Faster research and development
AI finds structure in large volumes of collected data across medical care, chemical research and discovery, and turns it into information scientists can use.
Industries · Pharmaceuticals
Artificial Intelligence in Pharma from research data to usable insight
AI is changing pharmaceutical research and care. It turns large collections of research data into usable information, gives analysts fast access to chemical databases and takes over administrative and regulatory routines. We build these systems for pharmaceutical companies, from R&D prototypes to production tools.
- Research data turned into usable information
- Faster analysis of compound databases
- Less manual regulatory and admin work
- 57% of our team hold a PhD
- German company, working under the GDPR
Discuss your project
5.0 18 reviews on Clutch Go Global Awards winner
Trusted by enterprises, scale-ups and non-profits
What you gain
What AI changes in pharma
The areas where AI already supports pharmaceutical research, operations and care.
Connected information
AI can link production, logistics and care information, so drug care data is managed across the organisation rather than in silos.
Routine work automated
Administrative and regulatory tasks that absorb pharmacists’ time can be handled by intelligent models, leaving experts to review and act on the results.
What we deliver
AI solutions for pharmaceutical companies
Use cases from research to operations, including projects we have delivered for pharmaceutical clients.
- 01
AI in pharmaceutical R&D
Research and development projects that apply machine learning to medical and chemical data and evaluate state-of-the-art approaches for your problem.
- 02
Generic drug processes
Machine learning tools that give drug analysts rapid access to large databases of chemical elements and compounds and surface relationships that are hard to find manually.
- 03
Drug care information management
Systems that integrate production, logistics and care information from different data sources into one managed view.
- 04
Medical image translation
Generative adversarial networks translate tissue images between stain domains, so an existing disease classifier can be reused across reagents.
- 05
Pill detection and counting
Computer vision that detects, classifies and counts pills by National Drug Code to automate pharmaceutical inventory processes.
- 06
Skill gap analytics
NLP that extracts skills from CVs, role descriptions and reviews to find skill gaps and succession risks in large research organisations.
Not sure which of these you need? Describe the problem and we will recommend the approach.
Discuss your projectCase studies
All case studiesResults we delivered for our clients
Healthcare · Computer Vision, Generative AI
Medical image translation between tissue stains for a pharma company
Reusedone disease classifier across stain reagents
An AI component that translates histological images from one stain domain to another, so existing software and machine learning models can be reused across all reagents.
Read the case study
Pill detection and counting for a healthcare technology provider
99.9% precision, recall and F1 across all predictions
A computer vision system that identifies, classifies and counts pharmaceutical pills by National Drug Code (NDC) from photos, replacing error-prone manual counting.
NLP skill gap analytics for a science and technology company
~10,000 man-hours saved
An interactive NLP tool that assesses employees' skills, compares skill profiles and identifies skill gaps between business-critical staff and their potential successors.
Eye fat and muscle volume estimation from MRI scans
Automated fat and muscle segmentation in every MRI slice
A deep learning model for an ophthalmology centre that segments fat and muscle tissue in MRI orbit scans, estimates their volume and supports before-and-after comparisons.
Why AI Superior
Research-grade AI, delivered like a product company
Many vendors can build a demo. Making AI work reliably on real data, inside real business processes, is the hard part, and it needs a different kind of team.
01
A research-led team
AI Superior was founded in 2019 by AI researchers with published papers and patents, and the whole team works to that research standard.
- PhD57%
- MSc28%
- BSc15%
02
Built to reach production
Every project is de-risked in stages, with a go/no-go decision at each milestone, so budget only goes into approaches that have worked on your data.
4× our proof-of-concept success rate compared with the market average
03
A complete team from day one
Strategy, models and software are designed by one team, so nothing gets lost in hand-overs between vendors.
- Product AI product owner, business analyst, project manager
- Data Data scientists, ML engineers, BI analyst
- Development Software engineers, QA, DevOps
04
Your data stays under control
When data must not leave your organisation, we build on private, self-hosted models instead of third-party AI services.
- GDPR, German company
- Self-hosted LLMs
- Transparent methods
Our team
The experts behind your project
Your project is led by senior data scientists and engineers with research backgrounds, supported by our product and development teams.
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Dr. Sergey Sukhanov Chief Data Scientist PhD in Machine Learning, data scientist since 2013. Publications, patents and 30+ implemented AI use cases; several IEEE awards.
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Dr. Ivan Tankoyeu Chief Data Officer PhD in Computer Science. Recognised by the IEEE GRSS Society, winner of the Global AI Hackathon 2017, Kaggle TOP500 data scientist (2014).
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Sergio Frayle, M.Sc. Lead Data Scientist Master’s in AI, Pattern Recognition and Digital Imaging; 7+ years in image processing and computer vision, with publications.
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Enrique Fernández, M.Sc. Senior ML Expert Master’s in Robotics and Mechatronics; builds and deploys complex AI solutions, particularly in computer vision and NLP.
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Peter Luck, M.Sc. AI Consultant Master’s in Business Engineering and Computer Science; product and project manager for machine-learning products.
Client reviews
5.0 Rated 5.0 by the people we build for
18 verified reviews on Clutch
“They delivered an extra module that we were not expecting but turned out to be very useful.”
Research and custom software development applying generative adversarial networks for a pharmaceutical company with 5,000+ employees.
“I appreciate their approach, expertise, and the quality of information they provide.”
AI and machine learning consulting to identify the right use cases for a web design and graphics company.
“They are not only great theoretical experts, but also deliver with their awesome hands-on skills.”
How we work
A staged AI project life cycle
Every stage ends with a result you can check, and you decide on the next one only after seeing the previous one work. You keep control of scope and budget.
- Estimate before you commitYou see scope and expected results before the build begins.
- Go/no-go after every stageEach stage ends with a result you can check and a decision on the next step.
- Risks reported openlyWe share risks and opportunities as soon as the analysis shows them.
- Go / no-go decision
Discovery
We work through the business problem with your team and define the direction of the solution.
You get: Scope, approach and a high-level estimate of effort and expected results
- Go / no-go decision
Data and feasibility
We get to know your team and data and check whether AI is the right tool for this problem.
You get: A data assessment and a clear feasibility verdict before any build starts
- Go / no-go decision
Proof of concept / MVP
We start small, using the data already available, to test the solution in practice.
You get: Measured results on your own data and a basis for the investment decision
- Go / no-go decision
Integration and scaling
We integrate the solution into your existing systems, fine-tune the models and adjust them where needed.
You get: A solution running inside your processes, compatible with your data and systems
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Evaluation
We review the results with you and make sure they are read correctly.
You get: What the solution delivered and where to improve next
Awards and recognition
Ranked among the top AI companies
Recognised by international business awards and by independent B2B platforms that rank companies on verified client reviews.
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Go Global Awards Winner 2021 · International Trade Council -
Best Data Science & AI Service Provider, Europe 2021 · German Business Awards -
Top Artificial Intelligence Company 2023 · Clutch -
Top Machine Learning Company 2023 · Clutch -
Clutch Champion Fall 2023 · Clutch -
Clutch Global Fall 2023 · Clutch -
Top BI & Big Data Company Germany 2023 · Clutch -
Top IT Services Company Germany 2023 · Clutch -
Top Artificial Intelligence Companies 2023 · TrueFirms -
Top Machine Learning Companies 2021 · Techreviewer -
Most Reviewed IT Services Companies Germany · The Manifest
Where can AI help a pharmaceutical company today?
In research and development, where it turns large collected data sets into usable information; in generic drug processes, where it speeds up work with chemical compound databases; and in operations, where it automates administrative and regulatory routines and connects production, logistics and care data.
Can AI reuse models we have already trained?
Often, yes. For a pharmaceutical company we used generative adversarial networks to translate tissue images stained with one reagent into the domain of another, so an existing disease detection classifier could be reused. An interactive tool let the team validate the generated images, cutting the time and cost of processing new stain types.
Can AI be used in clinical settings?
Its use in clinical situations is still limited. We focus on research, analysis and operational use cases where AI supports experts, and we are clear about where human judgement remains essential.
Do you only work on lab and research data?
No. We have also delivered NLP analytics for a science and technology company with tens of thousands of employees, identifying skill gaps and successors for business-critical roles and saving around 10,000 person-hours.
How do you manage the uncertainty of research projects?
Our AI project lifecycle is adapted from software development standards and accounts for the scientific challenges of machine learning. Each phase has its own goals and quality criteria that must be met before the next stage begins.
Start your project
Tell us what you want AI to do for your business
Share a few details and our AI team will take it from there. Here is what happens next:
- We review your request and reply by email.
- A call with an AI expert to understand your problem, data and goals.
- A clear recommendation: the approach we suggest and a high-level estimate.
Prefer to pick a time yourself?
Schedule a call