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From data modeling to knowledge graphs

Data modeling is essentially the process of building rich business context on top of raw data. However, building this context manually requires significant effort. A knowledge graph can subsume data modeling by providing the relevant context dynamically.

Navigating LLMs: Is newer the better for data tasks?

OpenAI released GPT-5 earlier this year and the tech community immediately jumped to upgrade their AI systems. This was a natural reaction given that every new GPT model has gotten substantially better over the years.

SQL Server 2014: A Milestone That Still Powers the World’s Data

While the rest of the world is racing toward the latest AI-native databases and cloud data platforms, I find myself reflecting on a product that’s now over a decade old — SQL Server 2014.

Why Search Scales Differently from Data or BI

Search box powered by AI is quickly becoming the new expectation for accessing enterprise data. However, interestingly, this search box can reside in multiple places: it can be right within the database platform where the data is stored, or in the BI tool where the dashboards are built, or be standalone like Tursio. In this blog, we discuss the scalability aspect with these different architectural choices.

From 80% to 100%: The Accuracy Marathon in Enterprise AI

When organizations evaluate AI solutions, accuracy often becomes the headline metric. But what those numbers don’t reveal is the story behind them: the people, processes, and persistence required to transform an engine from “good enough” to exceptional. This is critical since most AI tools struggle with accuracy and are anywhere between 50-80% accurate out-of-the-box, which is typically insufficient for production deployments. In this blog, we describe our war story of overcoming this gap, i.e., going from 80% to 100% accuracy in a production environment. In our experience, this journey is less about the technology itself and more about the collaboration that fuels its growth.

Will SQL be the new Assembly Language?

Accessing enterprise data shouldn’t be hard—but for billions of professionals, SQL remains a barrier. Despite decades of BI tools and dashboards promising “self-serve” analytics, business users are still blocked by complexity, slow workflows, and reliance on data experts. Text-to-SQL AI seemed like a solution, but auto-generated queries still require verification, leaving the SQL wall intact. Tursio reimagines data analytics by connecting natural language questions directly to enterprise data while ensuring correctness. By inferring semantic models, constraining AI queries to relevant data, and systematically building query plans, Tursio delivers accurate, interpretable, and actionable answers—making AI-powered analytics truly accessible.

Why AI fails despite "great” models?

We’ve all seen AI demos—type a question, get an instant answer. In reality, 95% of enterprise AI pilots fail—not because models are weak, but because prompting is hard. Most users aren’t prompt engineers, especially when querying structured data, leading to hallucinations and lost trust. At Tursio, Auto Mode solves this: it scaffolds questions based on what exists, what’s meaningful, and what’s probable in your data. Users explore, select, and accept—without worrying about SQL or prompt complexity. AI should adapt to humans, not the other way around. The result? Fast, reliable insights you can trust.

Redefining Productivity with AI

AI isn’t replacing people—it’s amplifying them. Across functions, AI acts as a force multiplier, automating repetitive work, accelerating research, and enabling faster, more confident decisions. Analysts interpret rather than wrangle data, marketers generate content and insights instantly, customer teams serve faster, and managers focus on strategy over reporting. The new productivity measure is decision velocity, not hours worked. Tomorrow’s professionals need data literacy, AI copiloting skills, and cross-functional agility. At Tursio, we empower teams to access structured data in natural language, removing barriers and unlocking human potential, so AI amplifies impact instead of just adding tools.

MCP for Databases: New trick for old elephants

In enterprise AI, making LLMs understand structured data is tricky. Many turn to LangChain for rapid prototyping, but Model Context Protocol (MCP) is emerging as a production-ready alternative—sometimes called the “USB-C for AI.” MCP servers translate natural language into schema-aware, secure queries, giving LLMs safe access to SQL, Snowflake, or FHIR databases without glue code. While LangChain excels at flexibility, MCP shines in regulated, structured environments. Tursio complements this by focusing on the human experience: effortless natural language querying, context-aware explanations, and insights for decision-makers. Together, structured access and user-centric interfaces define modern AI workflows.

Is 100x Productivity Possible with AI?

Knowledge workers waste nearly 20% of their time hunting for data. Dashboards, reports, and analytics tools exist—but insights are buried. True efficiency isn’t about doing more; it’s about getting answers faster. AI can remove friction between question and insight, letting anyone explore data instantly without SQL or analyst handholding. Imagine a marketer checking campaign performance mid-meeting, a PM spotting drop-offs in real time, or finance stress-testing budgets on the spot. This is 100x efficiency: decisions made at the speed of thought. The next advantage isn’t more data—it’s faster, smarter access to it.

How Credit Unions Are Winning with Generative AI

AI adoption for credit unions works best as a staged journey, not a leap. Start small with Level 1—quick, natural language insights for immediate ROI. Level 2 expands to advanced financial analysis across departments, enabling data-driven strategy, risk management, and growth tracking. Level 3 brings department-level simulations, scenario planning, and deep operational insights for Lending, Risk, and Member Services. Success requires strong data governance, simplified deployment, and empowering non-technical users. By starting smart, scaling fast, and aligning AI with business priorities, credit unions can unlock actionable insights, improve decision-making, and maximize value without the pitfalls of a “big bang” rollout.

Searching Clinical Data using Generative AI

Healthcare data is messy, and querying it effectively is critical for better patient outcomes. SearchAI leverages generative AI to enable natural language search over clinical databases, including ICD, CPT, NDC, MIPS, and modifier codes. Using Boolean decomposition, ontology-aware navigation, and instance-specific tuning, SearchAI interprets complex medical queries, maps them to hierarchical codes, and returns accurate results. Hierarchical flattening and hybrid approaches enhance precision and coverage, achieving up to 99% accuracy. Fast, robust, and semantically aware, SearchAI empowers clinicians and researchers to quickly access actionable information, bridging the gap between technical complexity and clinical usability, and transforming how healthcare data is explored.