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MADAI / SOLUTIONS
Private AI designed for specific work.
Choose the area by workflow, data and accountability. Each solution is built as a measurable system with controlled sources, permissions and oversight.
In brief
MadAI Systems connects models, company knowledge, tools and governance into one operating system; scope expands only after quality has been demonstrated.
01 / FRAMEWORK
Measure the system before you scale it.
Architecture, data, models and automation are designed around specific work scenarios and tests, not around a general impression from a chat interface.
Governed AI agents
Automation with bounded tools, approval gates and an audit trail.
Local and hybrid operations
Model gateway, monitoring, security and the ability to change models without lock-in.
02 / IMPLEMENTATION
What has to be ready before production
The outcome must be repeatable, traceable and operable. We therefore evaluate model quality, retrieval, tool actions, security and human decision points separately.
- 01Use-case map
Process, owner, expected result and risk boundary.
- 02Data architecture
Sources, updates, permissions, retention and lineage.
- 03Evaluation set
Real tasks, expected evidence, correctness and safety.
- 04Operating model
Monitoring, incidents, model changes and accountability.
03 / DECISIONS
Questions that directly affect architecture.
The answers determine pilot scope, autonomy, model selection and operating cost.
Where is the best place to start?+
Where repeated tasks exist, data is available and the result can be checked objectively.
Does the entire solution have to run locally?+
No. Local inference, private cloud and governed APIs can be combined according to sensitivity, latency and cost.
How do we avoid an expensive pilot with no decision?+
Define the evaluation set, scope limit and go/no-go criteria before implementation.
Start with a task that can be tested.
We define the data, expected output, risk boundaries and evaluation set before choosing the model and integrations.