Why work with DataHow?
14/20
of the largest biopharma organisations as customers
100's
of industrial processes anaylsed using DataHow technology
1
unique solution
Trusted Technology Partners of
We'll introduce DataHowLab and highlight industry uses cases performed with the software.
Explain where you would like to improve. Our team has helped unlock the true process potential for so many of our customers.
Would you like to feel the impact on your operation? Our 1-month PoVs run our software on your process to make an impact in just 30 days.
DataHowLab: AI for Bioprocessing
DataHowLab has been developed to solve for the unique conditions and challenges of bioprocessing:
DataHowLab exploits its technologies to serve the objectives of process scientists. Its structured digital development approach ensures consistency and optimal results.
Highlighted Industrial Cases
Through its enhanced AI-insight capabilities, DataHowLab was able to improve CQA understanding while requiring 3x fewer experiments.
Transfer insights across products and scales with AI, reducing experimental effort and transforming historical data into a development asset.
DataHowLab was able to substantially increase titer by proposing a unique feeding strategy that the customer had not considered.
Mammalian - Microbial - C> - mRNA
Upstream - Downstream - Spectroscopy
Preclinical - Phase 1-3 - Commercial
Screening - Optimization - Scale-up - Characterization - Qualifcation & Validation - Monitor & Control
14/20
of the largest biopharma organisations as customers
100's
of industrial processes anaylsed using DataHow technology
1
unique solution
DataHowLab is unique in the market from both a technological and functional perspective. Many bioprocess data analytics leverage statistical models, which are effective but which have limitations when compared to DataHows hybrid AI-modeling engines.
Other tools take a mechanistic approach, relying on formulas which describe known dynamics but which struggle in domains where knowledge incomplete, such as in the complex domain of bioprocessing.
Applying a purely machine learning approach is impractical, nor economically viable, given the high cost of generating experimental data and the high volume of data required to bring machine-learning models into their sweet spot.
DataHow's hybrid AI models combine the best of both words - combining a mechanistic backbone (which decribes and codes for known process dynamics), with data driven models which learn from the smaller area of unknowns.
DataHowLab is also unique in how it delivers these technologies. More than a tool, DataHowLabs user friendly, structured environment provides a structured approach to applying digital process analytics and development.
DataHowLab has been designed for process scientists - not data scientists. Advanced technologies and AI-models serve the needs and goals of process development within an environment that supports users with workflows, automations, and intuitive UX.
The software more is than a technology tool kit, its a powerful digital process development solution which provides a clear digital development framework to maximise process design, analysis, and optimization.
DataHowLab empowers scientists with their own data.
DataHowLabs technologies and applications have been developed through close collaborations with industry for over a decade.
DataHow has analysed hundreds of process data sets and deeply understands the needs and challenges that bioprocessing faces. The software is the result of this - a process agnostic software which applies advanced technologies to meet the challenges of bioprocessing in a way that makes sense to bioprocess professionals.
To see the impact of the software on your own process, we offer a 1-month Proof of Value where we will analyse your process with DataHowLab towards a defined goal.
While the impact will vary from operation to operation, feedback and results from our customers speak for themselves:
There is only one way to find out! Speak to our team and start a 1-month proof of value!
Realise your process potential