Every stone finds its place. Every rock finds its balance.
Carefully stacked, beautiful rock flowers bloom in the hands of Japanese artist Ishihana-Chitoku. Echoes of the Japanese rock balancing art form ‘ishihana’ and visually similar to bonsai and ikebana; the stones are carefully chosen, and surprisingly quickly (at times under a minute), the rock flowers come to life. No glue, no tricks, just experience, precision, balance, and an eye for harmony.
In a fast-paced, rapidly evolving world and industry like ours, sometimes balance can make all the difference. How do you stay grounded as the winds of change continue to assail?
Our 14-year journey as Tiger Analytics has taken us to many destinations. There’s a lot of history to write when you have an indefatigable entrepreneurial spirit, an appetite to learn, and a desire to keep (tech) stacking the ‘rock flowers’ of code until you crack the solution. From partnering with Fortune 100 clients across varied industries to creative problem-solving that punctuates our ‘eureka!’ moments. From building AI-powered tech that supports people and ideas, room to reimagine what’s possible… We continue to learn and grow.
As we’ve always said… What we do matters; How we do it matters more. To grow higher, we must first grow deeper roots.
In our special anniversary edition of AI of the Tiger, we’ve crystallized this balance and growth in STAQD, our framework for building with intent. Join us as we explore how each of the elements works in practice:
S – Stewardship: Purpose-driven innovation
Time seems to move more slowly when waiting for test results at the doctor’s office. Behind the wait time is a maze of patient data, diagnostic scans, and treatment plans that need to be brought together to give patients and families much-needed clarity. We worked with a global pharmaceutical company to build a privacy-first, AI-powered solution that provided doctors with one connected view of all data, improving cancer detection and treatment. The simple question driving this project – How can tech make a difference? This spirit has shaped our work across oncology labs, dairy farms, hospitals, and other domains. A multinational dairy cooperative was on a mission to deliver specialized nutrition to those who need it the most. By modernizing their SAP and Azure integration, we helped spot once-hidden patterns, providing the insights they needed to serve communities more effectively. Backed by AI-driven predictive models and responsible analytics platforms, healthcare providers improved the effectiveness of preventive measures, diagnostic accuracy, patient outcomes, and long-term disease management. This is stewardship, using technology to make a positive change in people’s lives.
T – Trust & Governance: Building AI that’s ethical, responsible, and accountable
For every system we build, the people who rely on it need to be absolutely certain it works for them. As we integrate more data, scale models, and entrust them with more decisions, governance ensures that each insight is accurate, each outcome explainable, and each action accountable. This means building new roadmaps and adding to our existing frameworks to embed fairness and accountability in every step, from auditing datasets to remove hidden bias, to continuously monitoring model outputs, to creating traceable decision logs so actions are explainable. By operationalizing governance, we provide people the confidence to use the systems made for them, knowing that their data is safe, handled ethically, and being used to create insights that they can trust.
A – Aspiration: Architecture that lasts, insights that evolve
Which GenAI solution architecture is right for you? Enterprise Model APIs, or Custom LLMs, or New foundation models? At every turn, we are faced with several choices, each promising a different breakthrough. The challenge is not only identifying which solution is right for you today, but also engineering a reliable foundation for tomorrow. So, we go back to the basics – solid architecture, well-defined data pipelines, and single sources of truth. For instance, at a leading U.S. life insurance company, unifying data and analytics into one platform gave business teams faster, more reliable access to policyholder insights. Meanwhile, at Malaysia’s premier transshipment port, PTP, a Databricks Lakehouse became the backbone for real-time operational visibility, enabling stakeholders to coordinate decisions with ease. This is what aspiration looks like: making deliberate choices that strengthen our ability to adapt, grow, and endure.
Q – Quality: The discipline that turns rigor into reliability
Who doesn’t want results they can trust? As the speed of AI model upgrades, new tech debuts, and AI agent use cases increase exponentially, success and failure in decision-making may hinge on important questions – how trustworthy is your data? How scalable are the results? Our take? Reliable results come from quality data. A Fortune 500 financial services firm wanted to improve its monitoring mechanism and standardize data quality. By combining our observability-as-a-service framework and automated checks with hands-on validation, we helped reduce time spent on correcting errors and improve the consistency of insights. It’s this combination of data quality, observability rigor, and human diligence that led to our Tiger Data Quality Framework. Built on lessons learned from multiple engagements, it codifies quality practices while giving teams the flexibility to adapt to specific business contexts. Quality is reliability in practice, giving teams a steady foundation that cuts through uncertainty.
D – Domain Knowledge: Grounding tech in human context for real impact
Like a bridge over uncertain waters – technology needs to understand what domains want to solve. Domain fluency meets technical mastery to create meaningful solutions. When Mead Johnson Nutrition, a global leader in pediatric nutrition, modernized its data platform, we provided a solution that was built with a deep understanding of regulatory and scientific nuances to ensure insights could be trusted by both researchers and families, accelerating access to specialized nutrition. For a financial services client, solving merchant identity extraction required fluency in compliance regulations and fraud detection. We delivered cleaner, faster verification and reduced risk. Modernizing analytics for Victoria’s Secret & Co meant designing systems in step with seasonal demand, giving teams a comprehensive view to act on fashion trends with speed and precision. And for a global bakery café chain, ML-driven forecasts succeeded because they accounted for the realities of operations from ovens to queues, improving planning and keeping shelves stocked.
Across these stories, the approach, the technology, and the outcome vary. What unites them is the role of domain context across retail, healthcare, financial services, and more, ensuring that the tech stack works in ways that matter and in the language of the people who use it.
STAQD captures the principles that have always guided us, but it is our #TigerTribe that brings them to life, showing up every time and raising the bar. At the heart of Artificial Intelligence will always be the human intelligence that powers it, and that’s the Tiger Analytics — our loved ones, people, partners, alumni, and well-wishers — we celebrate this month. Here’s to the next chapter of building with intent and the impact that is still to come.
This edition was originally published on LinkedIn on September 4, 2025.
