Companies are increasingly staking their future on AI. Yet, a simple but disquietening truth is emerging: how do you extract discrete certainties from a technology built on probabilities?
Enter Tiger Analytics. The company, with deep roots in data science and has made a name for itself by solving data science’s vexing challenges, is now sitting at the intersection of this next paradox. By bridging the gap between deterministic enterprise outcomes from fundamentally non-deterministic systems, the company is poised to set itself apart from a crowded field.
From probability to reliability: The Tiger Analytics approach
Tiger Analytics started as a “pure play AI and data science consulting firm,” well before generative AI took the spotlight. This early focus now gives them a strategic edge as clients rush to adopt AI solutions.
“Early start, speed, and scalability are what we see as key strengths for Tiger,” says Durjoy Patranabish, vice president and head of global business at Tiger Analytics. “It’s not that we are starting today. We’ve already started years ago, even before some of the companies started their AI journeys.”
This data science heritage advantage translates directly to implementation velocity, a critical factor for companies playing catch-up in the AI race. Patranabish cites a case study involving one of the U.K.’s top four banks: “We built a couple of solutions related to conversational AI over nine months. When we asked why they chose us despite having 1,000-plus internal data and AI specialists, they said they would have taken probably a couple of years to build that product.”
Patranabish notes that the challenge of managing AI’s inherent uncertainty differs widely across organizational cultures. To address this, the company has crafted tailored strategies for each client profile.
“On one extreme, you have fairly traditional organizations that have not completely adapted to technology,” explains Patranabish. “There, it becomes very difficult because you have the legacy ecosystem. We’re trying to say that we are only going to give you additional insights, but the decision is yours.” This careful messaging helps mitigate resistance in companies where technology adoption feels threatening.
“On the other side, you have organizations that have adapted to AI. They understand that if they don’t adapt to AI, they will be left behind. There, the whole concept of AI not being an exact science but more a probabilistic science is well understood.” For these organizations, Tiger Analytics shifts its focus from explaining probability to maximizing accuracy. “The only thing you are looking for is how close you can come to the accuracy and drive incremental business value.”
Honing on reliability: The multi-cycle approach
As AI enters regulated domains like healthcare and finance, reliability is critical. Tiger Analytics ensures this through iterative refinement focused on acceptable error thresholds.
“Reliability is context-based,” Patranabish emphasizes. “If there are situations in which you need to have 0% defect, then it becomes extremely challenging and complex to build the solution compared to a scenario where you want to achieve a certain outcome, even if there’s a 5% error.”
This contextual approach to reliability determines the solution design path at Tiger Analytics. “The solution can become more and more reliable as you do more and more refinements— and those refinements come through continuous feedback loops. You can hit 70%, 80%, 90% average accuracy fairly easily, but the last mile is always the most demanding part,” Patranabish observes.
At the heart of Tiger Analytics’ approach to delivering AI certainty through reliability is a deep understanding of the complex ecosystem in which AI operates. Recognizing that AI development doesn’t happen in isolation, Tiger Analytics strategically collaborates with a broad network of platforms, libraries, and providers. By leveraging robust ecosystems built by partners like hyperscalers and data platforms, they accelerate innovation while ensuring unwavering reliability.
“With the advent of cloud technologies and data platforms — whether Azure, AWS, Google Cloud, Databricks, Snowflake, and others — they are all building their ecosystems,” notes Patranabish. “Those ecosystems are growing exponentially day by day. You will be left behind if you are trying to build your ecosystem to deliver a solution.”
Instead, Tiger Analytics focuses on creating a three-way partnership dynamic with the client and these cloud & data platform partners. “It’s a three-way communication: You have the client, you have service consulting players like Tiger Analytics, and you have the cloud and data partners. It’s a fairly synergistic thing.”
Tiger Analytics has developed specialized teams to manage these relationships: “We have separate towers for each of these partners. We have major towers for Azure, GCP, and AWS, as well as teams for Databricks and Snowflake.”
Ethical AI and Bias: Meet the Consequentialist Framework
While reliability focuses on technical accuracy, AI deployment also demands ethical considerations. Tiger Analytics’s approach — particularly their “consequentialist framework” — addresses the complex trade-offs inherent in AI decisions.
“In healthcare diagnostics, we build solutions for predictive onset of diseases and predictive side effects on medications,” Patranabish explains. “Here is a simple example: on one side, you want the patient to leave your premises and return home as soon as possible. On the other side, you also want to avoid any subsequent re-admission for as long as possible.”
This creates a dilemma that AI solutions must navigate. “Shall I release [the patient] quickly, or shall I keep [the person] for some more time so that the time to return to the hospital is extended? Those are dilemmas that we help our clients balance,” adds Patranabish.
Similar frameworks apply across industries: “Whether product penetration is more important for you, or whether customer satisfaction is more important — those are the kinds of [concerns] we face not only in healthcare but also in other industries.”
For less regulated industries like retail, manufacturing, and CPG, market uncertainties create unique challenges, especially with data outliers and biases. Tiger Analytics addresses these with structured testing and continuous monitoring.
“Technically speaking, we can detect [uncertainties and biases] by having the right kind of sampling and testing done,” says Patranabish. It is also where Tiger Analytics plays to its data science heritage strengths when advising clients. “How do I design my dataset on which I’ll build a solution, on which I’ll test a solution, and then continue to do model monitoring to see if biases are creeping in with the advent of new data?”
This process isn’t a one-time fix but requires ongoing vigilance: “Normally, every six months to one year, we evaluate the current state. What is my current bias? How are my features performing from a model contribution standpoint as well as from a prediction standpoint? Are there any new data elements that are becoming significant?”
External shocks like COVID-19 in the past highlight the limitations of historical data: “We didn’t have any data. For forecasting, on what basis do you forecast for a COVID kind of situation? You look at a very small window of data and extrapolate or build it over a period of time.”
Navigating the regulatory maze
Part of the AI uncertainty stems from the different regulatory regimes sprouting across the world. And as global jurisdictions develop diverse regulatory frameworks around AI, Tiger Analytics is positioning itself as a navigator of these complex waters.
“These regulations are still in an unstable state,” observes Patranabish. “The European Union, the U.S., and Singapore have already created distinct regulations, while India and Australia are planning to create regulations. Everyone is observing what others are doing and trying to shape their own regulatory framework.”
Despite this fragmentation, Patranabish expects eventual convergence: “I believe that it will become a fairly standard regulatory framework across at least the major economies in the near future.”
Until then, Tiger Analytics builds expertise through cumulative experience: “If I’m working in the European Union [AI project] for the first time, for example, we will depend a lot on our clients to guide us. But by the third or fourth client, we have already learned from our first two or three experiences.”
The next frontier: Agentic AI
As AI transitions from probabilistic models to autonomous decision-makers with Agentic AI, Tiger Analytics is already positioning itself at the cutting edge of this evolution.
“We are building a lot of agents for our clients,” says Patranabish, revealing Tiger Analytics’s strategic pivot toward agentic systems. “Now it’s about ‘I need to build multiple agents on top of the platform that can do specific tasks for me.’”
This shift toward agentic AI represents more than just technological advancement — it fundamentally alters how companies interact with AI. Rather than simply consuming AI outputs, they will increasingly delegate entire decision workflows to specialized AI agents operating within guardrails of human-defined objectives and ethical constraints.
For Fortune 50 and 100 companies partnering with Tiger Analytics, the future of agentic AI is already in motion. These industry leaders are pioneering real-world implementations of autonomous systems, positioning themselves at the forefront of a shift that’s set to redefine entire industries and securing a powerful early-mover advantage.
The real power of Tiger Analytics’s approach lies in its cumulative learning. Each implementation across different clients and industries becomes part of the company’s expanding knowledge base, enabling it to develop increasingly sophisticated and reliable autonomous systems. This virtuous cycle of learning positions them perfectly to achieve their ambitious USD1B revenue target by 2030.
“In the race to crack AI certainty, agentic AI is both the ultimate challenge and the boldest solution. By shifting from static models to dynamic, self-directed systems, Tiger Analytics isn’t just tackling today’s uncertainty, it’s architecting the autonomous decision engines of tomorrow’s enterprise AI.
This article was originally published on CDO Trends on May 02, 2025
