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Accurate reporting is vital to keeping operations safe and compliant in high-risk industries like oil and gas. Whether it involves equipment inspections or structural assessments, these reports ensure that critical infrastructure (like petroleum storage tanks and extraction platforms) is properly maintained and operating within safe limits.
However, even in the golden age of digital transformation, many companies still depend on outdated, manual processes to handle this essential work. That leaves them exposed to delays, data loss, and human error — all of which can jeopardize both safety and regulatory compliance.
This is the type of challenge that Walter Frank Pintor Ortiz is tackling with his AI startup, Query Vary.
As a robotics engineer turned entrepreneur, Walter is creating custom AI tools aimed at turning unstructured data (like internal documents, measurements, images, and reports) into organized, actionable information. In sectors like oil and gas, Query Vary provides tools that inspectors can use to consolidate diverse information and generate critical reports quickly and accurately, ensuring compliance with strict regulatory standards.
From robotics engineer to AI entrepreneur
Walter began his engineering career in 2013 while completing a master’s degree in aerospace engineering. He started as an intern at Singapore’s Advanced Remanufacturing and Technology Centre (ARTC), a major research and development hub focused on making manufacturing processes more cost-effective and environmentally sustainable. He joined ARTC full-time the following year and spent the next seven years there, eventually rising to a tech lead role.
Over the years, Walter worked across several complex, cross-disciplinary projects in sectors like robotics and aerospace.
In one project, he helped develop an automated system to spray specialized fluids onto turbine blades — an essential process for protecting aircraft engines from extreme operating conditions. The system treated more than 5,000 blades in its first year, significantly reducing manual labor and contributing to safer, more reliable air travel.
But over time, Walter began to feel constrained by the pace of traditional R&D and became increasingly drawn to startups, where tools can be built much more quickly and refined in real-time.
This led Walter to found his first startup, Query Vary, which was accepted into Y Combinator’s Winter 2022 batch. The platform’s goal is to help organizations create their own custom AI systems without writing code. It accomplishes this by accepting unstructured data like internal documents, field measurements, images, meeting notes, and communication logs, then generating more usable outputs.
How Query Vary turns oil and gas inspections into instant documentation
Technical reporting is one standout area where Query Vary excels, and it’s a critical task for energy companies that must meet strict safety and regulatory standards across oil fields, pipelines, and storage facilities.
It’s an area in which Rosen, a multinational safety inspection firm, struggled and needed outside help. When the company contacted Query Vary, its Malaysia division was struggling to comply with API 653, an industry standard for inspecting above-ground storage tanks for corrosion, leaks, and structural failures — issues that, if missed, can lead to environmental damage, costly shutdowns, and safety hazards for workers and nearby communities.
At the time, inspectors relied on paper notebooks and outdated cameras to document their on-site findings, making it hard to capture and organize information effectively. Once back at their desks, they would then manually transcribe their notes into formal reports, a process that could take two to three weeks and often introduced transcribing errors and inconsistencies. These delays and inaccuracies increased the risk of missing critical issues, ultimately compromising the safety of the tanks.
To understand the root of these inefficiencies, Walter and his team visited Rosen’s site operations in Johor Bahru to study the inspection process firsthand. There, they found that inspectors were operating without reliable internet access and had limited ability to record their findings.
In response, the team at Query Vary developed a custom AI reporting system that could streamline the entire reporting workflow while adapting to inspectors’ specific needs on the ground.
First, inspectors would record their on-site findings (such as photos, notes, and checklists) using explosion-proof tablets built to withstand harsh conditions and capable of full functionality while offline. Once a connection becomes available and data is uploaded, an AI model processes the inputs, extracts and compiles relevant details, and generates a structured report aligned with API 653 standards, including proper formatting and language consistency.
The impact of this platform was immediate. With field data now captured more reliably and processed automatically, the company saw a sharp drop in input errors and missing information. Over time, the company saw report turnaround times drop by 90%, shrinking to an average of just two days — allowing Rosen to deliver higher-quality reports to its Malaysian clients and meet compliance standards more efficiently.
An automated operating system for field reporting
As of 2025, the approach used to build Rosen’s custom system has become a main offering of Query Vary’s service model, where Walter continues to serve as CTO. He now also holds this same role at his second startup, Filepillar.
Backed by VC firm Antler, Filepillar marks Walter’s expansion from data integration into financial compliance, applying similar automation techniques to streamline complex tax preparation and financial reporting, and providing companies with advanced tools that can help them gain a clearer view of their finances and produce more accurate reports.
Through Query Vary and its custom solutions for companies like Rosen, Walter Frank Pintor Ortiz is developing AI tools that adapt to the real-world needs of inspectors and field teams — replacing outdated processes with faster, more reliable systems.
