We design, build and deploy generative AI and intelligent automation around the way your organisation actually works—with validation, security and human review where they matter.
In practice. The checks, integrations and exception handling are part of the solution. They need to be scoped alongside the model.
We agree the deliverables and responsibilities around your project before work starts.
We can explore a new solution, extend an existing application or help a prototype become a system your team can operate.
Extract, classify and check information in documents, emails and contracts. We design the flow from incoming information to structured output, including the cases that need a person to review or correct the result.
Build applications that help colleagues find information or ask questions of internal data. The design accounts for data access, business definitions, source verification and what the application should do when it cannot provide a dependable answer.
Review the existing application, evaluation approach and deployment constraints. We connect the solution to the required systems and agree testing, monitoring and support responsibilities before it becomes part of a live workflow.
We use the first conversation to understand the question, identify who needs to be involved and agree a suitable next step. Scope, timing and commercial terms follow from that discussion.
Map inputs, outputs, users, existing systems and the consequences of errors.
Test with representative examples and agree what acceptable performance means.
Implement the application, access controls, checks and exception handling within the agreed scope.
Agree the deployment, monitoring, handover and user adoption needed for operation.
An outline is enough. We can identify missing information together.
Each case explains the problem, the approach and the context behind the result.
Yes. We can assess an existing prototype, identify gaps in evaluation or integration, and agree the work needed to take it further. The next step depends on the current system and the workflow it needs to support.
An on-premises deployment can be considered where security, infrastructure and model requirements allow it. Our self-service data case describes a local deployment at a public institution. We assess your requirements before recommending a deployment approach.
We agree evaluation criteria around the real task and test representative examples, including difficult cases. Depending on the workflow, the solution can flag uncertainty, request clarification or route output for human review. These behaviours need to be designed and tested explicitly.
We agree operational ownership, monitoring, documentation and any support scope as part of the engagement. Those responsibilities may sit with your team, with us under an agreed arrangement, or be shared. They should be clear before launch.
Common Sense AI works with enterprises, public organisations and mid-sized businesses in Belgium. Our team combines strategy, engineering and data science, with complementary expertise available through Algorhythm Group when your project needs it.
Projects can start at any stage. We can help explore a question, build a new solution or improve work already in progress.
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