By: Emily Rumball

Enterprise buyers have navigated a decade of unpredictability. Demos impress, contracts are signed, and then reality sets in. AI tools have often been presented as solutions to complex problems, but when it comes to practical business applications, the outcomes can vary. In many cases, this results in another visually appealing dashboard, which may not necessarily lead to tangible business results.

As Rafsan Bhuiyan has witnessed throughout his career, dashboards alone do not equate to revenue. The true ROI comes from action, particularly when driven by Large Language Model (LLM)-powered AI that effectively bridges the gap from insight to execution. Without this, metrics may simply become digital decoration.

In 2025, with budgets under heightened scrutiny and executives expected to demonstrate ROI in quarters, not years, the tolerance for unreliable technology has significantly decreased.

Bhuiyan feels that pressure firsthand. A decorated U.S. Army veteran turned AI strategist, he has designed systems that have contributed to billions in revenue for Fortune 500 companies before founding OrionQ, a unified AI RevOps platform tailored to operate in the dynamic, high-stakes environments of real business.

His philosophy is straightforward: If an AI platform isn’t capable of functioning effectively under pressure, within the complexities of real-world operations, it may not yet be suitable for the enterprise.

A Career Measured in Outcomes, Not Prototypes

At CarMax, Bhuiyan led a team of senior engineers to design and maintain the AI-driven personalization engine supporting a significant number of vehicle sales and a large finance portfolio. The system processed complex, real-time data from over 250 locations, providing recommendations that helped boost conversions on a large scale.

At T-Mobile, in collaboration with OpenAI, Bhuiyan helped equip over 7,500 executives with GPT-powered RevOps tools, scaling from around 2,500 users in a relatively short period. This wasn’t just a technology deployment; it involved a notable cultural shift. Executives were introduced to AI frameworks that supported their decision-making, integrating generative AI into daily operations while maintaining focus on business objectives.

“These weren’t pilots designed to impress in a demo,” Bhuiyan says. “The real challenge comes six months in, when the initial excitement has worn off and the stakes are higher than ever.”

From Bottlenecked RevOps to a 14-Day Deployment

Bhuiyan founded OrionQ in 2024 after observing a recurring challenge across industries: sales, marketing, and revenue teams often found themselves isolated, dependent on engineering and data science for integrations, data processing, and forecasting. These projects frequently suffered from unclear direction, “feature creep,” and manual processes that took 2–3 months before yielding actionable results. The outcome? Delays, attribution issues, duplicated efforts, and millions in missed opportunities.

OrionQ addresses this challenge with a unified “one brain” RevOps platform that can be deployed in 14 days or less. Its proprietary Q-Data Intelligence enhances leads with insights from climate, geospatial, and trend data, which can help improve conversion rates.

From there:

• The Marketing AI Agent runs sentiment-aware campaigns with predictive ROI and auto-scheduling.

• The Rev AI Agent manages autonomous outreach via voice, SMS, and email, following up on missed leads, booking meetings, and managing contextually relevant follow-ups.

Each action is powered by personalization. Since every company is unique, and every customer’s needs differ, OrionQ’s self-improving AI Agents combine an up-to-date knowledge base with trained AI models to truly understand both clients and their customers, surfacing only the products, services, and insights most pertinent to them.

The result: precise, tailored information that drives action, without overwhelming users with lengthy, generic AI-generated responses.

Pricing That Encourages Accountability

Rather than relying on static licenses that often go unused, OrionQ adopts a QTokens model, where clients pay based on verified outcomes, such as a qualified lead, a launched campaign, or a booked meeting.

“Accountability shouldn’t be an add-on,” Bhuiyan states. “If we say we’ll deliver something, you should be able to see exactly what you’re getting and what it’s worth.”

Military Precision, Applied to AI

Bhuiyan’s Army service shaped his approach to technical deployments, emphasizing clear mission objectives, disciplined execution, and adaptability in changing circumstances.

He recalls the inefficiencies of military bureaucracy—endless paperwork, communication delays—and how these experiences fueled his drive to eliminate wasted effort.

“If AI can help clear away the administrative clutter, leaders can devote more time to strategic thinking, innovation, and making critical decisions,” he says.

Winning in High-Responsiveness Industries

OrionQ’s roadmap focuses on sectors where speed and context are critical to revenue success. A construction firm that takes hours to respond to an inquiry risks losing a million-dollar project. A professional services firm that follows up too late may never get a second opportunity.

By tailoring AI agents to the seasonal patterns, buying signals, and data flows of each industry, OrionQ aims to offer not just automation, but performance designed to scale effectively and consistently over time.

Beyond the Hype Cycle

In an AI market filled with “launches” that fade soon after the press release, OrionQ sets itself apart as a more measured alternative: a platform led by a founder who has operated in environments where performance is critical.

For decision-makers seeking collaborative solutions that translate into real results, Bhuiyan offers a reliable option: “We’re here to be your dependable partner, from day one to day 365.”