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Join other Pharma & Healthcare leaders and stay ahead of the competition with AI solutions.

Pharma & Healthcare State-of-the-Practice

Mariusz Gralewski

Mariusz Gralewski, CEO at DocPlanner

“We engaged deepsense.ai for an AI Advisory engagement with the aim of reviewing and enhancing our AI capabilities and practices. From the outset, the deepsense.ai team demonstrated exceptional technical proficiency. They quickly developed a thorough understanding of our business context and objectives, giving us confidence that they could be a trusted partner in achieving our aspiration at DocPlanner of becoming an AI leader in the healthcare sector. deepsense.ai was adept at identifying practical quick-win improvements in our AI operations, providing guidance for our long-term investment priorities in the AI domain and ensuring a thorough transfer of knowledge to our internal AI team throughout the engagement. Overall, our experience with deepsense.ai exceeded our high expectations. Their expertise and collaborative spirit are top-notch, and we highly recommend deepsense.ai as a valuable partner in the AI journey to any product-based technology business with high aspirations and robust technical rigor.”

Burkhard Boeckem

Burkhard Boeckem, CTO at Hexagon AB

“deepsense.ai helps us discover new scenarios and optimise our products under various conditions. For example, in one of our projects we developed a 3D facial reconstruction device capable of detailed skin analysis. deepsense.ai contributed to the elements requiring artificial intelligence for it. The final implementation involved accurately identifying key points, correctly segmenting facial areas, and detecting wrinkle lines and their estimated severity. Our collaboration shows how to apply cutting-edge AI in niche markets and industries where we seek a competitive advantage. We share efforts in our innovative approach, which differentiates us from peers and startups, embodying our belief that it’s better to disrupt ourselves than to be disrupted by the competition. We look forward to continuing our collaboration with deepsense.ai in the future.”

Ned Taleb

Ned Taleb, Co-Founder & CEO at B-Yond

“We have successfully partnered with deepsense.ai on multiple R&D projects. The deepsense.ai team was able to effectively partner and work hand-in-hand with our development team, complementing our domain knowledge with deep expertise in AI/ML and predictive analytics. Their professionalism and proficiency in data science made them an ideal partner for us, so we wish to continue our collaboration in the future.”

Tom Bianculli

Tom Bianculli, CTO at Zebra Technologies

“At Zebra Technologies, we’ve had the pleasure of collaborating with deepsense.ai across a variety of AI-related engagements. One particular example where deepsense.ai’s expertise really stood out was their involvement in the development of our GenAI-powered frontline worker digital assistant. The solution integrated a diverse set of data sources, providing assistance to frontline employees with relevant responses in their moment of need. While working with Zebra teams, deepsense.ai has consistently demonstrated a strong technical capability, coupled with a proactive approach, an unwavering commitment to quality and delivering what they promise. Their dedication to our success has made them an invaluable partner in our journey. We look forward to our continued partnership going forward.”

Brian S. Raymond

Brian S. Raymond, Founder & CEO at Unstructured

“At Unstructured, we have been delighted to partner with deepsense.ai, a collaboration that has significantly accelerated the development across our Product Roadmap. Specializing in the complex domain of unstructured ETL for RAG, deepsense.ai has matched our technical intensity and contributed across various functional areas.”

Bill Salak

Bill Salak, CTO & SVP Operations at Brainly

“deepsense.ai has been a dependable and high-quality partner to Brainly’s AI research, development, and operations efforts over the past 3 years. deepsense.ai professionals work side-by-side with our in-house teams, contributing to the development of significant projects. Their commitment to both technical excellence and teamwork has been evident in everything from daily operations to our most complex challenges. Their team has integrated seamlessly with our in-house teams, bringing top-tier talent and a collaborative spirit that drives innovation. We are grateful for this partnership and confident in their professionalism and expertise. Brainly highly recommends deepsense.ai for anyone seeking a team that brings both skill and a true collaborative spirit to the table.”

Proven Technical Expertise

Access to Beta Features

Contributing to Applied AI Research

Thought Leadership

Machine Learning for In-Silico Research

Revolutionize drug discovery and development with AI-powered solutions for in-silico research, clinical trials, and manufacturing. Experience innovation at every stage of the pharmaceutical lifecycle.

Chemical Reaction Prediction

Leveraging LLMs (with using e.g., Graphormer, SMILES) for predicting reaction outcomes.

Hypothesis Generation

AI-driven insights from electronic lab notebooks (ELNs) using Retrieval-Augmented Generation (RAG).

Optimization of Graphormer Models

Improvements at high levels (algorithm optimization) and low levels (CUDA/kernel improvements).

Custom Dashboards

Streamlined interfaces for ML teams using tools like Dash and Plotly for in-silico experiments.

Clinical Trials Optimization

Site Selection

AI models for identifying ideal clinical trial locations based on patient diversity, accessibility, and infrastructure.

Patient Recruitment and Retention

Predictive analytics to identify and engage suitable participants.

Federated Learning Models

Collaboration across healthcare facilities without sharing sensitive data.

Supply Chain and Manufacturing

Predictive Analytics

AI-driven predictive analytics for optimizing production lines and managing stock levels.

Digital Twins

Digital Twins for pharma manufacturing plants to test and enhance operational efficiencies.

Clinical Applications

AI Diagnosis Tools

Suggest diagnoses or additional questions during consultations.

Medical Imaging Analysis
  • Computer Vision Models: Detect abnormalities in CT, MRI, X-rays, and microscopic images (e.g., cancer detection, diabetic retinopathy).
  • Digital Pathology: Identify tissue anomalies in biopsy slides using DL algorithms.
  • Dental X-Ray Detection: Automate identification of cavities, root infections, or orthodontic issues.
AI Assistant for Doctors
  • Administrative task reduction through automated note-taking and documentation.
  • Tools like AI Drug Tool: Cross-check drug recommendations and contraindications.

Patient Interaction

Virtual Healthcare Assistants
  • Answer medical queries: “What does hypothyroidism mean?” 
  • Provide actionable post-visit recommendations or reminders.
Remote Monitoring
  • AI solutions integrated into smart wearables for tracking vital signs (e.g., heart rate, falls, or sleep patterns).
  • Predictive Analytics: Alert healthcare providers about potential medical emergencies based on data trends.
Telemedicine Enhancements

AI tools for real-time life sign measurements and symptom tracking during virtual consultations.

AI-Powered Chatbots
  • Patient engagement before and after visits.
  • Example: Pre-visit anamnesis tools to collect patient history or triage queries.
  • Comprehensive databases for doctors and patients alike, with personalized recommendations.

Operational Efficiency in Healthcare

Predictive Maintenance of Medical Devices

AI-driven analytics for scheduling repairs and reducing equipment downtime.

Health Clinics Assistance
  • Reduce appointment no-shows by automating reminders and confirmations.
  • Automate patient communication to manage high call volumes effectively.
Structured Personal EHR (Electronic Health Records)

AI-generated summaries and insights from patient records for better continuity of care.

Cost Optimization Tools

Streamline patient workflows and reduce redundancies in administrative processes.

AI for Research

AI Research Assistant for Doctors

Summarize medical journals, recommend trending papers, and provide insights into the latest breakthroughs in specialized fields.

Test Result Analysis

AI models to analyze ultrasound (USG) or other diagnostic tests and present actionable insights.

Procedure and Information Aggregation

Provide support in adhering to procedures and guidance across various markets through regular reviews and summaries of diverse data sources.

How Our Clients Usually Start Working with Us

AI Discovery Workshop

1-2 days
Generate and evaluate ideas for AI use cases in terms of business potential and technical feasibility

Proof of Concept Project (PoC)

2-4 weeks
Validate an idea for an AI-based functionality or solution

AI Advisory Project

2-4 weeks
Improve how AI is implemented and operated following
a prioritized action plan based on an assessment of current practices and tools

AI Team Augmentation

Varies
Complement your team with advanced AI capabilities
to benefit from expert AI guidance or additional implementation bandwidth