Custom AI development for a business task that needs more than a generic tool. Compare existing models, retrieval, adaptation and purpose-built machine learning before choosing the right approach.
Use this service for document understanding, forecasting, classification, recommendations or image analysis where your data and acceptance criteria define the solution. A custom model is considered when simpler approaches are insufficient.
02 / What we scope and deliver
What we scope and deliver
Data assessment and a comparison of baseline models or non-AI alternatives.
A scoped model or AI pipeline with an evaluation dataset and error analysis.
Inference interface, integration guidance and monitoring requirements for deployment.
How success is evaluated
Evaluate performance on representative held-out data rather than a demonstration alone. Select task-relevant quality measures, inspect failure cases and compare latency and inference cost under the intended workload.
What to bring to the first call
Examples of inputs and the outputs your users consider correct.
Data availability, usage rights, labeling quality and privacy constraints.
Expected workload, integration points and limits on latency or running cost.
From discovery to delivery
01
Define the business problem
Map users, the current workflow, data access and constraints. Agree on the baseline, acceptance criteria and scope before choosing a model or committing to a build.
02
Test a focused pilot
Evaluate a representative task with your data and users. Check quality, error handling, latency and running costs before expanding access or automating critical decisions.
03
Integrate and hand over
Plan permissions, deployment, monitoring and human review with your team. Agree on documentation, training and ongoing support in the project scope.
Explore related project work
These project pages show capabilities and product concepts. They do not establish independently verified customer outcomes or promise a return on investment. GARC includes a floor-plan beta; LogiTwin is a logistics concept.
Pricing depends on the workflow, data readiness, integrations, security requirements and support scope. A strategy call helps define these inputs; a proposal should separate implementation work from model, cloud and third-party running costs.
Can we start with our existing software?
Yes. The first step is to assess available APIs, permissions and data access. A limited integration or pilot may be more suitable than replacing a working system. Feasibility and delivery dates are agreed after discovery.
Will AI operate without human review?
That depends on the risk and task. Define approval points, permissions, fallback behavior and audit logs before deployment. Sensitive actions should stay within the controls agreed with your team.
Plan your implementation
Share your workflow, current systems and target outcome. Choose a time for a strategy call to discuss feasibility, scope and the next step.