Industrial AI has a homework problem: it needs to understand the machinery. Hitachi Digital brings the factory knowledge, the software teams, and a very large testing ground.
A former hedge-fund trader built Qineqt around bespoke data and temporary exclusivity. Its brief life exposes the awkward economics of selling an investment edge by subscription.
A fast database is useful. A fast database that lets a small team stop moonlighting as an infrastructure department is a business. Tinybird has built its pitch around the difference.
A database built to handle transactions and analytics together found its opening in the seconds between a question and an answer. Now SingleStore is bringing that same impatience to enterprise AI.
A car, a bottling valve and an irrigation sensor have something in common: their data must arrive where it can change a decision. EMQ sells the software that makes the introduction.
A database company built for connected data now wants to supply the context behind enterprise AI. The interesting part is what its customers have stopped stitching together.
A fintech wanted to sell its merchants a live view of their own transactions. The dashboard looked like a product until traffic arrived; e6data's wager is that the expensive part of analytics is often the engine, not the data.
When three dashboards give three answers to one question, the problem is not a shortage of charts. Sundial wants to make the numbers agree - and teach an AI analyst what to do next.
At Las Vegas’s Sphere, the spectacle is all pixels. Behind it sits a more prosaic talent: moving vast amounts of data without making the audience wait. Hitachi Vantara has built a business around that invisible obligation.
The British data specialist says it cut Home Office integration times from seven months to two. Its business is built around a stubborn problem: getting sensitive information to the people who can use it.
Most investors will never see BetaNXT. Yet its systems touch roughly 50 million of them, process about 37 million transactions a day and sit beneath more than $6 trillion in assets. The company’s wager is simple: Wall Street’s old plumbing can become one connected data network without ripping out every pipe at once.
The Boston data company crossed $100 million in recurring revenue by challenging a lucrative industry habit: moving every last byte into one central warehouse. Its pitch is simpler - bring the query to the data.
DataCore built a business by telling IT teams to keep the expensive storage they already own. Now, after acquisitions and a Kubernetes push, it is trying to make the same anti-lock-in argument from the data center to the edge.
A paid marketing tool died when COVID froze its buyers. What survived was the expensive, brittle connector code underneath - and a repeatable playbook for turning customers' least-loved engineering work into a platform.
The Buffalo-area company does not sell the server or the storage array. It sells the crucial middle - and has spent nearly four decades turning compatibility, predictable throughput and long support cycles into a durable niche.
A 13-person shop in suburban Philadelphia is going after the plumbing nobody sees - the pipelines that move enterprise data - by making it workflow-driven, auditable, and priced like a utility instead of a taxi meter.
Everybody wants to do AI on healthcare data. Almost nobody wants to spend two years cleaning it first. Kythera Labs turned that dirty job into a business.
dbt began as a practical fix for analysts stuck between raw warehouse tables and impatient colleagues. A decade later, its workflow sits inside more than 100,000 data teams - and a completed merger with Fivetran is turning that humble idea into a bid for the data layer beneath enterprise AI.
The San Francisco studio has $176 million in committed capital, a $200 million exit and a contrarian wager: in enterprise AI, the useful plumbing may matter more than the flashy model.
Krishna Visvanathan and Scott Sage have spent two decades funding the plumbing beneath enterprise software. With a fresh $135M APAC fund and about $450M under management, Crane is betting the next category leaders start with infrastructure, not pitch decks.
It runs a small firm out of Santa Monica, writes a handful of checks a year, and once turned a $26.5 million CrowdStrike stake into a billion-dollar return. Here is how March Capital plays venture capital like a concentration bet.
Spun out of one of the world's most secretive trading firms, Jump Capital has spent a decade quietly funding the infrastructure that markets, money, and software run on - and it prefers the unglamorous parts.
What began as proxy infrastructure now sells something AI agents urgently need: live public web data that arrives structured, usable and less likely to break at the first CAPTCHA.
Crustdata is a San Francisco company that supplies real-time company and people data through clean APIs and webhooks built for AI agents. Founded in 2024 and part of Y Combinator's Fall 2024 batch, it pulls live data from many primary sources so AI SDRs, recruiting tools, and investment platforms can find the right person, watch for job changes and funding rounds, and act on fresh signals instead of stale databases.
Kaelio is a San Francisco startup (Y Combinator, 2025) building the open-source context layer that makes AI data agents reliable. Its core project, ktx, auto-builds a governed layer of business knowledge and executable metric definitions from a company's data stack - Markdown wiki pages plus YAML that declares tables, joins, grain, measures and filters. Instead of an agent writing raw SQL that looks right but returns wrong numbers, it asks ktx for a metric and ktx plans the joins and compiles warehouse SQL. Kaelio ships ktx (Apache 2.0), a hosted ktx Cloud with governance and observability, and a managed Data Agent that answers questions in Slack, web and email with source-backed results.
Mundo AI (YC W25) builds high-quality multilingual training data for AI models, tackling the shortage of non-English data that leaves most of the world's languages underserved by today's models. The company partners with native speakers and runs end-to-end operations in the countries where target languages are spoken, using proprietary software for data collection, generation, annotation, and quality assurance. Founded in 2024 by four University of British Columbia alumni, its customers are AI labs and companies training or improving non-English and multimodal models.
Shofo is a San Francisco startup (Y Combinator W26) building what it calls the world's largest indexed video library - a 'Common Crawl for video.' It continuously crawls billions of public short-form clips from platforms like TikTok, Instagram Reels and YouTube Shorts, then runs them through a pipeline that sanitizes, deduplicates, segments and labels footage with object detection, activity recognition and reasoning annotations. Shofo sells custom, ready-to-train datasets to AI labs, delivering a request like '100K hours of cooking videos where someone is holding a pan' in days rather than months.
Terminal is a universal API for telematics data in commercial trucking and fleet management. It connects to hundreds of telematics service providers - GPS, safety events, fault codes, dash-cam media, driver hours - and delivers that data in one normalized feed, so insurers, fleet software, logistics firms and fintechs can build products without wiring up hundreds of one-off integrations. Often called 'the Plaid of trucking,' the Toronto company was founded in 2023, went through Y Combinator's S23 batch, and has raised $26M to date.
Astronomer is a New York-based data infrastructure company and the commercial force behind Apache Airflow, the open-source standard for data orchestration. Its flagship product, Astro, is a fully managed, cloud-native platform that lets data teams build, run, and observe data pipelines at scale, and its Astro Observe layer extends that into data operations and observability. Astronomer employs a large share of Airflow's core committers, serves 700+ enterprise customers, and raised a $93M Series D in May 2025 to power orchestration for enterprise AI workloads.
DualBird is a Boston-based data infrastructure company building a cloud-native engine that brings hardware-grade acceleration to big data and AI workloads. By running rewritable FPGA silicon on AWS as a software service, DualBird plugs into existing Apache Spark and Apache Iceberg pipelines and claims 10-100x faster processing at 50-90% lower cost, without asking teams to change their code or workflows. The company raised $25M in combined Seed and Series A funding led by Lightspeed Venture Partners in November 2025.