deepsense.ai

Yesterday’s announcement of Anthropic's Claude for Healthcare & Life Sciences at the JPM Healthcare Conference reinforced an important shift: AI in regulated life sciences is not a future-state discussion -> it's already operating in production (with ~10k real requests in 24 hours hitting our mentioned MCP connectors, as teams integrate them into live systems, just as a proof). We’re grateful to be part of this ecosystem. Both live demos presented during the announcement were powered by MCP connectors built by deepsense.ai 💪 Our connectors were the MCPs shown in action: - CMS, - ICD-10, - NPI, - ChEMBL, - ClinicalTrials.gov, - bioRxiv/medRxiv They are now listed in the official Claude Connectors Directory: https://lnkd.in/gtWaKGC2 In the last 24 hours alone, our MCP infrastructure served ~10,000 real requests across healthcare and life sciences workloads. 👉 Why does this matter for pharma, biotech, and healthcare AI leaders? This is not only about LLMs supporting summaries or research assistance, but about production-grade AI systems that: -> draw from authoritative regulatory and scientific sources -> support clinical trial operations, protocol generation, regulatory workflows, and R&D -> operate with auditability, traceability, and high availability in mind -> scale reliably under real demand. At deepsense.ai, this reflects what we see across our life sciences work every day: = building domain-aware, compliant, and scalable AI systems designed to hold up under regulatory scrutiny. This is where healthcare and life sciences AI is heading, and it’s already live!

We’re proud to be named among the partners supporting 💊 Anthropic’s work on Claude for Healthcare and Life Sciences 💊 in an ecosystem focused on deploying AI where regulatory scrutiny, data integrity, and patient impact are non-negotiable. Why this matters for CTOs and AI leaders in pharma and healthcare: 👉 LLMs and AI agents are moving into core life sciences workflows - as systems supporting clinical trial operations, protocol authoring aligned with FDA/EMA guidance, regulatory submissions, pharmacovigilance, site selection, and R&D decision support, often integrated with platforms like Medidata, ClinicalTrials.gov, and internal trial data. 👉 Success is now measured against regulatory and operational reality - where the key question is no longer “does the model perform?” but “can the system withstand audits, ensure traceability, protect sensitive data, and operate consistently across regions while compressing trial timelines and time-to-market?” 👉 System design beats model choice - differentiation comes from architecture, validation strategy, governance, and domain-specific alignment, how models are constrained, evaluated, logged, and integrated, not from isolated demos or benchmark scores. More about Anthropic x Life Sciences' latest data and insights: 👉 https://lnkd.in/dcXrebFq At deepsense.ai, this direction mirrors what we see daily across our growing portfolio of life sciences projects: - from guideline-aware protocol generation (ENCePP-aligned), - AI-driven site selection outperforming legacy methods by 90%, - multimodal LLMs accelerating in-silico drug discovery 5×, - secure copilots supporting pricing negotiations and regulatory operations. More case studies here: https://lnkd.in/dQ2eXdMr If you’re responsible for AI strategy in a regulated environment, this is the right moment to ground decisions in the latest data and real deployment experience. Don't hesitate and join us today for The Briefing: Healthcare and Life Sciences January 12, 2026 | 11:30 AM PST | Live stream Anthropic leadership and customers will discuss how AI is being applied across healthcare delivery, clinical trials, regulatory operations, and life sciences R&D - with a focus on what is now feasible, compliant, and scalable in production. For CTOs, Heads of AI, and digital leaders operating under FDA, EMA, HIPAA, and GxP constraints, this is essential context for planning the next phase of enterprise AI adoption. Join here: https://lnkd.in/dB7PXwuw

Most LLM initiatives don’t stall where people think they do. According to conversations with CTOs and AI Directors running LLMs in production: -> The #1 scaling problem is infrastructure and integration, not accuracy. -> Teams with <$100k AI budgets are shipping copilots - while some $5M+ programs are stuck in internal resistance. -> 75% cite data privacy and security as a top blocker - and for many, it’s a hard stop, not a delay. -> Only 15% have a formal strategy for bias or hallucination mitigation, despite most acknowledging the risk. -> RAG, agents, and internal copilots dominate 2026 plans - but orchestration and latency are where systems start to break. Most leaders already know what they want to build: domain-specific copilots, agentic workflows, multimodal systems. What’s slowing them down is: – stitching LLMs into legacy stacks – getting legal and security to say “yes” – earning user trust once the system leaves the demo stage We documented these patterns, with numbers, not slogans, in our latest report: Inside the Minds of CTOs: What It Really Takes to Scale LLMs in 2025/26 👉 Learn more about it here: https://lnkd.in/d3EMJ9ZE

After 2025, and after deploying multiple agentic systems in production, one thing became clear. The projects that genuinely paid for themselves followed consistent patterns. The ones that didn’t rarely failed for a single, obvious reason. Our Senior AI Delivery Manager pulled together: -> Lessons from our agentic deployments, -> insights from rebuilding failing projects, -> and hard data from Bain, MIT, IBM, Google, and Microsoft. The result is a 🔝 12-factor framework for agentic AI ROI. If you’re responsible for turning agentic AI into measurable business impact, this will save you time: 👉 https://lnkd.in/dy988HCy Kudos to Rafał Łabędzki, Ph.D. for the effort!

After a year of working with CTOs on AI agents, RAGs, multimodal systems, and LLM deployments, one pattern kept repeating: → The hard problems are: integration, trust, governance, and scaling beyond the first use case. This blog explains why we built the LLM Adoption Report 2025/26: - why infrastructure and system design dominate outcomes, - why deep CTO interviews reveal more than “AI maturity” scores, - why surface-level surveys don’t support real technical decisions. If you’re planning agentic/LLM initiatives for 2026 and want a grounded view of how other technical leaders think, struggle, and decide, this context matters. 👉 Read what the report is about, and why it’s worth your time: https://lnkd.in/dJ3fMPER

95% faster customer service => A repeatable ROI pattern. How? 👉 https://lnkd.in/diXwC7hj Together with InterRisk TU SA Vienna Insurance Group, we ran a 3-week Proof of Concept focused on a specific, high-impact problem: post-sales email handling at scale, under strict GDPR and financial regulatory constraints. What mattered most (and why this worked): 1️⃣ The right use case: ~30,000 customer emails per month, many with scanned or handwritten attachments. -> High volume. High repetition. High operational cost. Clear baseline to measure ROI. 2️⃣ The right technology choices: - LLM + OCR, purpose-built for classification and structured extraction - Open-source model hosted on secure European infrastructure - Explainable, auditable workflows designed with compliance in mind, not added later 3️⃣ Numbers that justify production rollout: - ~85% automatic classification accuracy - Processing time reduced from ~5 minutes to ~30 seconds per email - >90% reduction in manual effort - Up to 5,000 emails/day, no performance degradation - Zero third-party data processing → GDPR-compliant by design 4️⃣ Built to scale, not just to validate The PoC was intentionally designed so it can be: - extended to other departments - reused across similar operational workflows - replicated in other industries with comparable communication volumes Yes, this is an insurance case. But if you run shared inboxes, back-office operations, claims, support, or compliance-heavy processes, the impact profile looks very similar. 👉 If you want to see the architecture, key tech components, metrics, and lessons learned in detail, explore the full case study: https://lnkd.in/diXwC7hj This is what AI looks like when it’s driven by ROI, security, and repeatability.

🙋♂️ If you were busy all year and told yourself: “I’ll look into AI agents properly when I have time”... - this is that one session worth making time for: https://lnkd.in/gtze3JXj In 60 minutes, senior engineers from deepsense.ai break down what actually works when AI agents leave the demo phase and hit real traffic, real costs, and real failure modes. No hype, no vendor slides (we don't like them, either! 😵💫 ), just field-tested patterns from production systems. You’ll see how teams: 1. Move from clever PoCs to durable, observable agent architectures 2. Use supervision, memory, and specialized sub-agents without blowing up cost or latency 3. Apply practical frameworks (not theory) to stabilize multi-agent systems in production 4. Decide what not to build - which is often the difference between ROI and regret This session was originally delivered as a paid workshop for enterprise teams. We’re sharing a condensed, on-demand version so you don’t carry last year’s technical debt into the next one. Watch the session on AI Agents - and close this loop properly: https://lnkd.in/gtze3JXj Mateusz Wosinski + Rafał Łabędzki, Ph.D. = 🧠 💪

As the year closes, a few uncomfortable questions tend to surface: 🔝 What actually worked in our AI initiatives this year? 🛑 What broke once things hit production? ❓ Which decisions scaled - and which ones quietly didn’t? 🤔 What should we double down on in 2026, and what should we stop doing altogether? “Inside the Minds of CTOs: What It Really Takes to Scale LLMs in 2025/26” was written for this exact moment. It’s a year’s worth of hard lessons from real production work = difficult trade-offs, failed assumptions, and conversations with leaders accountable for outcomes, not demos. 👉 Learn more here: https://lnkd.in/dP2ygrWR What’s inside is grounded in reality: -> 20 in-depth interviews with CTOs and AI leaders -> 97 pages of quantitative data and field insight -> Experience from enterprise AI systems across tech, pharma, telecom, manufacturing, healthcare, and regulated environments -> Lessons distilled from hundreds of AI projects delivered at deepsense.ai as an OpenAI and Anthropic Service Partner Late December is the time for reflection and recalibration. By the end, you’ll have: 1. A clearer mental model of what scaling LLMs truly entails 2. Benchmarks to pressure-test your roadmap 3. A grounded view of where agentic systems, RAG, and domain-specific LLMs deliver real value 4. Better questions to ask before approving next year’s initiatives This isn’t about chasing what’s new. It’s about starting next year better prepared than the last. Download the report here, to enter 2026 with clarity, not assumptions: 👉 https://lnkd.in/dP2ygrWR

Most agentic systems don’t fail slowly. 🛑 They fail all at once. -> One “smart” agent gets overloaded. -> Retrieval misses the key doc. -> Bot detection blocks access. -> The prompt grows. -> And suddenly the whole system feels brittle. We just published a deep dive on why elegant agent architectures collapse in production, and what actually works instead. If you’re building agents beyond demos, this one’s for you.