deepsense.ai

Enterprise demand for voice bots is accelerating again. Together with our partner, ElevenLabs, we’ve seen a clear shift over the past few months. We’re speaking with AI leaders who are facing the same questions: - “Why is our cost per contact still rising despite automation?” - “Why did our previous voice bot fail after the pilot?” - “Why does latency kill the UX the moment we go live?” - “How do we maintain control and compliance when LLMs are involved?” Here’s what we see in production. 1️⃣ Latency kills trust In voice, 800 ms feels broken. Unconstrained LLM calls + slow tool orchestration = silence. Silence = churn or escalation. 2️⃣ “Plug-and-play” doesn’t survive scale Most failures happen when voice AI is treated like SaaS. Real systems require: - Telephony integration (SIP, CCaaS, IVR replacement) - CRM / ERP / ticketing integration - Real-time API calls - Pre- and post-call hooks - Observability across transcripts and drop-offs This is where ROI is made or lost. 3️⃣ The best results? High-volume, structured interactions From our experience, the strongest ROI shows up in: - Inbound and outbound calls + lead qualification - Tier-1 troubleshooting and status checks (orders, claims, balances) - Booking & scheduling - Billing & payments - Internal tasks, like IT password resets or HR policy questions One of our systems handles 500,000+ outbound calls annually across 170+ locations - without increasing headcount. 4️⃣ Constraints > creativity in enterprise voice In regulated or customer-facing environments, determinism wins. We design: - Guardrails at the conversation-step level - Controlled tool invocation - Script enforcement where required - Full QA and regression testing before every release You don’t want “creative.” You want predictable, auditable, low-latency behavior. If you’re responsible for AI strategy and wondering where voice bots actually generate measurable ROI, take a look at what we can do for you: https://lnkd.in/dgsUnmhH

Across pharma, tech, and AI-native companies, the core tension is the same: - You need AI speed. - You need enterprise control. - You can’t sacrifice either. In a recent project, we built a cloud-native Data Science & ML platform on GCP + Kubernetes for a multinational enterprise with distributed teams across EMEA, which exemplifies the recurring problems we see. 1️⃣ AI Velocity vs. Enterprise Security The problem: - Data scientists want fast access to data and GPUs. - Security and IT require strict IAM, VPN controls, auditability, and isolation. Shadow ML environments. Manual access approvals. Friction at every step. What we implemented: - Isolated, containerized workspaces per user/team - IAM-based access control aligned with enterprise identity - Secure VPN-based connectivity - Persistent storage with policy enforcement 🔑 DS teams could experiment freely - without bypassing security controls. Governance was enforced at infrastructure level, not as a PDF policy. 2️⃣ MLOps Bottlenecks Blocking Production The problem: Many enterprises lack sufficient in-house MLOps or DevOps capacity. Every new model requires: - Infra setup - Environment configuration - GPU provisioning - Monitoring pipelines AI teams become dependent on central IT tickets. What we implemented: - Pre-configured containerized environments - Built-in experiment tracking - Standardized production-grade ML workflows - Self-service GPU provisioning 🔝 Teams could move from experimentation to scalable training without waiting on infra engineering. Productionization stopped being a bottleneck. 3️⃣ GPU Costs & Resource Chaos The problem: GPU clusters are: - Overprovisioned “just in case” - Underutilized most of the time - Hard to attribute to teams Finance sees rising cloud spend. No one sees clear ROI. What we implemented: - On-demand GPU training on Kubernetes - Centralized monitoring - Controlled, shared compute pools - Cost-aware scaling ✈️ Compute became elastic and observable. Shared infrastructure reduced idle GPU waste while supporting heavy workloads when needed. --- Leading AI, Data, or MLOps? 👊 Happy to share how we structured it.

Instead of redirecting users to external dashboards, MCP Apps let teams ship usable functionality inside the conversation — changing how AI-native products are built, discovered, and monetized 👇 In the latest AI Tech Experts Webinar, Michal Pstrag, ML Engineer at deepsense.ai, explains what MCP Apps are and how they enable interactive UIs to run directly inside AI chat interfaces. This session gives you both architectural context and practical guidance ⚙️ 👉 Watch the full piece here: https://lnkd.in/efyGCcZx #AIAgents #ModelContextProtocol #ConversationalAI #LLMApplications

💊 In regulated industries, 90% of the complexity sits below the API layer. Important: These insights on MCPs for regulated industries are drawn from our work supporting Anthropic’s life sciences ecosystem, where we built and operate MCP connectors that power integrations with clinical and scientific data sources. We’ve learned that: - No stateless design → validation debt - No role-based tool exposure → access control risk - No structured audit logging → FDA/HIPAA exposure - Storing prompts/responses → unnecessary PII liability - Centralized “god-mode” APIs → compliance nightmare What actually works: 1. Isolated, containerized MCP servers per data domain 2. Strict capability negotiation (tools exposed by role) 3. Full tool-call auditability via protocol structure 4. Observability without retaining user data 5. Intelligent caching to cut latency (~80%) without breaking integrity Compliance = architecture. For AI leaders in life sciences (and other regulated sectors), this is the real playbook: https://lnkd.in/djneXSFZ

💸 $30–40B invested in GenAI. 95% see no ROI. Yet a small group delivers 10–25% EBITDA uplift. Why? Building agents ≠ running enterprise-grade AI. And confusing the two is expensive. Yes - there are more and more low-code / business-user tools to build AI agents. They’re great for experimentation. They work well for solo builders, startups, and AI hobbyists. But once you move beyond "fun", things break. Enterprise reality brings security, governance, orchestration, ownership, and scale. That’s where most initiatives stall, on execution. This carousel breaks down what actually separates AI experiments from production systems that move P&L 👇 Learn more about it here: 👉 https://lnkd.in/dPyUPCMu #GenAI #AgenticAI #EnterpriseAI #AIROI #AIGovernance

This announcement from Vercel is actually worth a 2nd post because 1) it's such a big deal and 2) now I know why. The reason we've been able to build BB AI so fast is that there is ZERO communication required between business vision, product management and engineering. It's all in one person's head...mine. But all along, one of my biggest concerns was how to scale this without slowing down. We've onboarding a new engineering team to support me and their biggest roadblock is the ability to get ideas out of my head and into well documented requirements. When product can't do engineering and engineering doesn't know what the business wants, you need an effective communication layer and that just slows things down. One approach I took was to build a prototype of Jira <--> Claude (Thank you deepsense.ai for the idea: https://lnkd.in/gGhaGMYy) so that I could assign tickets to Claude in JIRA and that would spawn a cloud agent to implement the ticket, generate a PR and notify an engineer to review it. That's a start, but we need to merge the PR before a PM can review it. And if there are issues or bugs, we have to create another ticket and go back through the loop. This change from v0 / Vercel allows product managers to BUILD (not prototype) features and have engineers review / merge the code only after the feature is done properly. This massively reduces cycle time for product development. This is an ABSOLUTE GAMECHANGER. Thank you Vercel! https://lnkd.in/eeDtaGij

Imagine you’re a user of an e-commerce platform. You don’t scroll endless categories. You don’t tweak filters for 10 minutes. You just say what you’re looking for - and the platform actually understands. That’s what we helped build for a marketplace in the Gulf Cooperation Council. 👉 In 6 weeks, we moved them from static catalogs to AI-driven, conversational product discovery - designed for production. What was behind it: - A full marketplace architecture (UI → backend → partners) - LLMs embedded into personalization and discovery - Real integrations with merchants, logistics, and payments Early results: → 25% of autonomous agent actions executed correctly (above early benchmarks) → A clear path to improving reliability and scale The takeaway? Conversational commerce isn’t just chatbots. It’s about better discovery, higher conversion, and systems that can scale. This matters if you’re building: - Marketplaces or e-commerce platforms - AI features that must survive production - LLMs that work with real operations, not around them 👊 How do you imagine your ideal e-commerce experience?

Quick share for anyone working on LLMs in production 👇 We surveyed 20 CTOs and AI leaders about what’s actually happening after the PoC phase. A lot of the answers were familiar: - Great demos, painful production - ROI measured in cost + time saved, not hype - Security and compliance slowing everything down The short video sums it up well. The report goes deeper if you’re in the middle of shipping (or trying to). Worth a look if this sounds close to home: 👉 https://lnkd.in/d6h45xy9