Where this work gets applied, and what changes when the setting is regulated.
The area we know best. Clinical and operational data, document-heavy workflows, and settings where an answer has to be explainable before anyone will act on it. Our regulatory and submissions work runs under techworkslab.com.
Forecasting, quality analytics and automation of the repetitive decisions inside a production or supply process.
Risk and portfolio analytics, anomaly detection, and reporting pipelines where an audit trail is not optional.
In healthcare and finance the accuracy figure is rarely the thing standing between a model and production. The obstacle is the assurance argument: what the system is allowed to decide, how that is evidenced, and what happens when it is wrong.
We design that argument alongside the system instead of assembling it afterwards, which is both cheaper and considerably more convincing to an auditor.
We will say plainly whether machine learning is the right tool for it.
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