Data and opportunity audit
We review the available data and task examples. We decide what to test in a pilot and what is needed to run it.
AI solutions for business
Documents, enquiries or hours of manual searching piling up? We test whether AI can help with the work. We start with your examples and assess quality and running costs.
Discuss your requirementsBefore we startScope and budget
After launch30 days of support
Test AI against your actual task
We define the task, available data and evaluation criteria. A pilot uses your company's examples so we can compare outputs with expected results. We assess errors, running costs and the need for human review. We then agree on integrations, access controls and a development plan based on the findings.
A model built for your task
We compare models using your examples. Where useful, we add knowledge-base retrieval or assess model training.
Integration into workflows
We connect AI to the tools you need. We define which actions can run without approval and where a person should step in.
Measurable results
We track metrics before and after launch: accuracy, processing speed, and hours saved for your team.
What the work includes
This is what the work can include. We will decide what your project needs together and agree the scope before starting.
Requirements and prototype
- Workflows and constraints
- First-release functionality
- Key screen prototypes
UX/UI design
- Interface structure
- Responsive layouts
- States, errors and guidance
Development and integrations
- Data and quality criteria
- Pilot and output evaluation
- Integrations and quality monitoring
Testing and launch
- Testing agreed workflows
- Access and documentation
- Post-launch support
How we work
You see the work at each stage and can ask questions. Your technical lead helps with decisions, and the weekly update shows what is done and what comes next.
The task and data
We choose a specific task to test, collect examples and agree acceptable errors and how to evaluate quality.
Pilot on your data
We build a prototype and test the hypothesis on real data. You see accuracy in numbers, not promises.
Development and validation
We test responses against difficult and incomplete data. We add checks and human review where needed.
Integration and launch
We embed the solution into your systems, set up infrastructure and access controls, and roll it out in stages.
Review after launch
Under an agreed plan, we review errors, costs and new examples. We adjust settings, the knowledge base or the model based on what we find.
A project from our portfolio
Explore the design, pages and details of a LONCH project.
Before you start
Cost, preparation and project support.
How do we know whether AI fits our task?
We collect examples, agree quality criteria and compare a pilot with your current workflow. The decision depends on output quality, errors, running costs and human oversight.
How do you handle AI errors?
We design output checks, logging and human escalation. Actions with significant consequences can require additional approval. Quality monitoring continues after launch.
What determines the development cost?
The cost depends on workflows, design complexity, integrations, data migration and available materials. After clarifying the brief, we define the scope and estimate. The budget is agreed before development starts.
How is the launch timeline agreed?
We review the scope, external dependencies and content readiness before agreeing milestones. If you need an earlier release, we define which functions belong in the first version.
What happens after launch?
We provide 30 days of support after release and help with launch issues and team questions. Further features and product development are planned separately.
What repetitive task would you like AI to handle?
Describe the task and share a few examples without confidential data. We will discuss what to test first.
Discuss a project