deepsense.ai

From our daily work with global pharma companies, we see how AI can create value across the value chain. 👇 For teams still exploring where AI can deliver operational or product value, here are some concrete applications we’re already seeing: 1. Drug discovery & preclinical R&D Multimodal LLMs can help researchers work across molecular structures, scientific literature, and experimental data — accelerating in-silico analysis. → One implementation delivered a 5𝐱 𝐢𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭 𝐢𝐧 𝐢𝐧-𝐬𝐢𝐥𝐢𝐜𝐨 𝐝𝐫𝐮𝐠 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲 workflows. 2. Clinical study design LLMs can support protocol development by combining internal knowledge with regulatory and methodological guidelines. → In one project, guideline-aware generation helped 𝐩𝐫𝐨𝐭𝐨𝐜𝐨𝐥 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐟𝐫𝐨𝐦 𝐦𝐨𝐧𝐭𝐡𝐬 𝐭𝐨𝐰𝐚𝐫𝐝 𝐰𝐞𝐞𝐤𝐬, while keeping ENCePP requirements in the workflow. 3. Clinical trial planning & site selection Historical trial data, real-world data, epidemiology, and site performance can be combined to improve feasibility and site selection. → In retrospective analysis, 90% 𝐨𝐟 𝐀𝐈-𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐞𝐝 𝐬𝐢𝐭𝐞𝐬 𝐨𝐮𝐭𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐞𝐝 traditional selections in the US market. Read more use cases here: 👉 https://lnkd.in/dfdPRkJD 4. Medical and scientific intelligence Physicians and medical teams often need to extract answers from thousands of publications. → An AI research assistant now helps 2 𝐦𝐢𝐥𝐥𝐢𝐨𝐧 𝐩𝐡𝐲𝐬𝐢𝐜𝐢𝐚𝐧𝐬 𝐚𝐜𝐫𝐨𝐬𝐬 13 𝐜𝐨𝐮𝐧𝐭𝐫𝐢𝐞𝐬 work with insights from 3,000+ curated medical sources. 5. Medical, legal and regulatory content workflows Pharma content teams still spend significant time extracting, reconciling, and preparing information for review. → OCR, multimodal models, and agentic workflows can automate this into 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝, 𝐌𝐋𝐑-𝐚𝐥𝐢𝐠𝐧𝐞𝐝 𝐨𝐮𝐭𝐩𝐮𝐭𝐬 with built-in traceability. 6. Market access & reimbursement Pricing and reimbursement teams must navigate large volumes of evidence, submissions, and policy documents. → Long-context AI copilots can synthesize that material and help teams prepare 𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐞𝐯𝐢𝐝𝐞𝐧𝐜𝐞 𝐚𝐧𝐝 𝐚𝐫𝐠𝐮𝐦𝐞𝐧𝐭𝐬 for negotiations with public healthcare authorities. 7. Post-launch and patient-access operations AI can reduce manual work in compassionate-use information management, where accuracy and source verification are critical. → One workflow reduced source-verification work 𝐟𝐫𝐨𝐦 𝐝𝐚𝐲𝐬 𝐭𝐨 𝐚𝐫𝐨𝐮𝐧𝐝 2 𝐡𝐨𝐮𝐫𝐬, while producing detailed reports on document changes. For pharma AI leaders building their roadmap, these use cases can be a useful starting point for identifying where AI can move from experimentation to measurable operational value. Read more use cases here: 👉 https://lnkd.in/dfdPRkJD

Szymon Komorowski, Life Sciences & Healthcare Advisor at deepsense.ai, shares practical lessons from the AWS AI Health Leaders Forum in Zurich. Worth reading for AI leaders, and decision-makers in healthcare and pharma who want to understand what production AI actually requires: agentic workflows, evaluation, trusted data access, governance, and measurable ROI. Read the full article: 👉 https://lnkd.in/dNNty3GC

Clinical trial protocol and feasibility work 𝐥𝐞𝐚𝐯𝐞 𝐥𝐢𝐭𝐭𝐥𝐞 𝐫𝐨𝐨𝐦 𝐟𝐨𝐫 𝐠𝐞𝐧𝐞𝐫𝐢𝐜 𝐀𝐈. We’ve seen that repeatedly across projects delivered for global pharma companies. The useful signals sit across historical protocols, amendments, site performance, enrollment data, SOPs, feasibility inputs, internal systems, and years of operational experience. That experience shaped our new 𝐂𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐓𝐫𝐢𝐚𝐥 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥 & 𝐅𝐞𝐚𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 practice at deepsense.ai. We built the approach from: → multiple AI projects delivered for global pharma and healthcare companies → almost 20 years of Life Sciences and pharma experience from our industry advisor Across these projects, the same practical needs kept coming up: 𝐨𝐮𝐭𝐩𝐮𝐭𝐬 𝐡𝐚𝐝 𝐭𝐨 𝐛𝐞 𝐬𝐨𝐮𝐫𝐜𝐞-𝐛𝐚𝐜𝐤𝐞𝐝 𝐚𝐧𝐝 𝐫𝐞𝐯𝐢𝐞𝐰𝐚𝐛𝐥𝐞 𝐛𝐲 𝐜𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐞𝐱𝐩𝐞𝐫𝐭𝐬, historical trial knowledge had to be easy to reuse, and the workflow had to 𝐟𝐢𝐭 𝐞𝐱𝐢𝐬𝐭𝐢𝐧𝐠 𝐜𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐬𝐲𝐬𝐭𝐞𝐦𝐬. Governance, traceability, and validation also had to be built in from the start. We turned those recurring requirements into a 𝐫𝐞𝐩𝐞𝐚𝐭𝐚𝐛𝐥𝐞 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐲 𝐩𝐫𝐨𝐜𝐞𝐬𝐬. It covers use cases such as: 🔵 protocol complexity and risk review 🔵 amendment risk flags 🔵 inclusion/exclusion criteria analysis 🔵 feasibility and enrollment risk assessment 🔵 historical trial comparison 🔵 site selection support 🔵 source citations, confidence levels, approval steps, and audit trails The result is a private 𝐀𝐈 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐥𝐚𝐲𝐞𝐫 𝐛𝐮𝐢𝐥𝐭 𝐚𝐫𝐨𝐮𝐧𝐝 𝐭𝐡𝐞 𝐜𝐨𝐦𝐩𝐚𝐧𝐲’𝐬 𝐨𝐰𝐧 𝐜𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐝𝐚𝐭𝐚, 𝐬𝐲𝐬𝐭𝐞𝐦𝐬, 𝐚𝐧𝐝 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬. The goal is simple: spot protocol and feasibility risks earlier, make better use of internal trial knowledge, and avoid rework that can delay the study. Learn more about this approach here: 👉 https://lnkd.in/dge4bGMa

For a manufacturing leader with 70,000+ 𝐞𝐦𝐩𝐥𝐨𝐲𝐞𝐞𝐬 and $40B+ in annual revenue, the challenge was clear: move from separate supply chain agents to a reusable, scalable platform. In a 6-week engagement, we helped define the architecture, evaluation, observability, and integration approach needed for production scale. The results included 2–3x faster Time to First Token and 75% 𝐟𝐞𝐰𝐞𝐫 𝐭𝐨𝐨𝐥 𝐜𝐚𝐥𝐥𝐬 𝐩𝐞𝐫 𝐫𝐞𝐪𝐮𝐞𝐬𝐭. 👋 Swipe through the carousel for the key lessons and results. For the full architecture, process, and outcomes, read the case study: 👉 https://lnkd.in/dMA_2B83

AI agents that only retrieve information create one type of risk. But agents that can 𝐦𝐨𝐝𝐢𝐟𝐲 𝐚 𝐩𝐚𝐭𝐢𝐞𝐧𝐭 𝐫𝐞𝐜𝐨𝐫𝐝, execute a trade, or submit a file create a very different one. In our recorded webinar with Anthropic, we break down how to design agentic AI for regulated production environments. You’ll learn: → where MCP fits, and where it doesn’t → 6 controls for safer agentic systems → how to move beyond RAG without losing governance → what teams should prioritize before production approval Watch the session: 👉 https://lnkd.in/dfr45bt5

AI can support far more than protocol drafting. Its real value may lie in 𝐬𝐩𝐨𝐭𝐭𝐢𝐧𝐠 𝐭𝐫𝐢𝐚𝐥 𝐫𝐢𝐬𝐤𝐬 before protocol lock. In a new interview, Szymon Komorowski, our Life Sciences & Healthcare Advisor, explains 𝐡𝐨𝐰 𝐩𝐡𝐚𝐫𝐦𝐚 𝐭𝐞𝐚𝐦𝐬 𝐜𝐚𝐧 𝐮𝐬𝐞 𝐀𝐈 𝐭𝐨 𝐫𝐞𝐯𝐢𝐞𝐰 𝐩𝐫𝐨𝐭𝐨𝐜𝐨𝐥 𝐜𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲, test feasibility assumptions and identify factors that may lead to delays, recruitment problems or costly amendments. Read it here: 👉 https://lnkd.in/dznAWtbD The interview also marks a new chapter in our Clinical Trials practice: Protocol and Feasibility Intelligence. Together with Szymon, who brings experience from work with global pharma companies, we are developing a repeatable process for custom solutions. More about our solution here: 👉 https://lnkd.in/dYCwm-n5 #ClinicalTrials #PharmaAI #ClinicalDevelopment #LifeSciences

Your best AI coding agent may be the one that writes 𝐧𝐨 𝐜𝐨𝐝𝐞. For high-risk systems, the more useful role may be an agent that knows the architecture well enough to spot drift, broken permission paths, unsafe migrations, or a rollback that closes the UI but leaves the API exposed. That is one of the sharper ideas in Mateusz Kuprowski’s (Senior MLE) new article on the five control planes of agentic AI infrastructure: 👉 https://lnkd.in/dvRdQYpY #AgenticAI #AIInfrastructure #SoftwareEngineering

A global industrial manufacturer with 70,000+ 𝐞𝐦𝐩𝐥𝐨𝐲𝐞𝐞𝐬 needed to move from isolated supply-chain agents to a reusable enterprise platform. Explore our comprehensive case study: 👉 https://lnkd.in/dY8hH-wi What did we do? In six weeks, deepsense.ai helped define the path from prototypes to: → modular tools, skills, and knowledge → evaluation and observability → event-driven integrations → scalable governance → production The results: • 2–3× faster time to first token • 20% faster end-to-end responses • 75% fewer tool calls per question • 50% fewer output tokens • Hundreds of production requests handled daily The important lesson our client stated is that scaling agents is not primarily about adding more use cases, but about creating an architecture in which new agents, data sources, and capabilities can be added without rebuilding the core platform each time. The full case study covers the 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬, 𝐨𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤, and implementation roadmap behind the transition. Explore the case study: 👉 https://lnkd.in/dY8hH-wi #AgenticAI #EnterpriseAI #AIArchitecture

Healthcare AI is starting to produce numbers that can survive clinical scrutiny. ✅ • 96.3% of 3,622 chart extractions accepted by physicians • Zero hallucinated values • Clinical review reduced from 2 hours to 2 minutes • AI MVP development compressed from 9 months to 3 • +$2.1 million in reported clinical-trial screening efficiency and patient accrual These are front-runner results, not industry averages. That is precisely why they matter. They show what becomes possible when 𝐡𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬 𝐬𝐭𝐨𝐩 𝐭𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐀𝐈 𝐚𝐬 𝐚 𝐬𝐭𝐚𝐧𝐝𝐚𝐥𝐨𝐧𝐞 𝐚𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭 and start designing it into real clinical, operational, and pharma workflows. Read more about it here: 👉 https://lnkd.in/dirVa_9G The numbers above are compelling. But the most valuable part of this article is the production reality behind them: → How agents can work across institutional boundaries while sensitive patient data stays local → Why evaluation, traceability and “no-answer” behavior must be part of the architecture → Where generic RAG falls short in life sciences → How governance and data sovereignty can enable deployment rather than delay it → Why the best AI use cases begin with a measurable operational bottleneck The analysis was written by Szymon Komorowski, Life Sciences & Healthcare Advisor at deepsense.ai, following the Amazon Web Services (AWS) AI Health Leaders Forum in Zurich. Read it for a grounded view of where healthcare AI agents are already delivering measurable value, and what must be built around them to make that value reliable in production. 👉 https://lnkd.in/dirVa_9G #HealthcareAI #LifeSciences #AgenticAI #EnterpriseAI

When we talk to enterprise experts about their fears about adopting AI, we often hear that 𝐢𝐧𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐲 𝐥𝐨𝐰𝐞𝐫𝐬 𝐭𝐫𝐮𝐬𝐭 𝐚𝐧𝐝 𝐝𝐞𝐥𝐚𝐲𝐬 𝐬𝐜𝐚𝐥𝐢𝐧𝐠. 🛑 A model that succeeds once but fails under similar conditions creates hidden costs, increases verification effort, and makes production deployment far riskier than benchmark scores suggest. That's why 𝐛𝐞𝐭𝐭𝐞𝐫 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐦𝐚𝐭𝐭𝐞𝐫𝐬. At deepsense.ai, we're advancing 𝐬𝐲𝐧𝐭𝐡𝐞𝐭𝐢𝐜 𝐝𝐚𝐭𝐚𝐬𝐞𝐭𝐬 𝐚𝐧𝐝 𝐛𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐬 that measure not only model capability, but also repeatability and real business utility. Learn more: 📖 Why "Average" AI Isn't Enough: Introducing Business Utility → https://lnkd.in/dGDWdDCt 📊 Latest EDA Benchmark Leaderboard → https://lnkd.in/dTB6iyhD 🔬 Custom synthetic datasets for LLM & VLM evaluation and training → https://lnkd.in/dXE7pg5C