deepsense.ai

A clinical trial protocol can look promising on paper. But how well does it hold up against real-world enrollment conditions and competing studies? Before a trial moves forward, teams need to know whether evidence supports their assumptions. Our Clinical Trial Protocol & Feasibility Intelligence Demo shows how a single protocol synopsis can become a structured, evidence-backed review covering: 🔵 Protocol design, 🔵 Enrollment feasibility, 🔵 Trial timelines, 🔵Amendment risks, 🔵Competitive landscape. The workflow brings these findings into one report, supported by clinical trial registry data, with the option to explore the evidence further through an interactive Q&A. For clinical development and trial operations teams, this creates a more structured way to challenge protocol assumptions before they become costly operational issues. See how the workflow works in our demo: https://lnkd.in/de8KXuH6/?utm_campaign=%255Breach%255DMaincampaign&utm_source=linkedin&utm_medium=paid&utm_id=1206493984&hsa_acc=508184568&hsa_cam=1206493984&hsa_grp=884963314&hsa_ad=1617274074&hsa_net=linkedin&hsa_ver=3 #ClinicalTrials #Pharma #PharmaML #LifeSciences

🧑💻 Accounting workflows spread across PDFs, Excel, emails, and specialist systems, with employees repeatedly transferring and re-entering the same information. Sounds familiar? We faced this challenge. We worked with the client to identify where AI could deliver the most value without disrupting existing systems or removing essential human oversight. In just around a month, we mapped 4 accounting processes, identified 10 automation opportunities, and built 2 working AI automations. One of them reduced reconciliation preparation from hours to approximately 20 minutes. But the most interesting part is how we approached automation despite the integration constraints. Read the full case study to explore the implementation decisions, results, and lessons learned: https://lnkd.in/dt_-hgBz/?utm_campaign=%255Btraffic%255DMaincampaign&utm_source=linkedin&utm_medium=paid&utm_id=1206642164&hsa_acc=508184568&hsa_cam=1206642164&hsa_grp=905118884&hsa_ad=1606109564&hsa_net=linkedin&hsa_ver=3 #AIAutomation #CoPilots #WorkflowAutomation #PE

Today we're launching something special. 🔥 𝐀𝐈 𝐕𝐚𝐥𝐮𝐞 𝐂𝐫𝐞𝐚𝐭𝐢𝐨𝐧 is built for funds that need AI to become a portfolio value lever, not another set of isolated pilots. The story behind this is that across funds and portfolio companies come to us, we continue to see that AI ambition is high, but execution capacity is often limited. Why? 1. Scattered use cases. | 2. No clear ROI. | 3. Too many projects stop at the proof-of-concept stage. That is exactly why we are opening our AI Value Creation offering for PE & VC portfolios, after proving this path with other clients of this type 👉 https://lnkd.in/dsuKHZkw/?utm_campaign=%255Btraffic%255DMaincampaign&utm_source=linkedin&utm_medium=paid&utm_id=1206642164&hsa_acc=508184568&hsa_cam=1206642164&hsa_grp=884912774&hsa_ad=1606012084&hsa_net=linkedin&hsa_ver=3 At deepsense.ai, 𝐰𝐞 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐰𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐢𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭 𝐭𝐞𝐚𝐦𝐬 𝐚𝐧𝐝 𝐩𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐨𝐧 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐀𝐈 𝐢𝐧𝐢𝐭𝐢𝐚𝐭𝐢𝐯𝐞𝐬: - from fund-level research agents, - competitive intelligence systems, - portfolio-company enablement, - AI maturity assessment, - use case prioritization, - and production-grade implementation. One example: for a private equity portfolio platform, we designed an 𝐀𝐈 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐨𝐫 🚀 to help identify where AI could create real business value across multiple portfolio companies. The goal was to help leadership teams decide where AI is worth investment, where it is technically feasible, and what needs to happen for the initiative to move beyond experimentation. For PE and VC funds, this matters because 𝐀𝐈 𝐜𝐚𝐧 𝐛𝐞𝐜𝐨𝐦𝐞 𝐚 𝐫𝐞𝐚𝐥 𝐯𝐚𝐥𝐮𝐞 𝐜𝐫𝐞𝐚𝐭𝐢𝐨𝐧 𝐥𝐞𝐯𝐞𝐫, but only when it is treated as an operating model, not a collection of one-off ideas. Our approach helps funds and portfolio companies: > reduce low-impact experimentation > prioritize initiatives with measurable business potential > align business and technical stakeholders > shorten the path from idea to decision > build reusable AI playbooks across the portfolio > move validated opportunities into production systems We built this offering around what we already see in the field: 𝐟𝐮𝐧𝐝𝐬 𝐧𝐞𝐞𝐝 𝐚 𝐫𝐞𝐩𝐞𝐚𝐭𝐚𝐛𝐥𝐞 𝐰𝐚𝐲 𝐭𝐨 𝐭𝐮𝐫𝐧 𝐀𝐈 𝐩𝐫𝐞𝐬𝐬𝐮𝐫𝐞 𝐢𝐧𝐭𝐨 𝐩𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨-𝐰𝐢𝐝𝐞 𝐢𝐦𝐩𝐚𝐜𝐭. Explore how we help PE & VC companies here: 👉 https://lnkd.in/dsuKHZkw/?utm_campaign=%255Btraffic%255DMaincampaign&utm_source=linkedin&utm_medium=paid&utm_id=1206642164&hsa_acc=508184568&hsa_cam=1206642164&hsa_grp=884912774&hsa_ad=1606012084&hsa_net=linkedin&hsa_ver=3

Insurance competitors never reveal their strategy in a single place. 🛑 They reveal it through 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐥𝐚𝐮𝐧𝐜𝐡𝐞𝐬, 𝐩𝐚𝐫𝐭𝐧𝐞𝐫𝐬𝐡𝐢𝐩𝐬, 𝐩𝐢𝐥𝐨𝐭 𝐩𝐫𝐨𝐠𝐫𝐚𝐦𝐬, 𝐡𝐢𝐫𝐢𝐧𝐠 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬, 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐛𝐞𝐭𝐬, 𝐞𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦 𝐦𝐨𝐯𝐞𝐬, 𝐚𝐧𝐝 𝐫𝐞𝐠𝐢𝐨𝐧𝐚𝐥 𝐫𝐨𝐥𝐥𝐨𝐮𝐭𝐬. For financial services and insurance companies, tracking those signals manually creates a real business problem, where teams see too much information, too late, with too little structure. That makes it harder to answer questions like: > Which peers are investing in embedded insurance? > Who is piloting GenAI in claims, underwriting, or customer service? > Which partnerships are turning into actual distribution advantage? > Where are competitors moving from experimentation to scaled deployment? 𝐓𝐡𝐢𝐬 𝐢𝐬 𝐰𝐡𝐞𝐫𝐞 𝐜𝐮𝐬𝐭𝐨𝐦 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐦𝐚𝐤𝐞 𝐬𝐞𝐧𝐬𝐞 and work better than generic tools can summarize content. Competitive intelligence in insurance requires: - source-grounded signals, - industry-specific classification, - consistent peer benchmarking, - configurable dimensions, - auditability, - and reporting workflows that align with how strategy, innovation, and market intelligence teams actually work. For one of the leading insurance providers, we built an 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐭𝐨 𝐦𝐨𝐧𝐢𝐭𝐨𝐫 𝐡𝐨𝐰 𝐩𝐞𝐞𝐫 𝐢𝐧𝐬𝐮𝐫𝐞𝐫𝐬 𝐠𝐥𝐨𝐛𝐚𝐥𝐥𝐲 𝐚𝐝𝐨𝐩𝐭 𝐧𝐞𝐰 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬, launch partnerships, pilot innovation initiatives, and shift strategic priorities. The system combined: 1. Automated public data collection, 2. An AI-powered pipeline for signal extraction, classification, summarization, visualization, and report generation. The result: stakeholders moved to a structured, repeatable intelligence process. Instead of manually reviewing large volumes of public sources, they could 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐬𝐢𝐠𝐧𝐚𝐥𝐬 𝐦𝐨𝐫𝐞 𝐪𝐮𝐢𝐜𝐤𝐥𝐲, 𝐜𝐨𝐦𝐩𝐚𝐫𝐞 𝐜𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐨𝐫𝐬 across selected dimensions, and trace insights back to the source data. In insurance, the advantage is not just having more information, but knowing which signals matter, seeing them early, and turning them into decisions before the market has already moved. If your team is exploring where agentic AI and custom LLM or RAG systems can create measurable operational value, this is where we usually start: 👉 https://lnkd.in/esYN8uD7/?utm_campaign=%255Bclick%255DLLM(WIP)&utm_source=linkedin&utm_medium=paid&utm_id=703271224&hsa_acc=508184568&hsa_cam=703271224&hsa_grp=326434034&hsa_ad=1462897284&hsa_net=linkedin&hsa_ver=3

Enterprise data can be available, but still hard to use. 👇 In one recent project, we built an MCP server on Azure that connected ChatGPT with Microsoft Fabric for a US-based spirits producer and distributor. The goal was simple: give business users a 𝐬𝐞𝐜𝐮𝐫𝐞 𝐰𝐚𝐲 𝐭𝐨 𝐚𝐬𝐤 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐚𝐛𝐨𝐮𝐭 𝐢𝐧𝐭𝐞𝐫𝐧𝐚𝐥 𝐝𝐚𝐭𝐚 without having to rely on a technical team every time. Users could query approved datasets, compare information, and get structured answers, summaries, and reports in ChatGPT. This setup can also work for other companies with 𝐥𝐚𝐫𝐠𝐞 𝐢𝐧𝐭𝐞𝐫𝐧𝐚𝐥 𝐝𝐚𝐭𝐚 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬. Examples include: • operational and supply chain analysis • sales and commercial reporting • financial analysis • recurring management reports • internal data search • AI agents that need access to company systems 𝐓𝐡𝐞 𝐤𝐞𝐲 𝐩𝐚𝐫𝐭 𝐢𝐬 𝐜𝐨𝐧𝐭𝐫𝐨𝐥. An LLM should not have open access to business systems. Companies need clear rules for authentication, permissions, available actions, and data access. That is where a custom MCP server can help. It creates a controlled connection between AI tools and enterprise systems. Read more about how we build 𝐌𝐂𝐏 𝐬𝐞𝐫𝐯𝐞𝐫𝐬 𝐟𝐨𝐫 𝐞𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐀𝐈 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞: 👉 https://lnkd.in/dxFCnXve/?utm_campaign=%255Btraffic%255DMaincampaign&utm_source=linkedin&utm_medium=paid&utm_id=1206642164&hsa_acc=508184568&hsa_cam=1206642164&hsa_grp=887012544&hsa_ad=1541830714&hsa_net=linkedin&hsa_ver=3

Your RAG pilot worked. ✅ Now the agent needs access to tools, internal data, and business systems. 🔐 That is where the architecture gets harder: 1. Who can the agent act for? 2. What can it access? 3. How do you trace its decisions? 4. And what evidence will risk and compliance teams need before approving production deployment? This session explains how to design governed agentic systems - from MCP and access controls to auditability, reliability, and production readiness. Watch the full session: 👉 https://lnkd.in/ekZj-e3S/?utm_campaign=%255Bvideo%255DLLM&utm_source=linkedin&utm_medium=paid&utm_id=776239064&hsa_acc=508184568&hsa_cam=776239064&hsa_grp=416564784&hsa_ad=1468807454&hsa_net=linkedin&hsa_ver=3