Success Stories

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Fusemachine Client Dotscience Logo

“Fusemachines accelerated our AI Platform time to market substantially”

Luke Marsden - CEO

Dotscience is a company, SaaS platform, and enterprise product based in the United Kingdom. The Dotscience platform allows for AI to be reproducible, accountable, collaborative, and continuously delivered, by enabling Data Scientists and Machine Learning engineers to work together in a single environment.
Challenge
With an aggressive go to market strategy, Dotscience was limited by the number of available resources capable of efficiently testing their platform. Dotscience knew that they needed to scale their team while remaining true to their product roadmap, in a timely and cost-effective manner.
The Solution
In two weeks, Fusemachines was able to build a team of Data Scientists to begin testing and enhancing the Dotscience platform. At 1/3 of the rate of on-site consultants, Fusemachines saved Dotscience $48,000 annually per engineer, enabling Dotscience to confidently take their product to market.
Fusemachine Client Pushnnami Logo

Ad marketing agencies aim to increase marketing conversions by sending targeted ads to specific users.

A leading web based ad agency and notification company needed to develop a system that would understand user interests, cluster users into segments, predict ad copy, and select a device and time for engaging the user.
Challenge
The data they were able to collect didn’t allow them to identify all of the features needed and didn’t process it correctly to predict the best outcomes.
The Solution
Fusemachines created and deployed a neural network based factorization model that trains itself on live data and tunes itself automatically multiple times a day based on live performance. Fusemachines also developed critical data infrastructure for their ML pipeline reducing the time and resources needed to train models, iterate segments and improve ROI.
Fusemachine Client Butterfly Logo

“Fusemachines’ AI as a Service has consistently provided us with great results, with a talented and dependable cohort of engineers that collaborates well with our data science team generating tangible benefits, and we look forward to continuing this alliance.”

CEO, Butterfly.ai

Butterfly.ai is a web platform that focuses on providing leadership coaching to managers by using feedback from employees regardless of where they’re located. Managers are able to receive customized tools based on the evaluation they get from surveys sent out at high frequency to employees.
Challenge
Butterfly.ai was growing quickly but found themselves limited in their ability to address new client demands due to bandwidth constraints. In order to develop customized platform experiences, new features, and analytics for their new clients, the Butterfly.ai team realized it was crucial to quickly scale up their engineering team with resources they could trust. Finding these resources was not easy.
The Solution

Butterfly.ai hired Fusemachines and in two weeks they had a team of engineers with the skillset they were looking for. Adding 2 Fusemachines engineers initially, and one shortly after, Fusemachines made it easy for them to scale their team on demand.

As Butterfly.ai continues to grow, we are looking to foster our partnership with Fusemachines through the qualified talent to enhance our products. The flexible option to add and reduce engineers allowed us to have more manpower when we had to ramp up our product development.

Fusemachine Client Therapeutics Logo

The research scientists needed to identify proteins that have rejuvenating properties for treatment of a specific disease called Sarcopenia, a special form of Myopathy.

A US based biotech company aims to increase the quality of life for patients with degenerative diseases by treating and extending their healthspan. Their research focuses on discovering therapeutic proteins that help cells live longer and regenerate.
Challenge
  • The client was striving to lower the cost and time of running experiments.
  • A substantial amount of data needed to be analyzed quickly.
The Solution
Within 3 weeks of meeting, Fusemachines was able to onboard 1 PhD and 3 engineers to work with the client. The Data Scientist and engineers had instant communication during the integration process with the client’s internal team and project leaders. They worked to help solve protein prediction as well as extract information from research documents using natural language processing (NLP) to help corroborate the predictions.
As a second step, the client wanted to use NLP to comb through scientific journals and solidify the protein search based on the specific disease being analyzed and different predicted proteins. This would help speed up the process by proving the alignment of contemporary scientific literature with the findings.
To successfully predict proteins, Fuse engineers pre-processed the data to make sure there weren’t any inherent biases. To tackle the issue of dealing with unlabeled data, engineers used techniques to produce pseudo labels which help the AI model make better predictions. In short, this solution involved taking the “unknown” proteins, making predictions on them, then taking the proteins that have been predicted positive and feeding them into the model again for training, but this time as labeled examples.