Process automation
Machine learning applied to the repetitive decisions inside a workflow, so the routine cases clear themselves and people see only the ones that need judgement.
We build machine learning into the systems a business already runs on, so a model changes a decision rather than sitting in a notebook. Prototype quickly, prove it works, then scale it into the enterprise.
Six areas where machine learning reliably pays for itself, and where we have done the work before.
Machine learning applied to the repetitive decisions inside a workflow, so the routine cases clear themselves and people see only the ones that need judgement.
Turning operational data into something a decision can rest on, including the unglamorous work of getting it clean and consistent first.
Automated model selection and tuning, so a working baseline arrives in days and the argument moves on to whether it is useful.
Trend and demand models built on your own history, with the uncertainty stated rather than hidden behind a single number.
Reading documents, images and free text that no one has time to process by hand, with review kept in the loop.
Applications built for clinical and life sciences settings, where evidence and traceability matter as much as accuracy.
Applying advanced analysis and logic-based techniques, machine learning among them, to interpret events, support and automate decisions, and then act on them.
That definition rules a lot out. A model that produces an interesting number but changes nothing about how the work runs has not finished the job. We are interested in the part after the model.
How we workSame company, two front doors, depending on what you need.
This site. Machine learning and analytics systems built for automation, forecasting and decision support across industries.
See capabilities →techworkslab.com covers pharma drug development through to clinical submissions, CDISC standards and validated reporting.
Visit techworkslab.com →We will say plainly whether machine learning is the right tool for it.
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