SupportLogic

How NiCE reduced escalations by 35%, case volume by 5% and sped up resolution by 25%. 🚀 What do you do when 85% of your customers skip your search bar and go straight to opening a ticket? For the team at NICE, this was a clear signal: their traditional keyword search wasn’t working. By partnering with SupportLogic, they transformed their support experience from a "find a link" model to an "AI-generated answer" model. The impact? ✅ 35% reduction in customer escalations ✅ 4-5% reduction in monthly case volume. ✅ 25% faster time-to-resolution. ✅ Unified knowledge across 10+ internal sources. By moving to a natural-language, AI-driven search experience, NICE didn't just deflect tickets, they empowered their customers and engineers with instant, context-aware answers. Thank you Chris Romrell for your partnership and this case study

We are thrilled to announce that TechBullion has named our podcast, hosted by our CEO Krishna Raj Raja, as a must-listen for leaders in the support space. 🏆 In every episode, we dive deep into how technology, empathy, and data-driven insights are transforming the way companies interact with their customers. Being recognized alongside other industry leaders is a testament to the importance of the #SupportExperience. Want to see who else made the list or catch up on our latest episodes? Check out the article below in the comments! 👇 #CustomerSuccess #Innovation #SX #SupportLogic #TechNews

Your support organization collects data all day, every day. Then it goes to gather dust in your CRM. Support data is messy, it's true. But that messy data contains golden nuggets of customer intelligence that should be informing sales strategy, product development, and executive decision-making. On Thursday, March 5th, we’ll show you how easy it is to find those nuggets when we dive into the SupportLogic Data Cloud. You'll learn how Data Cloud helps: • Transform Support into a real-time stream of intelligence: By extracting unstructured signals from every post-sales interaction (including tickets, chat, and email) SupportLogic turns raw data into actionable insights for sales, product, and leadership. • Predict Escalations: Using advanced signal extraction, the platform identifies customer intent and sentiment within messy conversation data to predict likely escalations within 72 hours. • Protect Revenue: Stop listening only to the loudest customers. SupportLogic allows you to prioritize the highest-risk accounts by tracking sentiment and escalation risk across 100% of your interactions. See comments for registration link. #SupportLogic #SupportLogicDataCloud #IntelligentSupportExperience #SupportLogicEvent #CustomerSupport #CustomerExperience #SupportOps #SupportLeadership #SupportAI #SentimentAnalysis #EscalationPrediction #CustomerIntelligence #DataIntelligence

The "Cost Center" era of Support is over. Most enterprises are drowning in support data—chat transcripts, emails, and case notes—that sits untouched because legacy systems of record aren't built to analyze it. Without a way to synthesize these signals, product teams often prioritize the loudest customers rather than the highest-risk ones. To help you unlock this potential, we built the SupportLogic Data Cloud. It's designed to be the foundation for a true "System of Intelligence" that turns raw interactions into actionable strategy. Check out the latest deep dive by Thamizharasan S on how our customers are leveraging this technology to move from reactive ticket management to proactive business impact. #CustomerSupport #GenerativeAI #DataCloud #SupportLogic #SystemOfIntelligence #SupportExperience

The biggest trend that I have noticed in 2025 from hundreds of direct customer conversations is the move towards CRM-less architecture. Customers are questioning why do I need a support ticketing system? For decades, we’ve forced Support teams to live inside a "System of Record." We asked them to spend more time updating ticket metadata than actually solving customer problems. But a CRM is just a database in the cloud. It is built for the workflows and not for contextual and predictive intelligence. This is why I’m seeing a massive shift toward a CRM-less architecture. In my latest blog, I explain why leading Support organizations are decoupling their "Intelligence" from their "Database." By moving to a CRM-less model, you can: ✅ Listen to signals across all channels (Slack, Jira, Email), not just tickets. ✅ Move from reactive workflows to proactive, AI-driven interventions. ✅ Free your organization from the "CRM Tax" https://lnkd.in/em2nBx8X

My hot take on the news of Apple using Google Gemini to overhaul Siri. For those who’ve been in tech long enough, this feels like a repeat of 1997. Back then, Steve Jobs took $150M from Microsoft and made Internet Explorer the default browser on Mac. To the public, it was a "surrender." In reality, it was Apple buying the time to rebuild the company. Eventually not only they survived they bounced back to become the richest company on the planet. Safari browser which was built much later became the killer app for the iPhone launch. From an architectural and business standpoint, here is why this is a massive win for Apple and a structural threat to OpenAI: 1. The "Google Maps" Strategy (Rent vs. Own) Apple is "renting" Google’s LLM infrastructure today just as they rented Google Maps in 2007. They get the best tech on day one, satisfy their 2 billion users, and avoid the "Siri is dumb" narrative—all while they quietly build their own vertically integrated, silicon-optimized models. 2. Liability Abstraction: . The training of frontier models is a legal minefield of copyright and public data misuse. By using Gemini as the foundation, Apple offloads the legal liability to Google. Google takes the heat for how the data was scraped, while Apple sits a layer above, and they can apply on-device semantic indexing, private cloud compute and app intents to differentiate their offering from vanilla Gemini. 3. The Commoditization of the LLM: This deal is a blow to OpenAI. LLMs are quickly commoditizing and the winners in this space are the ones with the best distribution (Apple has 2 billion+ devices), best ecosystem, and the best UX layer. Above brand loyalty and trust. The Bottom Line: OpenAI is in a dangerous spot. In tech, the winner isn't the one who builds the greatest engine—it’s the one who builds the car that everyone drives.

Founders wear multiple hats but as a company scales, the "jack-of-all-trades" approach becomes a bottleneck and founders have give up some of their roles. Founders naturally gravitate toward one of four "workspaces" that defines their archetype 1. VSCode/Jetbrains 💻 (The Architect) These founders can turn an idea into a functional MVP overnight and understand the scalability of the stack. Superpower: Speed of execution. The Trap: Focusing on "perfect code" over "product market fit." 2. Figma 🎨 (The Product Visonary) These founders thrive in creation and obsessed with the product experience. Superpower: Building. They see the world as it could be and can manifest a vision into a tangible prototype. The Trap: Getting stuck in "tinkering mode" and delaying the launch for one more feature. 3. Keynote/Powerpoint (The Seller) 📊 These founders are obsessed with the narrative and storytelling. Superpower: Persuasion. They can raise capital, recruit top-tier talent, and close enterprise deals through the sheer power of storytelling. The Trap: Selling a vision that the current product or team can’t actually deliver yet. 4. Spreadsheets (The Operator) 📈 These founders can run business from excel sheets, and they are obsessed with efficiency. Superpower: Systems. They understand the "machine" of the business—burn rates, LTV/CAC, and process optimization. The Trap: Being so focused on operations that they lose sight of the bigger picture that drives brand loyalty, technical differentiation and intangible moats. The "Unicorn" Founder 🦄 Let’s be honest: It is incredibly rare to find a founder who genuinely thrives in all four workspaces. Most of us have one natural spike, a second skill we’ve learned to be good at, a third one that we can tolerate, and a fourth one that drains our battery completely. Recognizing which one is your "home" is the difference between burnout and breakthrough. If you thrive in Figma, find a spreadsheet magician COO early. If you’re a Spreadsheet Founder, hire a visionary Head of Product. In my personal experience self-awareness is your greatest competitive advantage. When you stop fighting your nature, you start scaling your impact. Which persona resonates most with you? Are you an Architect, Creator, a Seller, or an Operator? 👇

Why 70% of Enterprise AI projects never leave the sandbox environment? 📉 The "Experimental Gap" is real. Most AI projects struggle to go live n production, because they hit three walls: 1. Model Fragility: How do you swap models when a better/cheaper one comes out or provide redundancy layer if there is an outage or API throttling issue? 2. Context Isolation: How do we ensure the AI actually understands unique customer history, and is personalized per tenant 3. Security & Compliance: How do we protect PII at scale so your sensitive data stays within your firewall and never becomes training data to LLMs This blog by Saurabh Agarwal breaks down SupportLogic's architectural solution: LLM Hub Think of LLM Hub as an Enterprise Control Plane, it allows us to decouple applications from the underlying ML models. This is the "missing link" for any organization that wants to build AI application that is model-agnostic while staying security-first. https://lnkd.in/ghipFPpW

LLMs are becoming core enterprise infrastructure - yet most organizations still manage them like side projects. SupportLogic LLM Hub is differentiated by one idea: LLMs need the same rigor as cloud, security, and data platforms. It provides a single operational layer for: • Governance & compliance • Cost, usage & performance visibility • Model abstraction & resilience • Secure enterprise grounding This is what turns fragmented LLM experiments into a scalable, trusted AI platform. Read the full blog: https://lnkd.in/gs5eC3Xw #AI #EnterpriseTech #LLMs #AItrends #SupportLogic Saurabh Agarwal Michael Larionov Ryan Radcliff Krishna Raj Raja