Applied AI & ML Development
Models built, evaluated and deployed inside your environment — with confidence scoring, a human review path and monitoring your team can actually read.
Applied AI & ML Development
Taking a model from something that works in evaluation to something an operation can depend on.
We build and deploy inside your own tenancy. Residency and governance constraints are inputs to the design rather than exceptions handled at the end.
Every system is instrumented so drift and failure are visible early, and every output carries the evidence behind it.
How it works
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Evaluate
Build the evaluation set before the model, so success is defined up front.
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Build
Develop and fine-tune against the measures you agreed.
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Deploy
Inside your own tenancy, with confidence scoring and a review path.
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Monitor
Drift and failure visible early, with a retraining routine your team runs.
What we do
- 01Evaluation design and baselining
- 02Model development and fine-tuning
- 03Retrieval and knowledge systems
- 04Confidence scoring and review queues
- 05In-tenancy deployment and MLOps
- 06Drift detection and monitoring
This is for you if
- A proof of concept needs to become a production system
- Your reviewers will not trust output they cannot trace
- Data cannot leave your environment
- You need an audit trail your regulator will accept
What you get
- A working system in production, integrated rather than bolted on
- An audit trail of inputs, outputs and human overrides
- A model card: what it does, where it fails, who reviewed it
- Monitoring and a trained internal owner
A model nobody trusts is a model nobody uses. Traceability is a design requirement, not a feature.