Infometry Unveils New Solutions And Services Powered By Snowflake Intelligence & Agentic AI
Every CX leader knows the frustration. Your enterprise sits on mountains of data, yet business teams still wait days for answers. Sales needs trend analysis. Operations demands real-time insights. Customer experience teams require immediate access to sentiment data. Meanwhile, IT backlogs grow longer, and decision windows close faster than ever. The solution is Snowflake Intelligence.
This bottleneck costs Fortune 500 companies an estimated $250 million annually in delayed decisions alone. The problem isn’t lack of data—it’s the friction between having data and actually using it.
Breaking the Analytics Bottleneck
Infometry, a global leader in data analytics and cloud modernization, has launched a suite of AI-powered solutions designed to eliminate this friction entirely. The company’s latest offering combines Snowflake Intelligence with Agentic AI capabilities to transform how enterprises interact with their data ecosystems.
The announcement, made on November 7, 2025, represents a fundamental shift in enterprise analytics. Rather than requiring technical expertise to extract insights, these solutions enable natural language conversations with governed data. Decision-makers across finance, operations, sales, supply chain, and customer experience can now ask questions in plain English and receive instant, accurate responses.
As Nag Dinamani, CEO of Infometry, explains: “By merging Snowflake Intelligence with Agentic AI, we transform governed enterprise data into a conversational format—empowering every decision-maker to ask, learn, and act with confidence.”
The Technology Behind the Transformation
Infometry’s approach leverages Snowflake Intelligence—a comprehensive AI platform that includes Cortex AI, Cortex Analyst, Cortex Search, and Semantic Views/Models. These capabilities work in concert to deliver what enterprises have long promised but rarely achieved: true self-service analytics.
Natural Language Querying Without Compromise
Traditional business intelligence requires users to understand database structures, write SQL queries, or wait for technical teams to build custom reports. Snowflake Intelligence eliminates these barriers. Business users can pose questions naturally: “What drove the 15% spike in customer complaints last quarter?” or “Show me operational efficiency trends across our top five markets.”
The system translates these requests into precise database queries, executes them against governed data sources, and returns visualized answers—all within seconds. Snowflake has enhanced text-to-SQL performance by up to three times, while its Agent GPA framework detects up to 95% of query errors with near-human accuracy.
Governed Semantic Models That Scale
The foundation of reliable AI-driven analytics is semantic modeling. Infometry implements governed semantic layers that establish unified business definitions, consistent calculations, and dimensional hierarchies. This ensures that “revenue,” “customer lifetime value,” or “churn rate” mean exactly the same thing whether asked by sales, marketing, or operations.
Organizations implementing robust semantic frameworks report dramatic improvements. Fortune 500 companies have achieved 73% reductions in data preparation time and enabled over 12,000 concurrent users to access analytics with sub-second response times. Data governance compliance preparation time drops by 94%, while time-to-insight decreases by 67%.
These aren’t incremental improvements—they represent fundamental transformations in how enterprises operationalize data.
From Insights to Action: Agentic AI Enablement
Understanding data is valuable. Acting on it is transformational. Infometry’s Agentic AI framework bridges this gap by deploying domain-aware AI agents that don’t just answer questions—they initiate actions.
Proactive Intelligence That Works While You Sleep
Agentic AI systems operate autonomously, monitoring key metrics, surfacing exceptions, and triggering workflows without human intervention. An agent might analyze weekly sales trends, identify underperforming product lines, and automatically route recommendations to regional managers with contextualized explanations.
These agents integrate directly with enterprise systems including CRM, support platforms, and alerting infrastructure. They generate clear narrative explanations of performance shifts, create intelligent email summaries for stakeholders, and orchestrate follow-up actions across business systems.
The impact is measurable. Organizations deploying agentic AI report 60% reductions in support costs, 34% faster task completion times, and 14% improvements in resource utilization. In customer service scenarios, AI agents enable 40% deflection of incoming conversations while improving satisfaction scores by as much as 40%.
Flexible Foundation Model Selection
Infometry builds its agentic solutions using leading large language models including OpenAI, Anthropic Claude, Google Gemini, Salesforce Agentforce, and Snowflake’s CLAIRE. This multi-model approach allows customers to select foundations that align with their specific requirements around privacy, governance, performance, and regulatory compliance.
Different use cases demand different capabilities. Financial services clients prioritizing data residency might choose one model, while healthcare organizations focused on HIPAA compliance select another. The architecture remains consistent regardless of the underlying LLM.
Real-World Impact: The Fortune 500 Manufacturing Case
Numbers tell compelling stories. A Fortune 500 manufacturing customer implemented Infometry’s Snowflake Intelligence solution and achieved striking results within months.
Analytics latency—the time from question to answer—dropped by 50%. Operational analytics accuracy improved significantly, enabling faster, data-driven decisions across global operations. Teams that previously waited days for reports now access insights instantly.
More importantly, the solution democratized analytics. Business users who once depended on data teams for every query now explore data independently. This shift freed technical resources for strategic initiatives while accelerating decision velocity throughout the organization.
The manufacturing example illustrates a broader trend. When organizations eliminate technical barriers to data access, they unlock latent productivity across entire workforces. A McKinsey survey found that 61% of decision-making time is ineffective, costing typical Fortune 500 companies 530,000 manager-days annually.
The Customer Experience Connection
For CX professionals, these capabilities translate directly into competitive advantage. Customer experience hinges on understanding sentiment, identifying pain points, and responding rapidly to emerging issues. Traditional analytics processes are too slow for the pace of modern customer expectations.
Infometry’s solutions enable CX teams to monitor experience metrics continuously, receive proactive alerts about deteriorating sentiment, and drill into root causes without technical dependencies. Natural language queries like “Why did NPS drop in the Northeast region?” return not just data, but contextualized insights explaining the contributing factors.
Organizations implementing AI-powered CX analytics report substantial returns. Companies earning $1 billion annually can generate an additional $700 million within three years through customer experience investments. By 2026, Gartner estimates that 10% of customer interactions will be fully automated, up from just 1.6% today.
The connection between data democratization and CX outcomes is clear. When customer-facing teams access real-time insights, they deliver faster resolutions, more personalized experiences, and proactive service. Customer satisfaction improves, churn decreases, and lifetime value increases.
Self-Service Analytics as Employee Experience
The employee experience dimension deserves attention. Data democratization initiatives improve decision-making efficiency by 35% while enhancing operational effectiveness by 72%. Organizations report 40% reductions in time-to-insight metrics and 43% increases in cross-departmental collaboration.
Self-service analytics empowers employees at all levels. Sales representatives access pipeline analytics without requesting custom reports. Operations managers explore efficiency metrics independently. Marketing teams analyze campaign performance in real-time. This autonomy accelerates workflows, fosters innovation, and enhances job satisfaction.
When employees can answer their own questions, they feel trusted and empowered. Organizations implementing comprehensive self-service platforms report significant upticks in employee engagement and reductions in frustration with data access barriers.
Snowflake Migration and Modernization Services
Beyond analytics capabilities, Infometry offers comprehensive migration services to help organizations transition legacy data platforms to Snowflake’s cloud architecture. These migrations aren’t simply “lift and shift” operations—they represent opportunities to reimagine data strategies entirely.
The migration process includes semantic model development, KPI governance establishment, natural language query deployment, and Cortex integration. Infometry’s domain expertise across industries including high-tech, manufacturing, energy, finance, retail, life sciences, and healthcare ensures that migrations reflect industry-specific best practices.
Organizations migrating to Snowflake eliminate infrastructure management overhead while gaining elastic scalability. The cloud-native architecture supports workload spikes without performance degradation. Companies can scale analytics capacity up during peak periods and down during quiet times, paying only for resources consumed.
Implementation Considerations for CX Leaders
CX professionals evaluating these technologies should consider several strategic dimensions.
Start with High-Impact Use Cases
Don’t attempt to transform every analytics process simultaneously. Identify specific pain points where natural language querying delivers immediate value. Customer complaint analysis, NPS trend investigation, and support ticket categorization are excellent starting points.
Pilot projects demonstrate value quickly, build organizational confidence, and generate momentum for broader adoption. They also surface implementation challenges in controlled environments where adjustments are manageable.
Prioritize Data Governance from Day One
Conversational AI is only as trustworthy as the data it accesses. Establish clear definitions for key metrics, implement access controls that reflect organizational hierarchies, and create processes for validating AI-generated insights.
Organizations skipping governance steps inevitably encounter problems. Inconsistent definitions erode trust. Security gaps create compliance risks. Without proper foundations, even sophisticated AI delivers unreliable results.
Invest in Change Management
Technology alone doesn’t drive transformation. People do. Successful implementations require training, communication, and cultural shifts toward data-driven decision-making.
Help teams understand not just how to use new tools, but why they matter. Celebrate early wins. Share success stories. Create communities of practice where users exchange tips and troubleshoot challenges together.
Measure Business Outcomes, Not Just Technical Metrics
Track the metrics that matter: time-to-insight reduction, decision velocity improvement, user adoption rates, and business outcomes like revenue growth or cost savings. Technical performance indicators matter, but business impact drives continued investment and expansion.
The Competitive Imperative
The gap between data-mature and data-struggling organizations continues widening. Companies that democratize analytics, enable self-service capabilities, and deploy intelligent automation pull ahead of competitors still mired in manual processes.
Analyst firms predict that by the end of 2025, more than 40% of large enterprises will deploy autonomous AI agents in at least one core business function. This isn’t speculation—it’s happening now across banking, healthcare, manufacturing, retail, and technology sectors.
Organizations that delay face mounting disadvantages. Slower decision-making, higher operational costs, and inferior customer experiences compound over time. Meanwhile, competitors using conversational AI and agentic automation accelerate past them.
Practical Takeaways for CX and EX Professionals
Democratize Data Access
Remove technical barriers between your teams and the insights they need. Natural language interfaces eliminate dependencies on specialized skills and accelerate decision-making across all organizational levels.
Implement Governed Semantic Layers
Establish unified business definitions and calculations. Semantic models ensure consistency, build trust, and enable reliable AI-driven analytics at enterprise scale.
Deploy Domain-Aware AI Agents
Move beyond passive analytics to proactive intelligence. Agents that monitor metrics, surface exceptions, and trigger workflows reduce manual effort while improving response times.
Choose Flexible AI Architectures
Select solutions supporting multiple foundation models. This flexibility ensures alignment with evolving privacy, governance, and performance requirements.
Measure Business Impact Continuously
Track time-to-insight reduction, user adoption rates, decision velocity improvements, and tangible business outcomes. Let results guide expansion and optimization efforts.
Invest in Change Management
Technology enables transformation, but people drive it. Provide training, communicate value, celebrate wins, and foster data-driven cultures throughout organizations.
Looking Forward
The convergence of cloud data platforms, natural language AI, and autonomous agents represents a fundamental shift in enterprise analytics. Organizations can now deliver insights at the speed of business questions rather than the pace of technical processes.
Infometry’s combination of Snowflake Intelligence and Agentic AI positions enterprises to capitalize on this shift. The solutions address core challenges that have plagued analytics initiatives for decades: technical barriers, governance complexity, and the gap between insights and action.
For CX and EX professionals, these capabilities unlock new possibilities. Faster insights drive better customer experiences. Empowered employees deliver superior service. Data-driven cultures outperform intuition-based competitors.
The question isn’t whether to embrace conversational AI and agentic automation. It’s how quickly you can implement them effectively. Because while you’re deliberating, competitors are already acting—and pulling ahead.
