, evaluate, and integrate AI models for prediction, classification, and generative tasks. Connect the solution to your data and workflows, with deployment requirements and human oversight defined.
What we do
AI development, evaluation, and application integration.
Assess the task and available data, develop or adapt the model, and integrate it into the application. Evaluate performance and define deployment and oversight requirements.
Model development and adaptation
Develop or adapt a model to an agreed prediction, classification, or generative task.
- Data preparation and model selection
- Model development or adaptation
- Evaluation with representative examples
Application and workflow integration
Connect model outputs to the product or process where people will use them.
- Application interfaces and data connections
- Workflow and human review integration
- Error handling and application testing
Production readiness
Prepare the solution for deployment and define the monitoring and oversight it needs.
- Deployment and infrastructure requirements
- Performance and operational checks
- Monitoring, documentation, and handover
What you get
Evaluated AI components integrated into your application.
Build around a defined task, evaluate the model in context, and connect its outputs to the people and systems that will use them.
AI capabilities connected to application workflows.
Develop or adapt AI components and connect them to the product or process where they can support an agreed task.
- Model components suited to the task
- Application interfaces and data connections
- Workflow integration with human review points
Evidence of what the solution can and cannot do.
Check results using representative examples and agreed measures so your team can understand performance, limitations, and review needs.
- Evaluation against task-specific criteria
- Representative data and test examples
- Known limitations and oversight requirements
The foundations for deploying and running the solution.
Prepare the deployment setup and operational checks needed for the agreed environment, with responsibilities for monitoring and review made clear.
- Deployment and configuration guidance
- Performance and operational checks
- Monitoring and review responsibilities
How we work
From a defined use case to an integrated solution.
Agree on success criteria, evaluate the approach with representative data, and test the application integration. Prepare the solution for the agreed deployment environment.
Scope the task
Agree on the workflow, intended users, and outcomes to evaluate.
Develop and evaluate
Test an approach using representative data and relevant examples.
Integrate
Connect the solution to the application and define human review points.
Prepare to operate
Validate deployment, monitoring, and the responsibilities after release.
Why it matters
Evaluate AI against the task it needs to perform.
A defined use case, representative evaluation data, and clear oversight requirements provide a basis for AI development and deployment.
Focused applications
Choose a task where AI has a practical role in the workflow.
System integration
Define the data access and application interfaces needed to use model outputs.
Human oversight
Define review points and responsibilities for AI-assisted work.
Operational readiness
Plan deployment, monitoring, and changes after launch.
Where to start
Choose an application for AI.
Start with a specific task, suitable data, and a measurable outcome for the application or workflow.
Take a pilot into production
Review the model, integrations, infrastructure, and operating gaps before extending a pilot.
Add prediction to a workflow
Connect a model to an existing decision or process and define how results are checked.
Automate a classification task
Explore a model for organizing or routing information with appropriate human review.
Build an AI-enabled application
Start with a product task and determine the model and application components it requires.
Questions
Planning your AI engineering project.
Answers about scope, existing systems, and delivery requirements.
How does AI engineering differ from GenAI consulting?
AI engineering focuses on building, evaluating, and integrating AI components into applications. GenAI consulting focuses specifically on identifying and shaping applications of generative models.
Where does MLOps fit?
MLOps addresses repeatable model delivery, monitoring, and ongoing operations. It may form part of an engineering engagement or a separate scope for existing models.
Do we need a trained model already?
No. Work can begin with a task and suitable data, or with an existing model or pilot. The starting point determines the engineering scope.
How is the solution evaluated?
Evaluation is defined around the task and intended use, using relevant examples and agreed measures. Limitations and review requirements should be documented before release.
Can AI connect to an existing product?
The integration approach depends on the product architecture, available interfaces, data access, and workflow requirements. These are reviewed before implementation.
Next steps
Build AI around a defined business need.
Share the task, available data, and application requirements. We can assess the engineering and evaluation work involved.