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One of the biggest transformative opportunities for AI lies in the physical industries. These sectors have long been a walled garden – served by legacy industrial giants and sector-specific consultants, not modern software companies. That’s been due to a mix of proprietary, unstructured data, complex implementation needs, and distinct cultural dynamics.
With AI, this dynamic is shifting fast. The white space for startups to bring advanced, scalable, and agentic AI into physical industries is both compelling and defensible. These solutions offer a step-change in value over the status quo – and the data needed to make them work isn’t publicly available, giving focused startups a durable edge over large model providers.
[Note: We recently shared our thoughts on this shift - how domain-specific AI “brains” for industry will transform the largest, least-digitized segments of the economy, and how Systems of Action will unlock unprecedented value for these markets.]
That’s why we’re thrilled to announce that we’re leading the $10M Seed round for Intuigence, building the modern AI brain and copilot for process and chemical engineers.
Process manufacturing is the backbone of downstream oil refining, petrochemicals, and base chemicals – industries where engineers oversee complex chemical, thermal, and catalytic transformations to turn raw hydrocarbons into fuels and feedstocks. These sectors represent a ~$4 trillion global market, with hundreds of billions more spent annually on capital projects, maintenance turnarounds, and engineering services.
Despite their scale and complexity, they remain among the most under-digitized sectors in the global economy – and among the most emissions-intensive, contributing 6 - 8% of total greenhouse gas emissions, on par with agriculture, cement, or steel. A single large refinery or cracker can cost $5B - $20B to build and operate for 50+ years.
AI provides the solutions to some of the industry’s largest and most pressing challenges:
The industry faces a looming talent cliff. In oil & gas (O&G), 30 - 50% of skilled workers are set to retire by the end of the decade and will take with them decades of hard-won, often undocumented knowledge. The highly cyclical nature of O&G meant that few Gen Xers entered the field in the '80s and '90s, creating an unfilled generational gap.
Leaders across the sector consistently cite this as the top threat to operations. Capturing, retaining, and scaling this disappearing expertise is an urgent challenge – and one perfectly suited to AI copilots that can learn from historical data, conduct expert interviews as engineers retire, and assist new engineers in real-time.
Today’s process manufacturing industries like O&G run on disconnected and antiquated systems. Documentation is often spread across millions of PDFs in siloed, on-prem databases – or in general-purpose, ‘old-school’ tools like Excel, SharePoint, PowerBI, or SAP. Engineers routinely spend ~30% of their time just searching for the right document to troubleshoot an issue or approve a management-of-change (MOC) request.
Even the more industry-specific platforms like Aveva, WellView, Cognite, and Enverus are limited in how they support front-line engineers. Every O&G facility is a unique, intricate and constantly changing web of piping, pumps, control loops, valves and tanks which most pre-AI systems are too brittle to handle.
Due to this complexity, implementation of these legacy systems is notoriously high-touch. Vendors do tens of billions in revenue with their services-heavy approach, creating custom software stacks that break easily and are expensive to maintain. This status quo signals the exact kind of jump ball moment we look for when a platform shift like AI arrives. AI systems architected with cutting-edge data preprocessing and flexible, relational data schemas will unlock profoundly faster time-to-value, breaking the mold to deliver truly scalable, intelligent solutions.
Lastly, the lack of system-level intelligence forces plants to operate conservatively – trading performance (both cost and emissions reductions) for risk mitigation. AI flips that equation – by providing context-aware insights and a dynamic understanding of the system, enabling engineers to optimize performance without jeopardizing safety.
IntuigenceAI is building the AI-native platform for O&G, chemicals and process manufacturers – sitting on top of legacy systems of record – to augment engineering teams, improve margins, reduce emissions, and help operators run these massive systems with greater safety, speed, and insight. Because these industries are distinctly tight-lipped with their data, the opportunity for Intuigence to develop specialized models and workflows – trained and tuned on customer data behind the firewall – is an especially compelling competitive moat.
IntuigenceAI is starting with the most foundational blueprint of refining operations: Piping & Instrumentation Diagrams (P&IDs). These technical diagrams are the ground truth for how refineries operate. They guide everything from day-to-day procedures, to $500M deep maintenance turnarounds which bring facilities to a grinding halt.
P&IDs are the foundation, but full detail of the refinery requires combining many unstructured, highly-dimensional and highly varied documents, including single-line diagrams, equipment specs, maintenance logs, operations manuals, safety reports and more.
Intuigence uses advanced computer vision, fine-tuned LLMs, multimodal models and graph-based AI to autonomously ingest, interpret, and digitize these P&IDs into rich knowledge graphs layered with spatial, geometric, and topological context. On top of this foundation, they’re building agentic workflows for root cause analysis, mass balance calculations, compliance reporting, turnaround & isolation planning, and more - helping engineers make smarter, faster and safer decisions.
Critically, Intuigence links these AI systems into live sensor data from the plant, turning static documentation into a dynamic, queryable, decision-driving asset.
Alongside this intelligent System of Action foundation, Intuigence has trained highly-specialized, virtual AI-engineers on a vast corpus of public and non-public chemical, process and controls engineering documentation and literature. These domain-specific models possess PhD-level understanding of industrial facilities – and outperform leading frontier models by 8x on standard benchmarks. IntuigenceAI is expanding its engineering bench with mechanical, electrical and EPC engineering agents set to be released later this year.
IntuigenceAI’s wedge – dynamic, queryable P&IDs – is compelling because it mirrors the software archetypes we’ve seen disrupt other other heavy industries. Take Trunk Tools in construction: they ingest thousands of blueprints and drawings, structure them into machine-interpretable, queryable formats, and then build vertical AI workflows on top. Intuigence is doing the same for the O&G industry.
Of course, building a System of Action for an industry like O&G or chemicals is no mean feat. It demands a deep understanding of regulatory complexity, safety-critical environments, and the organizational hairballs that define most industrial operators. It also requires ruthless product focus to deliver a scalable platform that actually drives value in the field.
That’s why we’re backing a team with not just technical horsepower, but rare domain credibility, earned insight into what matters to these buyers, and the experience to close enterprise deals with industry leaders.
Moe Tanabian, the founder and CEO of IntuigenceAI, brings exactly that blend – having sat at the intersection of AI and industrial data for over a decade. As Corporate VP at Samsung, he launched AI-powered products that bridged hardware and software. At Microsoft, as the Global Vice President, he led Microsoft’s Industrial portfolio including Azure IoT, Azure Digital Twin, Windows IoT and Azure Industrial AI globally. Most recently, Moe was Chief Product Officer at Cognite – the first Norwegian unicorn – where he built AI solutions used by global industrial giants like BP, Saudi Aramco, and Koch. Moe helped define the last generation of industrial AI. With IntuigenceAI, he’s building what comes next – backed by an exceptional team of engineers and researchers focused on bringing cutting-edge intelligence to one of the world’s largest, most complex, and least digitized markets.
We believe IntuigenceAI is on the path to becoming the industrial AI brain for process manufacturing – starting with downstream O&G, then expanding into chemicals and beyond. This is vertical AI at its best: a deeply embedded team solving high-friction, high-impact workflows in a market that’s massive, sticky, and long overdue for reinvention. We’re excited for what’s ahead.
