Artificial intelligence does not run on models alone. Behind every production deployment, every inference call, every training run that generates a capable model, there is an infrastructure layer that most people never see and almost everyone underestimates. The companies building that layer are solving problems that are harder, less glamorous, and more consequential than the demos suggest: how to get a custom model into production in five minutes without a team of infrastructure engineers, how to cut $3 billion in cloud spend without touching a single line of application code, how to train AI that never leaves the building, how to run inference on a satellite in orbit. From autonomous cloud optimization to GPU acceleration at the kernel level, from federated computing for sensitive industries to multilingual ML that works at global scale, these are the scale-ups building the technical substrate that makes the AI economy function.
Companies are listed in alphabetical order. This list is non-exhaustive.
1. AFTERQUERY
Headquarters: San Francisco, CA | Total Funding: ~$30M
The quality of an AI model’s output is ultimately a function of the quality of the data it was trained on. That observation is well understood in theory and routinely neglected in practice. Most training datasets for frontier AI models consist of prompt-response pairs with no explanation of the reasoning behind each answer, making it difficult for models to generalize the lessons they learn to novel tasks. The reinforcement learning phase, where a model completes sample tasks and receives feedback, is similarly constrained by the quality of the feedback signal and the realism of the tasks used to generate it.
AfterQuery is the expert-dataset company that closes that gap. Its datasets are distinguished by a critical differentiator: alongside every prompt-response pair, AfterQuery provides a structured, step-by-step account of the reasoning process behind each answer, giving models richer signal to learn from at every training stage. The company generates its datasets with the help of nearly 100,000 developers, attorneys, and other domain professionals, and also provides multimodal training data and datasets optimized for specific training phases including reinforcement learning. Founded 14 months before its funding announcement, AfterQuery raised $30 million led by Altos Ventures in April 2026, with participation from Y Combinator, The Raine Group, and BoxGroup. Annual recurring revenue had already exceeded $100 million at the time of the raise, a figure that places it among the fastest revenue ramps of any AI data company. Every leading AI lab is a customer. For the labs and enterprises trying to build models that reason rather than retrieve, AfterQuery is the data infrastructure that makes the difference.
2. AIRA TECHNOLOGIES
Headquarters: Fremont, CA | Total Funding: ~$32M
The wireless communications industry is undergoing its most structurally significant shift in a generation. 5G and 6G networks generate enormous volumes of spectrum utilization data, interference patterns, and channel state information that traditional signal processing approaches cannot use in real time. The result is that wireless networks operate significantly below their theoretical efficiency, wasting spectral resources, degrading user experience in dense environments, and failing to adapt dynamically to the rapidly changing conditions that modern networks encounter every second.
Aira Technologies applies machine learning to the wireless physical layer, the software that governs how spectrum is used, signals are encoded, and interference is managed in wireless networks. Its ML-native PHY approach replaces legacy signal processing algorithms with models trained on real-world wireless data, enabling real-time adaptation to channel conditions that fixed algorithmic approaches cannot handle. The company has raised $32 million from investors including Lux Capital, National Science Foundation, and NVIDIA, and its technology has been validated in partnership with major carriers and equipment vendors testing next-generation wireless infrastructure. Aira holds multiple patents on ML-based signal processing and has demonstrated throughput improvements of up to 60% in dense deployment environments compared to conventional approaches. As 6G standardization accelerates and spectrum efficiency becomes a direct commercial differentiator for network operators, the infrastructure layer that makes wireless networks intelligent is moving from a research priority to a procurement one.
3. AMBER SEMICONDUCTOR
Headquarters: Dublin, CA | Total Funding: ~$80M+
AI data centers have a power problem that sits below the level at which most infrastructure discussions operate. The efficiency of a data center is not just a function of its GPUs or its cooling systems; it is also a function of how power is converted, distributed, and delivered from the grid to the individual processor. Conventional power delivery architectures route electricity through multiple AC-DC and DC-DC conversion steps, each of which introduces losses that compound across thousands of racks. At scale, the wasted power from inefficient distribution is measured in megawatts. At a moment when AI data center power demand is doubling every two to three years and grid capacity is the binding constraint on expansion, power delivery efficiency is not a peripheral concern.
Amber Semiconductor’s solution to that problem is architectural. Its power management tile mounts on the backside of a circuit board, enabling power to be delivered through a vertical path that eliminates horizontal distribution losses, replaces over 33 discrete power ICs per board, and integrates DC-DC conversion directly at the point of load. The company raised $30 million in a Series C in Q1 2026, bringing total funding to over $80 million from investors including Khosla Ventures and Lux Capital. Amber’s approach is particularly well-suited to AI accelerator boards and GPU clusters, where power delivery density is highest and efficiency losses are most consequential. Its technology has been validated with hyperscale data center operators and AI accelerator manufacturers. For a data center industry where every percentage point of power efficiency translates to millions of dollars in operating costs and weeks of grid capacity, Amber is working at exactly the right layer.
4. CIRCUIT
Headquarters: San Francisco, CA | Total Funding: Undisclosed (early-stage)
Machine learning models are only as good as the features they see. Feature engineering, the process of transforming raw data into the structured signals that ML models can learn from, is one of the most labour-intensive and least systematized steps in the entire ML development lifecycle. Most organizations either hand-code features for each model, rebuild them from scratch when models change, or accept that their models are operating on suboptimal representations of the data they have. The result is that feature development is a significant bottleneck in ML iteration speed, and the institutional knowledge embedded in feature pipelines is almost never reusable across teams.
Circuit is the feature extraction and management platform that industrializes that process. Its system provides a structured, versioned layer for defining, computing, and serving features to ML models, enabling teams to build once and reuse across models and use cases rather than rebuilding feature logic for every new experiment. The platform integrates with existing data infrastructure including data warehouses, streaming systems, and model training pipelines, and provides both batch and real-time feature serving for production model inference. For ML engineering teams that have spent months of cumulative engineering time rebuilding feature pipelines that already existed somewhere else in the organization, Circuit is the infrastructure that makes feature work a shared organizational asset rather than a per-project cost.
5. HUGO TECHNOLOGIES
Headquarters: New York, NY | Total Funding: ~$15M+
AI models improve when the data processing operations that feed them are fast, accurate, and systematically governed. Most organizations struggle with exactly that: data annotation workflows that are fragmented across vendors, quality control processes that are manual and inconsistent, and pipeline infrastructure that makes it difficult to move from raw data to model-ready datasets at the speed that modern AI development demands. The gap between the data an organization has and the data its models need is often a gap in operational infrastructure rather than in raw data supply.
Hugo Technologies builds the AI data processing operations platform that closes that gap. Its system manages the end-to-end workflow of data annotation, quality assurance, and dataset management for organizations building and continuously improving AI models. The platform provides structured task management for annotation teams, automated quality control layers that flag inconsistent or low-confidence labels before they enter training pipelines, and analytics that give ML teams visibility into the data quality metrics that drive model performance. Hugo’s model combines technology-enabled human expertise with systematic process controls, producing datasets that are both accurate and auditable. For AI teams that have learned through painful experience that model quality is constrained by data quality, Hugo is the operational infrastructure that makes data quality manageable at scale.
6. INSIGHTFINDER AI
Headquarters: Raleigh, NC | Total Funding: ~$40M+
Enterprise AI deployments have created a new category of observability problem. Traditional application monitoring tracks whether systems are up, whether latency is within threshold, and whether error rates are acceptable. AI systems introduce a different failure mode: models that are running, responding, and appearing healthy while quietly drifting from the performance characteristics they exhibited at deployment. A fraud detection model whose training data no longer reflects current fraud patterns, an LLM agent whose response quality has degraded under a subtle infrastructure change, or a recommendation system whose outputs have shifted with no corresponding alert in any monitoring dashboard are the characteristic failure modes of AI in production. They are invisible to conventional observability tools.
InsightFinder AI has been solving the harder version of this problem for a decade. Founded by CEO Helen Gu, a computer science professor at NC State University who previously worked at IBM and Google, the company built its platform on 15 years of academic research into unsupervised machine learning for infrastructure anomaly detection. Its newest product, Autonomous Reliability Insights, extends that platform to AI-specific observability: detecting model drift, diagnosing root causes in infrastructure that may be invisible to conventional monitoring, and remediating issues before they affect users. The company raised $15 million in a Series B led by Yu Galaxy in April 2026, with its revenue growing threefold in the prior year. Customers include UBS, NBCUniversal, Lenovo, Dell, Google Cloud, and Comcast. A major U.S. credit card company used InsightFinder to trace model drift to outdated cache in server nodes before the degradation affected fraud detection outcomes. For enterprises where AI system failures translate directly to financial and regulatory consequences, InsightFinder is the observability platform that sees what conventional monitoring cannot.
7. LANGUAGE I/O
Headquarters: Cheyenne, WY | Total Funding: ~$25M
Global enterprises run customer support operations in dozens of languages simultaneously, and most of them do it badly. The standard approach is to hire bilingual support agents, route non-English tickets to regional teams with longer response times, or use generic machine translation that produces outputs accurate enough to be technically correct and wrong enough to create friction with customers who can tell the difference. The underlying problem is not translation quality in isolation; it is that generic translation tools do not understand the brand-specific vocabulary, domain jargon, abbreviations, and product terminology that make customer support interactions accurate and on-brand.
Language I/O is the multilingual ML platform built specifically for enterprise customer support. Its platform plugs directly into the tools support teams already use, including Salesforce, Zendesk, ServiceNow, and Microsoft Teams, and delivers real-time, business-accurate translation calibrated to the specific vocabulary and terminology of the deploying organization. Unlike generic translation APIs, Language I/O trains on customer-specific language patterns and updates its models continuously as terminology evolves. The platform supports live chat, email, chatbot, and social messaging channels, enabling Fortune 500 support organizations to deliver native-quality multilingual experiences without hiring language-specific agents for every market. Founded and led by CEO Heather Shoemaker, the company has raised $25 million across five rounds, with the most recent funding in 2024 from Gutbrain Ventures, Joint Effects, and others. For global enterprises where the quality of multilingual customer support is a direct driver of retention and NPS, Language I/O is the ML infrastructure that makes consistency possible across every language and channel.
8. LATENT AI
Headquarters: Princeton, NJ | Total Funding: ~$22.5M
The AI industry has been built around centralized compute: models trained in data centers, inference served from cloud endpoints, and the assumption that connectivity is reliable and latency is acceptable. For a growing class of AI applications, none of those assumptions holds. A drone identifying targets in a GPS-denied environment cannot route inference through a cloud API. A satellite processing imagery in low Earth orbit cannot wait for a round-trip to a ground station. A defense system operating in a communications-contested environment cannot depend on any connection to central infrastructure. Edge AI, genuine intelligence running on constrained hardware in disconnected or latency-critical environments, is not a niche use case; it is a requirement for the most demanding deployments in defense, industrial, and enterprise contexts.
Latent AI is the global leader in edge AI deployment tooling, with a platform that enables developers to optimize, compress, and deploy neural networks on any device without compromising accuracy or requiring changes to existing ML infrastructure or frameworks. Spun out of SRI International in 2018 by CEO Jags Kandasamy and CTO Sek Chai, the company’s LEIP platform compresses large neural networks by up to 90% with less than 1% accuracy loss, and its agentic edge AI platform, launched in June 2025, enables streamlined MLOps for edge deployments at scale. The company has raised $22.5 million across five rounds, with a Series A in August 2025 from Voyager Space and a follow-on investment from AUM Ventures in March 2026. Latent AI has been named among the top 50 hottest edge hardware, software, and services companies of 2025 and partners with Wind River for mission-critical edge AI in defense and aerospace. One manufacturer using the platform reduced required GPUs by 92%. For organizations that need AI to operate where the cloud cannot reach, Latent AI is the deployment infrastructure that makes it real.
9. MONTYCLOUD
Headquarters: Redmond, WA | Total Funding: ~$30M+
Cloud operations teams are fighting a battle on two fronts. On one side, cloud environments are growing in complexity: more services, more configurations, more accounts, more regions, and more policies to enforce across all of them. On the other, the manual tools and governance frameworks that most organizations use to manage that complexity were designed for a simpler era. The result is a chronic gap between the policies an organization intends to enforce and the state of the cloud environments it actually runs, measured in misconfigurations, cost overruns, and compliance exposures that accumulate faster than operations teams can close them.
MontyCloud is the AI-powered cloud management platform that closes that gap autonomously. Founded by Venkat Krishnamachari and Kannan Parthasarathy and led by CEO Walter Rogers, the platform continuously monitors cloud environments across AWS, Azure, and GCP, enforces governance policies automatically, identifies cost optimization opportunities, and executes remediation actions without requiring manual intervention from operations teams. The company raised an $11.4 million Series B led by Riverside Acceleration Capital in January 2026, with participation from Lytical Ventures, S3 Ventures, Madrona Venture Group, and Raptor Group. MontyCloud now has 85 employees and is part of a broader industry shift toward treating cloud governance not as a periodic audit exercise but as a continuous, automated operational discipline. For engineering leaders who have watched cloud spend and compliance risk grow faster than their operations headcount, MontyCloud is the platform that brings the two back into alignment.
10. PARASAIL
Headquarters: San Francisco, CA | Total Funding: ~$42M
Developers building AI-native applications face a deployment problem that the cloud industry has not yet solved. Major cloud providers offer GPU compute, but access requires long-term contracts, complex setup, and vendor lock-in that prevents teams from optimizing across the global supply of available compute. The alternative, managing a patchwork of GPU providers directly, requires infrastructure engineering resources that most AI teams do not have and cannot afford to build. The result is that getting a custom AI model into production at scale still takes weeks, not minutes, and costs more than it should.
Parasail is the AI Supercloud that removes that friction. Founded by CEO Mike Henry, the platform aggregates GPU compute from 40 data centers across 15 countries, applies an AI orchestration engine that automatically matches workloads to the optimal compute configuration for speed, throughput, and cost, and delivers production-ready endpoints in minutes with five lines of code. Since launching in April 2025, Parasail processes over 500 billion tokens per day and has achieved 30% month-over-month revenue growth, with customers including Elicit, mem0, Gravity, Kotoba, and Venice. The company raised $32 million in a Series A co-led by Touring Capital and Kindred Ventures in April 2026, with participation from Samsung NEXT, Flume Ventures, and Banyan Ventures, bringing total funding to $42 million. Kindred Ventures managing partner Steve Jang described Parasail as “the first agent-focused inference and training solution” for a world where agents call multiple models at runtime and require massive, flexible token throughput. For AI builders who should not have to become infrastructure experts to ship great products, Parasail is the infrastructure that removes that requirement.
11. RHINO FEDERATED COMPUTING
Headquarters: Boston, MA | Total Funding: ~$47M
Some of the most valuable data in the world for training AI models cannot be moved. Patient records across hospital networks, financial transaction data across competing institutions, sensitive government datasets across agency boundaries — the data that would produce the most capable specialized AI models is precisely the data that cannot be centralized, shared, or transferred without violating privacy regulations, contractual obligations, or institutional trust requirements. The dominant response to this problem has been to accept the constraint and train on less data, producing models that are less capable than they could be.
Rhino Federated Computing takes a different approach. Its federated computing platform enables AI model training and validation across distributed datasets that never leave their source environments. Hospitals, payers, pharma companies, and research institutions can contribute their data to collaborative model development without exposing it to other participants or to any central infrastructure, with Rhino’s platform orchestrating the distributed computation and aggregating model updates rather than raw data. The company has raised $47 million from investors including Alumni Ventures, Northpond Ventures, and General Catalyst, and operates across more than 200 healthcare sites globally. Its federated network has been used to train AI models for radiology, oncology, and rare disease applications where no single institution holds sufficient data to train a capable model alone. For regulated industries where the data needed to train better AI is locked behind privacy walls that cannot be moved, Rhino is the infrastructure that makes collaboration possible without compromise.
12. SEDAI
Headquarters: San Francisco, CA | Total Funding: ~$37M
Cloud infrastructure costs are the most predictable waste in the modern enterprise, and the hardest to eliminate without the right tooling. Idle resources, over-provisioned instances, conservative autoscaling limits, and forgotten safety buffers quietly compound across thousands of cloud services, adding up to what industry data suggests is 28 to 35% of total cloud spend in over-provisioned capacity alone. The problem is not that engineering teams do not want to optimize; it is that cloud environments change continuously, manual reviews fall behind, and the work of right-sizing infrastructure is too granular and too constant to be addressed through periodic audits.
Sedai is the self-driving cloud, the first platform to autonomously manage production infrastructure using deep reinforcement learning rather than rules, alerts, or threshold-based automation. Founded and led by CEO Suresh Mathew, the platform connects directly to AWS, Azure, and Google Cloud, learns each environment’s actual behavior under real traffic conditions, and then autonomously adjusts compute, storage, and scaling configurations to optimize for cost, performance, and availability simultaneously. It has executed over 25 million autonomous actions in production, managing $3 billion in cloud spend across hundreds of thousands of cloud services, delivering more than $5 million in annual savings for enterprise customers and reclaiming over 22,000 hours of engineering time, with zero production incidents. The company raised a $20 million Series B led by AVP in June 2025, with participation from Norwest, Sierra Ventures, and Uncorrelated Ventures, bringing total funding to approximately $37 million. Customers include Palo Alto Networks, Experian, and McGraw Hill. Sedai grew revenue 7x in 2024 and converted 92% of its proofs of concept into paying customers. For engineering leaders who have spent years wondering why their cloud bill keeps growing, Sedai answers the question and fixes it at the same time.
13. SOLID
Headquarters: San Francisco, CA | Total Funding: Undisclosed (early-stage)
Data is the input to every AI system, and most organizations’ data infrastructure was not designed to serve AI workloads efficiently. AI agents, model training pipelines, and real-time inference systems all have data access patterns that differ fundamentally from those of traditional business intelligence or transactional applications: they require high-throughput batch retrieval, low-latency vector lookups, cross-source joins across structured and unstructured data, and governance controls that can operate at the speed of inference rather than the speed of a quarterly audit. Organizations trying to connect their existing data infrastructure to their AI systems often discover that the gap is not just a configuration problem; it is an architectural one.
Solid is the AI-powered data gateway that bridges that gap. Its platform provides a unified access and routing layer between enterprise data sources and the AI systems that need to consume them, handling the transformation, caching, access control, and delivery mechanics that make data accessible to AI workloads at the speed and format they require. The gateway integrates with existing data warehouses, lakes, and operational databases, abstracting the complexity of cross-source data access behind a single interface that AI agents and ML pipelines can query without requiring data engineering involvement at the point of use. For data and ML engineering teams that have spent months building custom pipelines to connect AI systems to data sources that should have been accessible from day one, Solid is the infrastructure that makes data a first-class citizen in the AI stack.
14. STANDARD KERNEL
Headquarters: San Francisco, CA | Total Funding: Undisclosed (early-stage)
GPU performance is ultimately determined by the quality of the software kernels that orchestrate computation on the hardware. Writing high-performance GPU kernels for AI workloads has historically required specialized expertise in GPU architecture, parallel programming, and performance optimization, expertise that is scarce, expensive, and not scalable with the pace at which new AI model architectures are being developed. The gap between a model’s theoretical performance on a given GPU and what it achieves in practice is often a gap in kernel quality, and that gap widens with every new architecture that outpaces the available kernel optimization expertise.
Standard Kernel is closing that gap using AI. Its platform uses AI-generated kernel optimization to automatically produce high-performance GPU kernels for AI inference workloads, enabling models to run faster and more efficiently on existing hardware without requiring specialized kernel engineering expertise. The approach is particularly well-suited to an AI landscape in which new model architectures are released faster than the kernel optimization community can keep up, and where the efficiency of inference compute directly determines the economics of AI deployment at scale. Standard Kernel sits at the intersection of two compounding trends: the proliferation of AI model architectures that require hardware-specific optimization, and the shortage of the specialized engineering talent traditionally required to provide it. For AI infrastructure teams trying to extract maximum performance from every GPU they have, Standard Kernel is building the automation that makes that possible.
15. UNION.AI
Headquarters: Bellevue, WA | Total Funding: ~$38.1M
The gap between an AI experiment and a production AI system is one of the most consistently underestimated challenges in enterprise AI. Building a model is hard. Getting it into production, maintaining it as it drifts, retraining it as data changes, connecting it to the real-time systems that depend on it, and doing all of this reliably at the scale of thousands of concurrent workflows is harder. Most organizations have either built fragile custom infrastructure to manage this lifecycle or accepted that their AI systems will live in a permanent state of controlled experimentation, never fully deployed.
Union.ai is the AI development infrastructure platform that makes production AI manageable. Built by the team that created Flyte at Lyft — including CEO Ketan Umare — the platform has grown from an ML workflow orchestrator into an end-to-end development infrastructure for orchestration, model training, inference, and observability, all running within the customer’s own cloud environment so that data never transits Union’s infrastructure. The platform completed its $38.1 million Series A in February 2026, led by NEA with participation from Nava Ventures and Mozilla Ventures, following a year in which revenue grew 3x and the customer base expanded 2.6x. Flyte, Union’s open-source orchestrator, has crossed 80 million downloads. The company’s Pandera data validation framework has surpassed 100 million downloads. Over 3,500 companies use AI development infrastructure powered by Union.ai, including Spotify, Carfax, and Hopper. The commercial launch of Union 2.0 introduces pure Python workflow authoring, live debugging, dynamic runtime decision-making, and crash-resilient pipelines. For engineering teams that have learned through experience that the hard part of AI is not building the model but keeping it running, Union.ai is the infrastructure that finally makes that manageable.
This was a brief overview of the rapidly evolving AI infrastructure, DevTools, and MLOps landscape. If there is a company you think belongs on this list, reach out to our editorial team and we will make sure they are included on the next one.
