Intelligence03 / 04 · One team
Applied AI
We use language models and machine learning where they help: support, operations, documents, and product features. Each one has checks and a person who can step in. If a simpler system is enough, we say so.
In this practice
- Workflow automation and internal assistants
- LLM features inside existing products
- Data pipelines and analytics that operators trust
- Evaluation, logging, and fallback paths
What you walk away with
- —Feasibility note and cost model
- —Evaluation harness and quality thresholds
- —Deployed inference with monitoring
- —Fallback and escalation behaviour
How it starts
A scoping call, then a written proposal with the architecture and the price. Nothing starts until you accept the scope.
How it runs
Two-week cycles, with a demo every two weeks. You see the working software, not only a status report.
Who owns it
You do. Source code, infrastructure, and intellectual property transfer to you, with a handover session.
After launch
We can stay for monitoring and updates, or hand the system to your team. You choose.
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