Data-Driven Marketing
Application Development
Macrolab and Macroscan provide a robust foundation for application development in laboratory imaging and analysis. Built for clear image capture, calibration, and reproducible workflows, these platforms can support the development of tailored applications when a laboratory has requirements that extend beyond the current standard scope.
If there is interest in a custom application, Digonaut can support the evaluation and development process together with the customer. Our approach is practical and collaborative: we start from the scientific and operational need, assess technical feasibility, and define a path toward a validation-friendly workflow.
From Platform to Proof of Concept
- Feasibility assessment based on the intended assay or inspection workflow
- Proof-of-concept development on Macrolab or Macroscan
- Joint definition of customer requirements through URS drafting
- Alignment with reproducibility, traceability, and validation-friendly workflow expectations
- A collaborative development path from initial idea to application design
Typical Use Case
A pharmaceutical company may want to assess whether a new imaging-based workflow—such as vial inspection—can be supported on an existing Digonaut platform or a custom optical setup. In such cases, we can work together to define the requirements, build a proof of concept, and evaluate how the application could fit into a broader standardized workflow.
What types of custom applications can be developed on Macrolab or Macroscan?
Macrolab and Macroscan provide a strong technical foundation for custom laboratory imaging applications, particularly where clear image capture, calibration, and reproducible workflows are critical. Depending on the use case, they can support proof-of-concept development for tailored applications that extend beyond the current standard scope. This may include new assay formats, specialized imaging workflows, or application-specific evaluation methods .
How do you assess whether a proof of concept is technically and scientifically feasible?
We begin with the scientific and operational need. From there, we review the sample type, imaging requirements, expected performance criteria, and workflow context to determine whether the application is a suitable fit for Macrolab or Macroscan. The goal is to assess feasibility in a structured way and build an early understanding of performance, reproducibility, and the path toward validation-friendly workflows.
Can Digonaut support URS drafting for a new laboratory imaging application?
Yes. If a proof of concept shows sufficient potential, Digonaut can work together with the customer to draft a User Requirements Specification (URS) . This helps translate scientific needs and operational expectations into a structured document that can support further development, internal review, and preparation for validation activities.
How do Macrolab and Macroscan help create a reproducible and validation-friendly workflow?
Macrolab and Macroscan are designed around controlled image capture, calibration, and standardized workflows. Features such as reproducible imaging conditions, saved calibration parameters, and consistent handling of assay images help reduce variability and support traceable evaluation processes. This creates a stronger basis for validation-friendly workflows and more reliable long-term use in laboratory environments.
What is the typical process from proof of concept to validation readiness?
A typical process starts with an initial discussion of the intended application and technical requirements. This is followed by feasibility assessment and proof-of-concept development, where performance can be reviewed together with the customer. If the concept is promising, the next step is to define requirements in a URS and identify the documentation and workflow elements needed to support validation preparation. This creates a structured transition from early technical exploration to a more formal development and validation path.
How quickly will I see results from your platform?
Most clients begin seeing measurable improvements within the first 30 days of implementation. Our platform starts optimizing immediately, but the most significant results typically emerge after 2-3 months as our AI learns and refines your campaigns based on performance data.