Dview

440,000 hours. That’s 50 years of working life no longer spent pulling data, fixing reports, or chasing numbers across multiple tools. That’s what our customers have documented before vs after deploying Dview. But the bigger impact is harder to measure: → Faster answers for business-critical questions → Fewer repetitive reporting cycles → Live insights in the room when decisions matter Data intelligence platforms are often sold on cost savings. The real advantage is decision speed. #DataPlatform #EnterpriseData #AIAnalytics #BFSI #DecisionIntelligence

Enterprise AI isn’t struggling because of weak models. It’s struggling because most enterprises still can’t connect the dots across their own data. That’s where Knowledge Graphs come in. They transform fragmented systems into contextual, connected intelligence - enabling organisations to move from static reporting to decision-grade insight. #EnterpriseAI #KnowledgeGraphs #DataIntelligence #AgenticAI #DecisionIntelligence #ArtificialIntelligence #AIInfrastructure

✨ Most enterprises have invested in the AI layer. Very few have invested in the layer underneath it. Here is what that layer actually contains: → A knowledge base that is fragmented across 12 tools → Metrics that mean different things to different teams → Governance that was designed for reports, not real-time agents → Pipelines that were built for dashboards, not AI queries The model is not the bottleneck. The infrastructure holding the model back is. Every enterprise AI project that quietly failed in the last 18 months failed at this layer - not at the model layer. The companies that are actually scaling AI are not buying better models. They are building better foundations. What does your data foundation look like underneath your AI layer? #EnterpriseAI #DataInfrastructure #AIStrategy #DataEngineering

Data gives answers. Context gives judgment. Trustworthy #Enterprise #AI is built on context, not prompts or large models. The future belongs to organisations that turn context into intelligence.

🧤 Most enterprise data is fragmented across PDFs, dashboards, spreadsheets, transcripts, CRMs, warehouses, emails, and tribal knowledge locked inside teams. Imagine an Enterprise AI is going through its “demo phase.” 🪂 Beautiful copilots > ⚖️ Impressive benchmarks > 🧭 Fast summaries... But inside enterprises, the real problem was never generating text. The real problem was trust. Every department defines metrics differently. Every workflow carries hidden context. Every answer has governance, access control, and accountability attached to it. That is why most AI pilots quietly fail after the excitement fades. Not because the models are weak. Because the knowledge layer underneath is broken. ☄️ #EnterpriseAI #ArtificialIntelligence #DataInfrastructure #KnowledgeLayer #ConversationalAI #AIInfrastructure #DataIntelligence #DecisionIntelligence #GenerativeAI #AgenticAI

Everyone is talking about AI agents. Very few are talking about what happens after they enter the enterprise. Because the real problem was never connecting tools. It’s what starts breaking after the connection: → Agents behaving differently over time → Token costs quietly exploding → Workflows are becoming unpredictable → Context getting fragmented → Security and governance are becoming harder to control “Plug-and-play AI” sounds simple until it touches real enterprise systems. As agents gain access to APIs, databases, workflows, and decision-making, the challenge shifts from intelligence to reliability. And every enterprise eventually learns the same thing: The model is not the system. Infrastructure is. Governance is. Context is. Deterministic execution is. The companies that win in AI won’t be the ones with the most agents. They’ll be the ones with the most reliable intelligence infrastructure. #AI #EnterpriseAI #AgenticAI #LLMOps #MCP #DataInfrastructure #GenerativeAI

🥁 Welcoming Grand View Research, Inc as our newest partner. What began as a collaboration has quickly evolved into a strategic alignment; built on trust, ambition, and a shared belief that AI will redefine how decisions are made. At the center of this journey are Shashi Kumar, Swayam Dash, Vineet Agarwal, Kauts Shukla, and Supratik Shankar, CFA - leaders who represent a new class of thinking: AI-first, outcome-driven, and unafraid to challenge legacy systems. Our partnership is rooted in a simple idea: Data shouldn’t just translate into decisions. It should accelerate them. Together, we’re building toward: ⚡ Faster, more reliable insight generation ✨ AI embedded into core research workflows 💫 A step-change in how intelligence is created, consumed, and monetized What makes this powerful isn’t just the technology - it’s the willingness to experiment, iterate, and push boundaries. With Grand View Research’s domain depth and Dview’s AI infrastructure, we’re not just improving workflows; we’re redefining the future of research and decision intelligence. Excited for what lies ahead. Welcome aboard, #GVR team. 🫡 Nachiket Agarkar | Jugal Punjabi | Ankit Rastogi, CFA | Nayanjyoti Das | Rajat Saxena | Amar Bahl | Rahul Ghosh | Shubhanga Prasad

Most data systems are built to answer one question: What happened? Dashboards, reports, alerts - they do this well. But this is where things start to break. Because knowing what happened doesn’t tell you: What’s changing What’s at risk What needs attention right now Leaders still have to interpret signals, connect context, and decide what to do next. At Dview, we’re building MCP to address it. A system that doesn’t just report data, but reasons on top of it, surfacing direction, not just information. From “what happened” → to “what should we do next.” #Dview #AI #DecisionIntelligence #MCP #GenAI #DataEngineering

A comprehensive approach to data management extends beyond tools like Snowflake and Databricks. It involves all data types: structured, semi-structured, and unstructured. This evolution from data to pipeline, modeling, and analytics into a conversational format enhances knowledge, which is crucial for success. It accelerates AI use cases without necessitating an extensive data strategy, ensuring that current opportunities are not missed. Context-Aware, Persona-Adaptive Intelligence delivers the exact right insight for the right persona at the right time, dynamically adapting based on changing metrics, KRAs, and goals. Google for Startups has significantly transformed our thinking and has profoundly influenced our journey, from product launch to enterprise onboardings.

APIs gave enterprises the power to connect systems. But someone still had to stitch workflows, manage context, and make sense of it all. At Dview, we saw the gap. So we built MCP. From: Manual orchestration To: Intent-driven intelligence A system that doesn’t just execute - it decides. #Dview #AI #AgenticAI #MCP #GenAI #DataEngineering